tag:blogger.com,1999:blog-16887125491447601042024-03-13T06:51:25.192-07:00Ewan's Blog: Bioinformatician at largeAnonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.comBlogger75125tag:blogger.com,1999:blog-1688712549144760104.post-66433777614863848392016-12-20T14:24:00.000-08:002016-12-20T14:24:51.467-08:00My blog has moved!<div dir="ltr" style="text-align: left;" trbidi="on">
As you may already be aware, I have <a href="https://ewanbirney.wordpress.com/">moved my blog over to WordPress</a> for practical reasons. I've enjoyed Blogger for its simplicity, but the experience on WordPress is just easier.<br />
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This move takes place in December 2016, when I am celebrating the 20th anniversary of EMBL-EBI's Protein Data Bank in Europe with a series of posts on intriguing structures.</div>
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My nine ‘Structures of Christmas’ start with <a href="https://ewanbirney.wordpress.com/2016/12/12/christmas-structure-lysozyme-2016/">lysozyme</a>, a protein found in our tears (and egg whites). It is one of the most researched protein structures, and was the first enzyme structure to be solved via X-ray diffraction.</div>
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My other "Structures of Christmas" are:</div>
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<li><a href="https://ewanbirney.wordpress.com/2016/12/17/the-mighty-mighty-ribosome">A machine that makes machines</a></li>
<li><a href="https://ewanbirney.wordpress.com/2016/12/16/seeing-the-light-opsin/">A protein that lets us see light</a></li>
<li><a href="https://ewanbirney.wordpress.com/2016/12/19/the-twilight-world-between-chemistry-and-life/">An entity that causes a hideous disease in cattle</a></li>
<li><a href="https://ewanbirney.wordpress.com/2016/12/18/rubisco-the-lazy-needy-carbon-fixer/">An inefficient molecule that led to the evolution of plants</a></li>
<li><a href="https://ewanbirney.wordpress.com/2016/12/20/vibrio-cholerae-attack/">A killer of human gut cells that tricks a human protein into helping it</a></li>
<li>A sophisticated tool vertebrates use to defend themselves, which employs a deliberate randomisation strategy</li>
<li>A major player in giving a cell its shape and making cells move</li>
<li>A structure that helps break down lactose to galactose and glucose, and was determined at very high resolution using cryo-Electron Microscopy.</li>
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See you over on <a href="https://ewanbirney.wordpress.com/">WordPress</a>...</div>
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themarytoddhttp://www.blogger.com/profile/01781413043369448707noreply@blogger.com2tag:blogger.com,1999:blog-1688712549144760104.post-1075999538218507602016-10-14T08:22:00.000-07:002016-10-14T08:22:45.165-07:00GA4GH: What? Why? How?<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">In August 2016, I was offered the position of Chair of the
Global Alliance for Genomics and Health (GA4GH), and am delighted to accept. To give a little more background to the announcement going out today, and
to provide some answers to personal questions, I’ve written a bit of Q&A.<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">In this post I answer the three questions I am most
frequently asked about the GA4GH, namely: What on Earth is it? Why am I becoming
Chair? And how on earth do I find the time for these things?</span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
What is the Global Alliance for Genomics and Health?</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h1>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">The GA4GH is producing solutions for sharing genomic and
clinical data responsibly. Low-cost, high-throughput sequencing has changed –
and is still changing – the way we understand living things, from the basic
science of life to human disease. Healthcare is a big focal point for this
change and, now that there is a critical mass of knowledge about the path from personal
sequence to treatment decisions, is able to embrace routine sequencing of
patient genomes and other molecular measurements.</span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">But if we want a future in which all people can benefit from
this change, we need to solve a number of technical, structural, security and
ethical problems. The Global Alliance for Genomics and Health was set up to to
do just that.<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">At present, my overall impression of GA4GH is of a
professional orchestra warming up: intense, but disjointed, activity and
passion. The different sections are poised to work together, producing an
ecosystem of harmonised technical and ethical standards.</span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
How we measure</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Over the next decade, healthcare will begin to change the
way it collects molecular measurements from patients. 'Genomics entering the
clinic' means that it will soon become a matter of routine to gather DNA, RNA,
protein and metabolite data from patients, along with traditional information.
(Note: I like to use ‘Genomics’ in a broad sense, encompassing DNA, RNA, protein
and metabolite measurement, partly because terms like ‘omics' and ‘multiomics’ are clumsy and don't translate well beyond the field).</span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Incorporating new measurements is not new to healthcare: for
example, blood biochemistry has been a mainstay in medical practice for over 50
years, and clinical genetics has been used to successfully diagnose millions of
people in recent decades. Oncologists routinely use the presence or absence of
specific genetic loci in certain tumours to guide treatment. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I think it is generally accepted that these more narrow, field-specific
measurements of genomics will change to become a more comprehensive, routine
collection of many molecular aspects at the same time, applicable to many areas
of healthcare. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Genomes, whole-blood transcriptomes, tumour RNA-seq and
large scale-metabolomics can all provide relevant information that is useful in
assessing individuals, and still more useful when analysed collectively.</span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
A game-changing opportunity</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">This is all a bit hairy in terms of skills transfer and
capacity, but it’s much more <a href="http://genomeinformatician.blogspot.co.uk/2016/09/sharing-clinical-data-everyone-wins.html">exciting in terms of opportunity</a>.<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">The consequence of this is that healthcare, a massive chunk
of the world economy (between 8% and 20% of GDP in developed-world economies) is
going to be conducting high-throughput molecular phenotyping on humans – <a href="http://genomeinformatician.blogspot.co.uk/2015/05/human-as-model-organism.html">a wonderful,outbred mammal</a>. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">From the perspective of research, which has traditionally
looked to other organisms to understand how they work before translating that
knowledge to humans, this is an amazing opportunity. Being able to use human
data directly – particularly by gathering the huge datasets generated in
routine healthcare – will be <a href="http://genomeinformatician.blogspot.co.uk/2016/09/sharing-clinical-data-everyone-wins.html">transformative for science</a>.<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">The sheer ‘firepower’ of healthcare means humans will be the
most studied organism on the planet. No other animal will come close in terms
of scale, detail and longitudinal sampling. What an opportunity for research –
both basic and applied!</span><br />
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Turbulent waters</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Repurposing healthcare data for research, at scale, will not
be smooth sailing. There are real cross currents around data, with different
levels of access and rules of engagement buffeting one another. <o:p></o:p></span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Fundamentally, much of molecular biology research data is
fully open, globally aggregated (e.g. ENA/GenBank, PDB, the Human Genome) or,
in the case of human research subjects, distributed in accordance with different
consents that patients have signed. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Healthcare data is completely different. There is a thicket
of national legislation, each rooted deeply in national law, language and
societal norms, and the primary remit of each system is to keep its citizens
healthy – not to create resources for research. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">As generating molecular data becomes more a matter of routine,
the constraints for access will doubtless change, perhaps without reference to
research and its potential to create better long-term solutions. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Another interesting driver for change is patient engagement.
Increasingly, clinical research has become more of a two-way relationship, with
patients empowered to be owners of their personal measurement data, in addition
to being donors.<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Even assuming that all goes well and access issues are
resolved, there is the matter of handling data on massive scales, and being
equipped to analyse it. Engineering around large-scale genomic data is no
trivial matter. One can’t simply slurp up spreadsheets or STATA frames of
genomes, transcriptomes and metabolomes – you need proper computational muscle.
<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">There is also the opposite 'knowledge flow': for healthcare to
leverage genomics well, there are many practical problems for which research
holds the solutions. We need these solutions and skills to flow from basic
research into healthcare. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">If we want a future in which we can all benefit from our
investment in studying humans on the molecular level, we need to solve these
problems.</span><br />
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The major challenges, in a nutshell</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;">Technical problems</b>
require solutions for working with data on different scales in sensible,
portable ways. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;">Structural problems</b>
can be resolved when we agree on how to represent reference data. That includes,
for example, genomes and variants, but also the way we describe things. The
meta-data must be aligned to allow the transmission of key clinical data, and
to allow data sharing more broadly. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">(The devil is in the detail here. Consider the many ways one
could represent ‘nested variation’ – a single nucleotide polymorphism on a
structural insertion of DNA in the context of an inversion – something we elide
over in both research and clinical practice.)<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;">Ethical and
regulatory problems</b> are perhaps the most discussed across the board, and we
must find a way for <i style="mso-bidi-font-style: normal;">bona fide</i> researchers
to access data within an appropriate framework, globally. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><b style="mso-bidi-font-weight: normal;">Security problems</b>
require tight coordination. The GA4GH aims to establish a federation in which datasets
are appropriately accessible. That means we need access tools like APIs and
virtualisation schemes that can work smoothly, with predominantly secure
electronic methods, and absolute clarity and constant forward thinking about security.<o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">But as with all complex issues, many of the challenges are
some kind of combination of problems, or hide in the spaces in between.</span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
GA4GH: an ambitious endeavour</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Resolving so many challenges in a relatively short time is
certainly ambitious, but it can be achieved. It isn’t easy technically or
socially, because it will only be effective if it is global. But we know from
experience that it isn’t impossible. </span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">The extensive work done already shows that
it is tractable: for example we already share (mainly by data transfer) large
cohorts of patients for joint analysis delivering many new insights. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">We have established appropriate ethical access to these
schemes. We have also demonstrated, in specific areas, that federation can work
(e.g. MatchMaker Exchange for rare disease patient discovery) and that virtualisation
is an effective approach (e.g. PanCancer Analysis). </span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Many academic and commercial groups in the
GA4GH already provide practical solutions, but they are not as well coordinated
as they should be. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">The goal of the GA4GH is to enable a future in which
secondary use of healthcare-generated genomics data is routine and practical,
and we already have a strong start. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">We need to make existing ad-hoc schemes better by coming
together more.</span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
Why am I Chairing the Global Alliance?</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h1>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">GA4GH has been <a href="http://genomicsandhealth.org/about-global-alliance/history">in operation for three years</a>, led first by
David Altschuler (now at Vertex Pharmaceuticals), then by Tom Hudson (now at Abbvie).
David and Tom oversaw the establishment of GA4GH and grew it from 90 to 433
partner organisations. Under their leadership the GA4GH set up a series of
technical, meta-data, ethical, regulatory and security work streams, many of
which have been very successful, if isolated. There have also been a number of exploratory
projects set up, though many seem to be driven by curiosity and personal
interest. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">My goal for the GA4GH over the next three years is to
rebalance delivery and structure, building on the partnership’s existing
strength of exploratory work. Many people in GA4GH, and some outside the
Alliance, are eager to see more alignment, and there is an incredible pool of talent
in engineering, genomics, clinical and ethics, all ready to come together
around this. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">We may not be able to solve every challenge, as many of
these eventually merge with healthcare informatics generally. But I am
confident that we will make substantial progress and achieve a far better world
for both healthcare and research.</span><br />
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
Where do I find time for these things?</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">(No, I do not have a Time-Turner.)</span></h3>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">When people who know me heard that I took on another
leadership role, they rolled their eyes and either berated me for not saying
no, or simply asked how on Earth I will balance this with my other
responsibilities. <o:p></o:p></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I am stretched a bit thin, between my leadership roles at EMBL-EBI,
ELIXIR and Genomics England, my consultancy for Oxford Nanopore and GSK and my
advisory role for other organisations, and other professional responsibilities.
Importantly, I also have a life outside of science: I am a Dad with two
children and a wonderful wife. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">How could I take on being Chair of the GA4GH, with
everything else going on? How could I … not?<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I am an endlessly curious, optimistic person and love bringing people
together to make collaborations work, even if having such diverse commitments
requires time slicing, and results in my being distracted. In fact, for the past
three years I have been quite active in GA4GH, but at a very technical level. This role is more than just guiding one or two working groups. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<h3>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
Team work</span></h3>
<h2>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Working in teams – tight or loose – and in close partnership
is my default strategy, in both work and family life. My wife and I are very
much equitable partners, with demanding careers and full-time jobs. We are both
responsible for making sure the logistics work (and that we have backup plans),
and for setting aside quality time with our children. That said, one of the
drawbacks of being spread thin is that sometimes I will be at home, but
completely distracted by work – something that drives the whole family a bit
nuts. I know I am not alone in struggling with this. Like many people I feel
that I short-change my family, even as they support me completely.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I could not function as Director of EMBL-EBI without Rolf
Apweiler as joint Director, and the high level of trust we share. Although we
are chalk and cheese (focused, organised German and messy, problem-orientated
Brit), our complementarity is a real strength. I also lead EMBL-EBI research in
partnership with Nick Goldman, and as a group leader I’ve partnered previously
with Ian Dunham and now with Tom Fitzgerald to lead my research projects. I see
my roles with Genomics England, Oxford Nanopore and GSK as providing help,
support and constructive criticism, but as a consultant my interactions are
limited. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Chairing the GA4GH will be a partnership role I share with
the Alliance’s strong Executive Director, Peter Goodhand of the Ontario Institute for Cancer Research</span><span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">, who keeps many of the
processes working smoothly. We both plan to recruit an active set of
Vice-Chairs who will provide a high level of strategic oversight. </span><br />
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I know there
is enough talent in the GA4GH to deliver this.</span><br />
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<h3>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
Enabling talented people to deliver</span></h3>
<h2>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">High-level leadership is mainly about providing the right space
and conditions for knowledgeable, talented people to step up and deliver. Being
clear about the vision and direction is incredibly important, but setting out a
vision often isn’t the most challenging aspect. The hard thing is to identify
the people who have the right mindset and skills, and enable them to drive part
of the work all the way through to delivery. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I am speaking from experience when I say that this is true for
leadership generally, both in formal organisations like EMBL-EBI and more loosely
coupled organisations such as GA4GH. The problems we are grappling with cannot
be resolved single-handed; rather, we will be able to deliver practical solutions
by aligning individuals and groups, and ensuring they have the right balance of
skills, enthusiasm, resources and motivation.</span><br />
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<h3>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
No human is an island.</span></h3>
<h2>
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">The adventure of understanding things is deeply exciting for
me, whether it’s a well-known problem or an unexplored area of science, so the
GA4GH is a project after my own heart. As with any ambitious endeavour, there
are bound to be arguments and hard decisions of all shapes and sizes in the
GA4GH. But the motivation of people to participate, the rewards of
collaboration and the potential benefits to society are great.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">I am very lucky to be surrounded by supportive, excellent
colleagues on every level: the people who manage me, my peers around the world
and those I manage. I am also lucky to be working in science, which thrives on
collaboration, information exchange and support, and where just being
reciprocally nice is an excellent strategy. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></div>
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<div class="MsoNormal">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Working together, we are going to make the next few years of
GA4GH amazing. I cannot wait.</span><o:p></o:p></div>
</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com6tag:blogger.com,1999:blog-1688712549144760104.post-45621063603221327512016-09-06T03:41:00.002-07:002016-09-06T05:42:53.280-07:00Sharing clinical data: everyone wins<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
Patients who contribute their data to research are primarily
motivated by a desire to help others with the same plight, through the
development of better treatments or even a cure. Out of respect for these
individuals, and to uphold the fundamental tenets of the scientific process, I’d
like the clinical trials community to shift its default position on data
sharing and reuse to align to data availability on publication, similar to the life science community. This
will enable more robust, rigorous research, create new opportunities for discovery
and build trust between patients and scientists.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
This aspiration is widely shared in the basic research
community, and has been well articulated in considered and public discussions such as a series
led by the <a href="http://www.nap.edu/catalog/10613/sharing-publication-related-data-and-materials-responsibilities-of-authorship-in">National Academy of Sciences in 2003</a>. Nevertheless,
recent articles in the <i style="mso-bidi-font-style: normal;">New England
Journal of Medicine</i> have pushed against data sharing, calling those who reuse data
“<a href="http://www.nejm.org/doi/full/10.1056/NEJMe1516564">research parasites</a>” (followed by a bit of <a href="http://www.nejm.org/doi/full/10.1056/NEJMe1601087">clarification</a>)
and concern about how best to structure <a href="http://www.nejm.org/doi/full/10.1056/NEJMp1607282">clinical trial data sharing</a> with a lengthy and complex embargo procedure, potentially including payment.<o:p></o:p><br />
<br /></div>
<h1>
</h1>
<h3>
A tradition of sharing</h3>
<h1>
<o:p></o:p></h1>
<div class="MsoNormal">
<br />
Sharing tools has been the norm (mostly) for genetics and molecular
biology since the early days of genetics, mainly because you couldn’t really
get anything done unless people let you use their reagents. This has persisted for
over a century, from the first studies of fly lines to cDNA clones, enzymes, antibodies
and, now, ‘omics datasets. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
The Protein Data Bank in the 1970s, the EMBL Bank (now ENA)
and GenBank nucleotide collections in the 1980s and the Human Genome Project in
the 1990s all thrived thanks to the norms of reagent sharing and data
deposition, and the returns to science were - and are still - huge. Such practices are
pragmatic in terms of both data quality and author credit, each of which provides
incentives for researchers. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
I am perhaps painting a beautiful picture of an imperfect
world – there is still much to be done to ensure all this data sharing can <i style="mso-bidi-font-style: normal;">work</i>. Compliance, agreeing on things
like adaptable standards, and keeping the infrastructure humming are all
challenges we grapple with on a daily basis in molecular biology. But we have
much to be proud of, and embracing the ethos of sharing has brought us a long
way in a short time.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
Data release: why?</h3>
<h1>
<o:p></o:p></h1>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Releasing data when you publish a paper isn’t about giving things
up – although I can see that for some, the lack of instant reward might make one
feel that way. Data release is not about rewarding a single PI; it’s about
benefitting the clinical research community as a whole, and making the most of
the data entrusted to you by patients. So - why release data?<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h4>
We are custodians, not owners, of patient data.</h4>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br />
Patients participate in trials to further medical research, benefit
from new medicines (potentially) and gain from focused care and advice. But numerous
surveys have shown that participants are <i style="mso-bidi-font-style: normal;">primarily</i>
motivated to share their data – the most valuable aspect of a clinical trial –
by the altruistic desire help others in the future.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
So it is very strange that some researchers feel justified in assuming the data produced in a clinical trial is somehow their own scientific property. From the
perspective of patient care, this position is particularly questionable when it
impedes the ability of other scientists to re-examine the data for additional
studies, which would contribute to the progress so eagerly desired by the
participants.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
If we’re not doing all this research to improve patient
care, then probably we should change the consent process.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h4>
Challenging the interpretation of observations is fundamental to the scientific
process.</h4>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br />
Evidence is a wonderful thing. Our freedom to base our
arguments on reproducible experiments dates back to the 17th Century, when people
in Europe were finally permitted to openly discuss and debate science based on
direct observation. Evidence is the backbone of scientific discourse, so it
follows that papers without data can be easily dismissed as well-articulated
speculation.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
When a dataset is published, readers are then able to drill down
to raw observations, and can verify methods or explore alternative explanations.
Yes, this means they can potentially expose errors in your work <i style="mso-bidi-font-style: normal;">and</i> your thinking. But it’s far more
common for readers to double-check the work against other published datasets,
which can answer lots of different questions. Ultimately, this is a good thing
for science.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h4>
Sharing data sharpens the mind.</h4>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br />
The very real anxieties that come with data sharing are both
individual and collective, because we are building knowledge together. Professional
pride dictates that if your data will be open for inspection, you will be much
more careful about the details. (After all that data cleaning and fixing, confounder/covariate discovery and adjustment, you do <i style="mso-bidi-font-style: normal;">not</i> want to be the one who left a howler for others to discover.)<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Everyone knows there are skeletons in the data closet,
mostly down to the complications of running real-life experiments, so current
analyses make use of several approaches to boost confidence in the results. But
generally speaking, just knowing your peers could be wandering through your data
sharpens your mind and makes you focus on handling and presenting your analysis
properly. <o:p></o:p></div>
<div class="MsoNormal">
When an entire community does this, it benefits from a
deeper consensus on what a “good study” looks like. That matters a lot.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h4>
Meta-analysis and Serendipity</h4>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br />
When it can be done, meta-analysis (the combining of
datasets) is a win–win–win (funders, scientists, patients). It’s about building
on studies, combining them to gain new insights, asking different questions and
finding new leads. Meta-analysis isn’t always possible – clinical trials often
look at entirely different things, and even when they do study the same thing, they
can’t always be aligned very well. But meta-analysis is <i style="mso-bidi-font-style: normal;">only</i> possible when people share their datasets.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Serendipity is another benefit of data sharing – I am always
amazed at how important it is for science. Serendipity has guided us to some
seriously profound insights, for example the relationship between the Malaria
parasite and plants, or how metabolic enzymes can be used as lens crystals. It’s
been behind many of the completely weird discoveries that make biology so
wonderful, and many practical discoveries, such as CRISPR, that push the
frontiers of possibility.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
I’ve stumbled happily upon Serendipity many times, and very
often others have made serendipitous discoveries based on data or methods I
have published. You’d have to be pretty cynical to begrudge your fellow
scientists such pleasure, and, frankly, a bit petty to fret over whether
they’ll remember to credit you (nearly all scientists carefully reference their
sources, if only to reassure reviewers of the credibility of the data they use).<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
For funders, both meta-analysis and serendipitous
discoveries compound their return on investment and make them look good. For scientists, being able to make use
of comparable data to verify or cross-validate their work, or to make unplanned
discoveries, is invaluable. For patients, knowing their contribution is being
used in lots of different and useful ways can give a sense of pride.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Sceptical about whether this really applies to clinical
research? Well, without having access to a large number of trials, I doubt
anyone could say. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Having more large datasets on hand for meta-analysis can
only benefit those planning and analysing the results of clinical trials. And as
clinical trials begin to incorporate more high-dimensional, data rich datasets
(e.g. imaging, metabolomics, multi-omics) – and to share them – there will be plenty
of opportunities to carry out sophisticated meta-analysis. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
As for Serendipity, well, it can strike at any time.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
The scoop</h3>
<div>
<br /></div>
<h1>
<o:p></o:p></h1>
<div class="MsoNormal">
It is hardly possible for anyone to “scoop” you simply
because you released your data on publication – particularly if that dataset
represents only what is needed to support your paper. If someone else looks at
that data and comes up with an interesting observation you missed, they can potentially
make that corner of science a little bit better. Dwelling on the negatives will
get you nowhere, but looking on the bright side may land you a new
collaborator.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
If the only datasets you share at publication time are those
that relate specifically to that paper, there is no need for complicated
embargo rules that provide authors enough time to perform a full analysis on
all the data collected (as proposed in the most <a href="http://www.nejm.org/doi/full/10.1056/NEJMp1607282">recent NEJM editorial</a>). Tracking and versioning might become more complicated
with later papers, but this approach does the important job of tying the
datasets to the publication in a reasonable timeframe, opening up that piece of
science for proper verification and discourse.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
If you really believe you are going to be scooped for some
missing analysis on a dataset, the solution is to delay publication. If you’re
worried that making your data public will expose you to undue criticism, make
your analysis bulletproof. That will be good for you and for the system as a
whole, as understanding the strengths and weaknesses of different analyses only
makes the community stronger.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<h3>
When data sharing is not straightforward</h3>
<div>
<br /></div>
<h4>
Human subjects</h4>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br />
No matter what, we have to honour patient consent. As
scientists we may wish such agreements were more future-proof, but when those consents
preclude data sharing beyond the study group, we have to accept it and move on.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Exactly how to future-proof consents for clinical trials is
no simple matter. One solution would be for funding agencies or regulators to begin
insisting that consent forms provide a reasonable level of research access,
which would facilitate research but respect the privacy of individuals. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Currently, for genetic studies, there is a lightweight
vetting process, involving both individual and institutional sign off, which
assures patients that the researchers will perform appropriate research on the
dataset. This is a clunky approach and it certainly needs improvement, but it
is functional. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h4>
Standards and infrastructure</h4>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br />
Data sharing is only feasible if the parties involved are <i style="mso-bidi-font-style: normal;">able</i> <i style="mso-bidi-font-style: normal;">to
do it</i>, without worrying that they’ll run into trouble transferring files from
one site to another, or that their data will disappear into some kind of black
hole. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
A robust, global archive for this kind of information would
be one important piece of a larger infrastructure that would make biomedical
data sharing straightforward. The EMBL-EBI model – biomolecular archives
supported by international collaborations – is a solid example. Funding for infrastructure
like this is huge value for money, and costs little in the context of global clinical
research funding.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
CDISC standards are functional, and well used by the
clinical trials community. But there is a constant need to review standards and
establish new ones for emerging technologies. This work never ends, but the end
goal of harmonisation (i.e. to support meta-analysis) is a good one, and the
whole process helps us along on our eternal quest for a shared language. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h4>
Regulatory and commercial concerns</h4>
<h2>
<o:p></o:p></h2>
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I do not have a lot of experience in this area, but it’s
clear that regulation of clinical trials is a huge deal for the pharmaceutical
industry. Any data release policy
needs to work well for the regulators, and for commercial interests, who can have different concerns from academia. For both, the science performed in
clinical trials must be very sound, so that mind-sharpening step of data
release is certainly of value, but most companies that I know are delighted when other science happens from the data they release. <o:p></o:p></div>
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<h3>
Evidence is beautiful</h3>
<h1>
<o:p></o:p></h1>
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<br />
In this on-going debate about data, let us base our
arguments on… data. We are all likely to change our views view when presented
with compelling data and well-reasoned analysis, which is one of the nice
things about being a scientist. <o:p></o:p></div>
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Refreshingly, for the most part I do not think this debate is
one of those boring political ones where everyone chooses a side, closes their
ears and steels themselves for uncomfortable dinner-table discussions. Scientists
already working in an open-data environment understandably campaign for
everyone to join them – though they are full aware of the downsides. Scientists
working in clinical trials can see there are advantages to sharing data, but
have neither the time nor the inclination to sort out the myriad details that would
make it workable.<o:p></o:p></div>
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<br /></div>
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As a starting point, we can focus on the simplest, tried-and-tested approach of publishing your data alongside your narrative – a practice that has served science well for over 300 years. But more importantly, we can keep the discussion going, and work with one another to overcome the barriers to realising the full potential of biomedical research. That would be a win for scientists, their funders and, most importantly, patients themselves.</div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com3tag:blogger.com,1999:blog-1688712549144760104.post-21729476430448007492016-06-14T23:57:00.000-07:002016-06-15T01:08:12.158-07:00The EU Referendum in the UK: A very personal view<div dir="ltr" style="text-align: left;" trbidi="on">
<br />
<br />
I have tweeted prolifically about the UK Referendum on membership in the European Union, strongly supporting the REMAIN (staying in the EU) campaign. In response to requests for a more substantial explanation of my position, I present here a short version and a long version of my views.<br />
<br />
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> Short view</span></h2>
<br />
I am voting to REMAIN IN the EU for four main reasons:<br />
<br />
<ol style="text-align: left;">
<li>It will be <b>at best bad</b> and <b>at worst catastrophic</b> for the UK economy to vote "leave".</li>
<li><b>The trade deal we have as part of the EU is sensible</b>. Bizarrely, the current trade deal (somewhat sweetened by David Cameron's efforts) is really having our cake and eating it, too. The EU deal we have is nothing close to resembling the 'EU super state' people worried about in the 1990s. The Eurozone is the closest thing to such a state - but we are clearly not part of the Euro. For those who want to be part of the common market only, the trade deal brokered by Cameron is what we have on the table.</li>
<li>Immigration is big, complex issue that modern societies, including the UK, have tackled rather clumsily and not managed well. However, <b>leaving the EU will not improve our ability to handle immigration</b>. If anything, it will worsen it.</li>
<li><b>We are fundamentally linked to Europe</b>: our borders, our electricity, our science and arts, our pollution, our friends and family who have settled in other countries. We are not going to physically change the location of our islands, and as this no longer the 19th Century, we are no longer in the business of building an Empire. Our future is here, in this place. We've got to be a strong part of Europe's future, keep our place at the table and argue for what we think is right. Spinning our wheels trying to recreate the glory of 19th Century Britain, in isolation from our nearest neighbours, is mad and regressive. </li>
</ol>
<div>
<br /></div>
<br />
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> Longer view</span></h2>
<br />
Before I unpack my four major reasons for remaining in the EU, some "meta" points to consider:<br />
<br />
<br />
<h4 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><b> 1. As an individual, I am clearly pro-Europe.</b> </span></h4>
<div>
<br /></div>
<div>
I am a Director of the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI). This is a scientific International Treaty Organisation that, while not being directly linked to the EU (for example, Israel is a member country of EMBL), keeps me very closely involved with issues affecting science in the EU and Europe (e.g, discussing shared challenges with Italian, French, German and other researchers and policymakers involved in genomics and big data analysis). This position has given me unique insights into issues that affect the scientific community throughout Europe, and over the past decade I have come to understand and deeply appreciate the value of blending different approaches of other (non-UK) European countries with our own. </div>
<div>
<br /></div>
<div>
The criticism that I am biased may be true, if it is based on my genuine warmth for all the different, complex facets of Europe, without thinking they are in any way perfect. Everyone brings their own biases to the discussion, whether they are due to position, experience or job. Understanding our own biases and setting them aside to explore the issue from different viewpoints is valuable, and will make this discussion about the EU richer.<br />
<br />
<br />
<h4 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><b> 2. I do not think the EU is perfect.</b> </span></h4>
</div>
<div>
<br /></div>
<div>
I am passionate about the UK staying in the EU, but that's not to say I believe the EU it is perfect. There are small niggles and annoyances where I think it could do better, and some gaping issues that it needs to confront. That said, I cannot think of any national government (including ours) that could be held up as an example of perfection for the EU to emulate. (For those who put forward the UK Parliamentary system as a perfect system, consider the House of Lords... Would you even think about creating such a body today?)<br />
<br />
I get to see quite a bit of national governments and transnational schemes close up, through the lens of science (which is inherently international). Seen in this way, the EU is neither the worst nor the best bureaucracy. It is simply unique, with uneasy tensions between Brussels and various coalitions of nation states, and amongst the nation states themselves. This is no more odd than the familiar tensions between England, Scotland, Wales and Northern Ireland in the UK, or those between the German Länder or Chinese provinces. It is the dynamic of the EU that is unique, both because of its geographical spread and the power distribution of its Member States. <br />
<br />
Despite what the UK press might have you believe, the EU centre is far less powerful than those of equivalent national systems, for example in the UK or Germany. The European Council, where all the democratically elected Heads of State have either an explicit or an implicit veto, has a massive influence in Europe, and each European nation state gets its vote.<br />
<br />
The EU's lack of perfection does not justify leaving it; if anything, its imperfections are to be tackled directly and with representation. There is every reason to continue to strive to reform the EU.<br />
<br />
<br />
<h4 style="text-align: left;">
<b><span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> 3. I do not think it's crazy to ask the question. </span></b></h4>
</div>
<div>
<br /></div>
<div>
I'm not sure that a referendum is the best way to get a considered answer, but I don't think it's crazy for citizens to question governmental structures, or to question whether the EU's structure is "good for Britain" from a purely self-centred British perspective. It is in fact very good for people to be engaged enough to ask what this transnational body is really trying to achieve, in a general sense (more on this below). One should never have a system that cannot be sensibly challenged. <br />
<br />
I have some good friends who are committed to voting "out", sometimes in the interests of sovereignty and libertarian values, and sometimes (often my parents' generation) out of concern for cultural changes brought about by immigration. I feel strongly that we all owe it to each other to hash it out with good, considered arguments, based as much as possible on evidence. What we will all be proud of in end is how we treat each other with respect and listen to one another, even when we disagree. There is no place for derision or contempt here - the stakes are simply too high.<br />
<br />
<br />
<h4 style="text-align: left;">
<b><span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> 4. This is a personal view. </span></b></h4>
</div>
<div>
<br /></div>
<div>
This is not the EMBL view. I am not representing anybody else but myself in this post.<br />
<br />
<br />
<br />
<h3 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> Unpacking my "Remain" suitcase</span></h3>
<br />
I've heard plenty of reasons cited for voting "out" - here are my "vote in" arguments, in much more detail.<br />
<br />
<br />
<h3 style="text-align: left;">
<span style="color: #134f5c; font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> 1. It will be at best bad and at worst catastrophic for the UK economy if our citizens vote "out". </span></h3>
<br />
Leave campaigners take umbrage at this, positing, "We are a great trading nation, and economically we will be the same or better out of the EU". Not so. We are a great trading nation, and that's precisely thanks to making pragmatic decisions when presented with important ones, like this referendum. <br />
<br />
<br />
I am not an economist, but then neither are most people flinging about fantasies about some sort of panglossian world of trade for the UK outside of the EU. <br />
<br />
<br />
<b> Straightforward facts:</b></div>
<div>
<br />
<br />
<ul style="text-align: left;">
<li>More than 40% of our trade is with Europe. Perhaps that proportion might be a bit lower in the future, but it's never going to be below 30%, if only for geographic convenience and the sheer sizes of Germany and France. </li>
</ul>
<br />
<br />
There is simply no way that the EU is going to offer us a better deal once we've flounced off in a huff. Cross-border service agreements and trade without freedom of movement? I don't think so. Why? First, all the other EU agreements (e.g. EEA, Swiss) require free movement in exchange for free trade. Second, excluding London's financial services will only be a big win for Paris and Frankfurt. Expect massive lobbying, pushing hard on an open door to prevent any trade deal including financial services. <br />
<br />
<br />
Take an example of a German company wanting to be competitive in the UK: BMW may sell so many cars in the UK, but it will be in the EU's interest to negotiate BMW to sell many, many more cars to the US and China (that's what really keeps the BMW people in Munich up at night). Also, BMW doesn't care as much about having tariffs for their expensive cars entering the UK as they do about the market distribution of those tarrifs, and whether they make BMW less competitive as a brand in the UK compared to other car makers. They may lose a little market share if Jaguar continues to have a UK manufacturing base, but it's not really enough to swing a trade deal one way or the other.<br />
<br />
<br />
Making new trade deals with China, India and the USA on our terms? The bigger economy always sets the terms, always has done (I refer you to the Swiss/China deal) and always will. The UK might be the 5th (nominal) or 9th (PPP) biggest economy in the world, but the first non-NAFTA, non-EU economy smaller than us (on PPP terms) is South Korea, then Saudi Arabia, Turkey and Iran. Even with those countries, it is very hard to imagine that the EU trade block negotiators will allow a better deal to be struck with the UK than with the EU. Let us please, please not snooker ourselves in a negotiation before we've even started. Embarrassing.<br />
<br />
<br />
<ul style="text-align: left;">
<li>In the <b>best case</b>, negotiations will take around 5 years. It is more likely they will take closer to 10 years. During all that time, our first priority will be to sort our relationship with the EU (where we have ~40% of our trade now). </li>
</ul>
<br />
<br />
In the best possible scenario, our new, crack team of trade negotiators will be moving on from their discussion with the EU within 5 years, talking next to South Korea and Turkey, both of which may well be happily bargaining to compete with the UK's freshly negotiated position with the EU, for another 5 years. Perhaps we'd get a good deal eventually, perhaps not. <br />
<br />
<br />
Our points of leverage - international finance, pharmaceuticals - have a strong track record of moving their centre of gravity, and multinationals can reasonably be expected to position themselves to minimise the risk of screwing this up and, at the very least, de-emphasing Britain. Far worse, they might be expected to withdraw completely during this time. This would mean even less leverage for Britain, not more.<br />
<br />
<br />
<ul style="text-align: left;">
<li>In the <b>best case</b>, after 10 years any loss in our economy might be mitigated by freer terms of trade with Turkey, South Korea, Saudi Arabia and Iran (n.b. we will not get a better deal than the EU with NAFTA or China), assuming the EU's negotiators with those countries tank and really screw up their competitive position. </li>
</ul>
<br />
<br />
Really? South Korea, Turkey, Saudi Arabia and Iran? Does this sound like a better alternative to free trade with Germany?<br />
<br />
<br />
<ul style="text-align: left;">
<li>In the <b>worst case</b>, after 10 years we would be kicked around in trade deals and forced to accept the terms on offer from countries/trading blocks that, understandably, put their own interests first. </li>
</ul>
<br />
<br />
None of these countries or trading blocks would shed a single tear over the great trading nation that the UK used to be. They'll get the best deal for themselves, and move on. That's what trade negotiators do. Of course this sounds all rather grubby, realpolitik and unfair, but the reality is that, as always, we must choose our battles carefully. Very carefully. <br />
<br />
<br />
<b> Why on earth would we voluntarily suffer certain short-term pain, and risk catastrophic long term pain?</b> What is the motivation behind this self-destructive impulse?<br />
<br />
<br />
<h3 style="text-align: left;">
<span style="color: #134f5c; font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> 2. With the current offer, we have our cake and eat it, too.</span></h3>
<br />
There are some who argue, "<i>We don't want to be part of an EU super-state</i>". This is usually combined with statements asserting, "<i>The EU is undemocratic</i>," and "<i>We should reclaim our sovereignty</i>". The dismaying thing about these arguments is that they are part of an old fight, and one that has already been won. Britain is not in the Eurozone, where much of the further integration is happening. The UK is clearly outside - in effect, it leads (or could lead if it wasn't so self-obsessed) the non-euro component of the EU.<br />
<br />
<h4 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> Super-state?</span></h4>
During the 1980s and the 1990s there was an EU roadmap, which had it evolving into an explicit federal structure with a common currency and a political union across Europe. European Heads of State and government leaders decided to attempt monetary union first, before political union. However, a series of events radically changed the course of this strategy, affecting the whole of the EU but the UK in particular. There were three major changes: the treaty of Maastrict in 1992, the French and Dutch referenda on the European constitution in 2005, and the recent agreement brokered by David Cameron for the UK.<br />
<br />
<br />
The Maastrict treaty of 1992 involved the UK and Denmark's opt-out of the euro. All of the other EU Member States agreed to either adopt the currency together, or to "eventually" adopt it. The start of the euro was an exciting turning point, as it provided a "fair weather" monetary union and agreements about fiscal policies underpinning it. <br />
<br />
<br />
It was partly bloody-mindedness and partly thorough economic analysis that got the UK an opt-out of the euro. The UK Treasury argued that a successful currency union required fiscal (i.e. 'what state spends') union, and fiscal union requires political union to work properly. For the UK, it was all (monetary, fiscal and political), or nothing. This position was defended during the Labour years by Gordon Brown at the Treasury against the political direction of Tony Blair, an episode often forgotten about Brown's legacy.<br />
<br />
<h4 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> European constitution? Eurozone?</span></h4>
<br />
That this monetary union was intended to pave the way for a political union was enshrined in a "European Constitution", which was put to a vote in 2005. But a resounding "Non" from France and "Nee" from the Netherlands stopped the constitution in its tracks. The Treaty of Lisbon 'tidy-up' treaty left the Eurozone with a monetary union but no strong fiscal system. In this state the Eurozone entered the 2008 crash (to be fair, not caused by Europe), when the "fair weather" rules were tested almost to destruction. This unhappy state of affairs is still unresolved today. <br />
<br />
<br />
Respected economists and political analysts agree that the Eurozone must either break up or become nation-like, with enforceable, federal-scale rules and a quid-pro-quo arrangement whereby wealthier countries fund substantial public works and establish safety nets in poorer countries in exchange for stability. Breakup would involve a reversion to a zone of German influence, wherein countries that trade most with Germany will voluntarily link to its currency (with all the headaches that implies). Frankly, both options look impossible at the moment, but maintaining status quo is equally impossible. Surely, something must give. Whatever happens, the UK remains outside of it all.<br />
<br />
<br />
Possibly the only major concession secured by David Cameron was an agreement to change the European Union from operating as a single currency block (i.e. euro) with some unmerged secondary currencies (e.g. pound, kroner) to explicitly acknowledging that the EU will be a multi-currency union. This is a profound win. It reverses the part of the Maastrict treaty in which all states except the UK and Denmark were going join the Eurozone. It officially separates the boundaries of the Eurozone from the boundaries of the EU, and limits the Eurozone in regards to regulation of the currency/trade processes. I am sure there is a whole host of technical details about this that I don't understand, but for sure this is an important change.<br />
<br />
<br />
So - given all of this - the EU can no longer be considered a 'European super-state'. The Eurozone might emerge as a 'super-state of the future', but the agreement is clear that the UK will not be part of that. The current deal we are being offered is a rather too-good-to-be-true offer. <br />
<br />
<br />
Many northern Europeans think the UK is already being offered a very sweet deal. Eurosceptics of the 1990s should appreciate this - that there is a free trade area made up of nation states, and it is called the EU. Freedom of movement still a concern? Read on.<br />
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<br />
<h4 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> Non-democratic?</span></h4>
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<br /></div>
As for the democratic credentials of the EU, it is not for want of trying, as demonstrated by the <a href="http://www.europarl.europa.eu/atyourservice/en">European Parliament</a>. National politics (in the UK and in many other places) treats the European Parliament as an irrelevance, a scapegoat and object of derision. But honestly, its structure is no more odd than, say, having the second chamber of a national parliament composed of life-appointed peers and hereditary peers who vote internally on who gets to vote. For the UK to be indifferent to the democratic systems of the EU and then complain about its lack of democracy is a bit weird. <br />
<br />
<br />
The <a href="http://www.consilium.europa.eu/en/european-council/">European Council</a> (confusingly, completely different from the <a href="http://www.coe.int/en/web/about-us/who-we-are">Council of Europe</a>) is where the Heads of State/Government meet, and has equal power to the European Parliament. These democratically elected individuals feel quite strongly about their mandate. There is no pretending that this is somehow a clear governance situation; it has developed organically and, with all its different layers, attempts to balance the different sizes, political styles and cultures of the EU Member States. As a Brit well accustomed to complex constitutions and evolution rather than revolution in government, I find this organic approach somewhat comforting. It's certainly more baroque than many (most?) systems, but it's hardly unique in Europe, where the accidents of history have a way of creating political structure.<br />
<br />
<br />
It is not the time to reform these structures, and it won't be until the final end point of the Eurozone is clarified. In the event a federal fiscal system emerges, better structures must be hammered out, in which the EU's relationship to non-Eurozone countries is spelled out. A big bun fight is inevitable, but the first bun will not soar until the question of whether the Eurozone is going to be a federal state is resolved. In the event no such state emerges, the dismantling of the Euro will give rise to new structures during a time of great disruption.<br />
<br />
<h3 style="text-align: left;">
<br /><span style="color: #134f5c; font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> 3. Leaving the EU will not improve our ability to handle immigration.</span></h3>
<br />
A small number of older people I meet sometimes rage against modern Britain, incenses about immigration. "<i>We must control our borders and immigration better</i>," goes the chorus. "<i>We are losing our country</i>," goes the verse. This is hard to talk about sensibly, because what triggers this gut reaction is so amorphous.<br />
<br />
<br />
My main answer is that leaving the EU will not fundamentally change these issues for the UK, and that poorer countries outside of the EU need to resolve some of the serious issues that are putting pressure on their people to leave. Over half of the immigration into the UK is not from the EU, so in terms of numbers, half the problem is completely unrelated to our membership. <br />
<br />
<br />
It is easy to miss that 5% of NHS staff (10% of NHS doctors) are non-UK EU citizens, paying taxes and keeping our health system afloat. You might not notice the millions of young EU citizens - from waiters to engineers - working, paying UK taxes and, mainly thanks to their youth, making very few demands in return (one estimate puts increased demand on the NHS from non-UK EU citizens at 1%).<br />
<br />
<br />
A <i>laissez-faire</i> approach to cultural integration is unlikely to work, as described by commentators such as Trevor Philips. But EU migrants integrate well, so that's not really the challenge here. British culture is no more or less quirky than any other northern European culture - just open a can of Swedish surströmming (<a href="http://www.thelocal.se/20140210/swedish-expert-called-to-disarm-norway-fermented-herring-stink-bomb">fermented herring</a>) if you don't believe me. In my experience, when you push people on the cultural integration of Europeans in different European countries, they are not just tolerant, they actively like their Italian / French / Swedish / Maltese neighbours, delight in explaining English peculiarities to them. and enjoy learning a bit about other cultural quirks. Polish builders/plumbers, a favourite immigration example of yore, get quite good press these days, having demonstrated a couple of decades' worth of hard work and pragmatism. So cultural integration is mainly a non-issue when it comes to other European cultures.<br />
<br />
<br />
Lower-skilled people in the UK often voice feelings that they have had opportunities taken away from them from low-skilled (or more skilled but happy to work cheaply) people from elsewhere in the EU. Certainly over the past 50 years there has been a levelling out of developed and developing nations, with shifting patterns of employment where large-scale manufacturing happens and job opportunities open up. This is not my field of expertise, but it would seem sensible in this shifting environment to agree on minimum conditions and wages across as broad an area as possible to minimise any potential negative impacts of this kind of movement. <br />
<br />
<br />
It would not, however, seem sensible to assume that leaving the EU would result in a sudden resurgence of semi-skilled jobs in the UK.<br />
<br />
<br />
<h3 style="text-align: left;">
<span style="color: #134f5c; font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> 4. Our future is with Europe.</span></h3>
<br />
Like it or lump it, we are just 21 miles from France, connected by a tunnel. We continue to have massive, regular trade in goods, people and ideas with the Netherlands, France, northern Germany and many other parts of the EU. <br />
<br />
<br />
<b> Our history is intimately linked</b> with those of France, Holland and Germany, from the Plantangents through Protestantism to the English coronation of Hanoverian kings. <br />
<br />
<br />
<b> Ours are important islands</b> in Europe, but by no means the only ones, joining Ireland, Malta, Sardinia, Corsica, the Majorcas, the Azores, Copenhagen and Gotland who could point to clear blue water separating "them" from the "rest of Europe". <br />
<br />
<br />
In terms of everything that matters - culture, trade, electricity, pollution, holidays, food, football, rugby, tennis, language - we are European, definitely part of the broader sweep of northern nations with strong Anglo, Celtic, Viking and Protestant traditions. <br />
<br />
<br />
Like every other major European nation, <b>we can be proud of our contributions to the world </b>and, like every other European nation, be proud to learn from our mistakes. We can perhaps indulge in being slightly more proud than others about certain things (Most Nobel Prizes in Europe? Longest-running Parliament? Rule of Law?). But we could never pretend that French poets, philosophers and mathematicians (leaving aside Napoleon) haven't had an impact, or that the French revolution did not leave its mark on the world. Europe suffers no shortage of impressive achievements. <b>So why on earth would putting two fingers up to the EU suddenly make our achievements seem more important?</b><br />
<br />
<br />
We will inevitably need to agree on many things with our fellow European nations. We do not generate electricity consistently enough to unlink our electrical grid from Europe. We do not have enough gas from the North Sea to avoid trading for it. We do not produce enough food to sustain ourselves and eschew the produce of other countries. Viruses, bacteria, pollution will never submit themselves to border controls. Our parents might retire (or have retired already) to sunnier shores outside the UK, in other parts of the EU, and we will doubtless wish to (or need to) visit them easily. Our children will seek to enrich their lives by living in another country for a time, whether it's for a summer or a couple of years. <br />
<br />
<br />
<b> We must have a sensible level of agreement with the largest grouping of developed nations in the world, and the largest pool of educated, qualified people on our doorstep. </b><br />
<br />
For better or for worse, the EU, with all its faults, is the place to make these agreements. We will not dodge trips to Brussels, or arguments with our peers in Paris or Berlin, by leaving the EU. We have already changed the EU, partly through sheer bloody-mindedness, and partly by thoroughly analysing what is working and what could work better. Us being out of the Euro and showing that there is a productive non-Euro, inside EU life is key. If we stay engaged and keep our seat at the table, there is much more we can change for the benefit of all. What is on offer - to be part of the EU and not part of the Eurozone - is a remarkable opportunity. We'd be fools not to take it. <br />
<br />
<br />
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"> VOTE REMAIN.</span></h2>
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<br />
<br />
<br /></div>
</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com4tag:blogger.com,1999:blog-1688712549144760104.post-54363804395680511312016-05-17T09:55:00.001-07:002016-06-06T10:44:20.191-07:00Publishing Big Data Science<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
This is the third and final post in a series in which I
share some lessons learned about how to plan, manage, analyse and deliver a
‘big biodata’ project successfully.<o:p></o:p></div>
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<br /></div>
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Now that you have the results of your carefully planned,
meticulously managed and diligently analysed experiment, it’s time to decide on
what to publish, and where.<o:p></o:p></div>
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<br /></div>
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<br /></div>
<h3>
1. Present your work</h3>
<h2>
<o:p></o:p></h2>
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<br /></div>
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I love presenting, because having to explain my work to a
mixed audience helps me understand and articulate the science better, and to convey
the excitement of discovery. What is the work for, it not the joy of
exploration? Creating figures to use in a presentation is enjoyable, and helps
me get my thoughts in order. <o:p></o:p></div>
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<br /></div>
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I find writing paper less enjoyable than presentations, but
the same core is present in both – good figures which provide a strong narrative from design through to analysis. There is however a particular
rigour in writing a paper that brings out the best in a piece of scientific
work. Present, and publish – it’s important to us all.<o:p></o:p><br />
<br /></div>
<h2>
</h2>
<h3>
2. Organise your material</h3>
<h2>
<o:p></o:p></h2>
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<br /></div>
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Most of these papers comprise both a main paper and a
supplement. The main paper will feature the figures that tell the story:
experimental design, discovery, main findings, interesting cases. It should be
written for the interested reader who will mainly trust you on the experimental and
analysis details.The supplement is for the reader (including a reviewer or
two) who does <i>not</i> trust you. Sometimes, on other people's papers, <i>you</i> will be that reader. The
supplement should have the same flow, but have all the supporting details that
tell that reader the data and analysis are kosher.</div>
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<br /></div>
<h3>
3. Figures first</h3>
<h2>
<o:p></o:p></h2>
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<br /></div>
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Make good figures that illustrate your point, and test them
out in presentations, first to the group, then to colleagues in your institute,
and then more widely. You’ll fine-tune the figures as you go. Your presentation
will need quite a bit scaffolding (why the question is interesting, about your
experimental design, key statistics), but don’t be afraid to show sample data from
your results to show your motivation. Consider showing a boring and interesting
case side by side. You may find this scaffolding can be condensed into your
Figure 1 for the paper. You can show other figures in the supplement if they
support your work.<o:p></o:p></div>
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<br /></div>
<h3>
4. Put pen to paper</h3>
<h2>
<o:p></o:p></h2>
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<br /></div>
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Once your figures flow, you can write the results. You can
also start working on the supplement, following the same general flow. All the ‘data
is good’ plots will go in the supplement, as it can have extra “lemmas” about
the data. Don’t skimp in the supplement – include technical details supporting
things like, why your normalisation is sensible, or better than other approaches.
If the supplement gets big, provide an index on the supplement for navigation. The
sceptical reader will like to see this.<o:p></o:p><br />
<br /></div>
<h2>
</h2>
<h3>
5. Focus on the results</h3>
<h2>
<o:p></o:p></h2>
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<br /></div>
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Write the introduction and discussion <i style="mso-bidi-font-style: normal;">after</i> you are happy with your results write-up. Think about the
readers and the reviewers, and make sure to cite widely. If you are coming into
a new arena with this high-throughput approach, lavish praise on the importance
of the field and the massive amount of individual loci work on which you are
building. Basically, if you are publishing a large-scale approach in an area
that hasn’t had one, avoid being seen as an interloper; read the papers, cite
them – and you are likely to find a couple of new angles on your work through
this process.<o:p></o:p><br />
<br /></div>
<h2>
</h2>
<h3>
6. Length angst</h3>
<h2>
<o:p></o:p></h2>
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<br /></div>
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If you are aiming for a journal with strict length limits (and I do wonder why we tolerate this in this day and age), don’t let that hold you
back at the submission phase. Write as much as you need to, and acknowledge the
length in your cover letter. Emphasise that you want the reviewers to have a
full understanding of the science. For these more restricted space papers,
reviewing at that density is often really hard – the text can be edited after review.</div>
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<br /></div>
<h3>
7. Be open</h3>
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<br /></div>
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It is pretty standard that you will be publishing eventually open access (certainly if you are NIH, or Wellcome Trust and other funders). It is easier to do this via journals which automatically handle the open access submission (Plos, Genome Biology, BMC series and many others, sometime with open access fees). Due to the funder mandates pretty much every journal will at least allow submission of your author manuscript to PubMedCentral, but doing it yourself is quite annoying. </div>
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<br /></div>
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There are new experiments in open publishing as well to look at. Two examples are <a href="http://f1000research.com/">F1000</a> and <a href="http://biorxiv.org/">Bioarxiv</a>. In F1000 the whole process of submission, peer review and publication is done in the open - it interesting to watch open peer review in action. Bioarxiv is following the more physics pre-print server, and many journals allow pre-print posting whilst a paper is under review. This is a cool way to stop being scooped and provides a way to get community input ("informal peer review"). I think we're in an experimentation phase of this next stage in open science, and it's going to be interesting to see where we end up.</div>
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<h3>
8. Tidy up and submit your data</h3>
<h2>
<o:p></o:p></h2>
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Make sure you have all the raw data to submit, with the
meta-data nicely tidied up (ideally, your LIMS system will have this ready to go
by default). Submit your structured data (DNA, Proteomics, Metabolomics, X-ray structure, EM) to the appropriate archive (<a href="http://www.ebi.ac.uk/">EMBL-EBI</a> has the full range). Have a directory that you keep in house; otherwise, put all the
intermediate datasets and files on the web. This is good for transparency – the sceptical reader will be even more reassured when he or she knows that they can (if they want) not only get the raw data (a given for molecular biology) but can also come into the analysis half-way through. About half of these readers could be future members of a group you may ask to
"follow the analysis in paper A", or to confirm that "XXX did this in paper B". Do this for your own group's sanity and for extra brownie points from readers around the world.<br />
<o:p></o:p></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com10tag:blogger.com,1999:blog-1688712549144760104.post-73783994103408105642016-05-10T09:13:00.003-07:002016-05-12T14:24:34.521-07:00Managing and Analysing Big Data - Part II<div dir="ltr" style="text-align: left;" trbidi="on">
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This is the second of three blog posts about planning,
managing and delivering a ‘big biodata’ project. Here, I share some of my
experience and lessons learned in management and analysis – because you can’t
have one without the other.<br />
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<o:p></o:p></div>
<h2>
</h2>
<h2>
Management</h2>
<h1>
<o:p></o:p></h1>
<h3>
</h3>
<h3>
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<h3>
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<h3>
1. Monitor progress – actively!</h3>
<h2>
<o:p></o:p></h2>
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You need a good structure to monitor progress, particularly
if you’re going to have more than 10 samples of experiments. If this is a
one-off, use a program that’s well supported at your institute, like FileMakerPro or... Excel (more on this below). If you’re going to do this a lot, think about investing in a LIMS
system, as this is better suited to handling things at a high level of detail routinely.
Whatever you use, make sure your structure invites active updating – you’ll
need to stay on top of things and you don’t want to struggle to find what you need.<o:p></o:p><br />
<br /></div>
<h3>
</h3>
<h3>
2. Excel (with apologies to the bioinformaticians)</h3>
<br />
Most bioinformaticians would prefer the experimental group not to use Excel for tracking, for
very good reasons: Excel provides too much freedom, has extremely annoying (sometimes dangerous) "autocorrect" schemes, fails in weird ways and is often hard to integrate into other data flows. However, it is a pragmatic choice for data entry and project
management due to its ubiquity and familiar interface.<br />
<br />
Experimental group: before you set up the project tracking in Excel, discuss it with your
computational colleagues, perhaps offering a bribe to soften the blow. It will
help if the Excel form template comes out of discussions with both groups, and bioinformaticians can set up drop-downs with fixed text where possible and use
Excel’s data entry restrictions to (kind of) bullet proof it.<br />
<br />
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<o:p></o:p></div>
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One important thing with Excel: NEVER use formatting or colour to be the primary store
of meaning. It is extremely hard to extract this information from Excel into other schemes. Also, two things might look the same visually (say, subtly different shades of red), but are computationally as different as red and blue. When presentation matters (often to show progress against targets), you or your colleagues can (pretty easily) knock
up a pivot table/Excel formula/visual basic solution to turn basic information
(one type in each column) into a visually appealing set of summaries. <o:p></o:p></div>
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<h3>
3. Remember planning?</h3>
<h2>
<o:p></o:p></h2>
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When you <a href="http://genomeinformatician.blogspot.co.uk/2016/05/advice-on-bigdata-experiments-and.html">planned the project</a> (you did plan, right?),
you decided on which key confounders and metadata to track. So here’s where you
set things up to track them, and anything else that’s easy and potentially
useful. What’s potentially useful? It’s hard to say. Even if things look
trivial, they (a) might not be and (b) could be related to something complex that
you can’t track. You will thank yourself later for tracking things when you
regress this out. <o:p></o:p></div>
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<h3>
4. Protect your key datasets</h3>
<h2>
<o:p></o:p></h2>
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Have a data ‘write only’ area for storing the key datasets
as they come out of your sequencing core/proteomics core/microscopes. There are
going to be sample swaps<span style="mso-spacerun: yes;"> </span>(have you
detected them? For sure they will be there for any experimental scheme with more than 20 samples), so don’t edit the received files directly! Make sure you have
a mapping file, kept elsewhere, showing the relationships between the original
data and the new fixed terms. <o:p></o:p></div>
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5. Be meticulous about workflow</h3>
<h2>
<o:p></o:p></h2>
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Keep track of all the individual steps and processes in your
analysis. At any point, it should be possible to trace individual steps back to
the original starting data and repeat the entire analysis from start to finish.<br />
<br />
My approach is to make a new directory with soft-links for each ‘analysis
prototyping’, then lock down components for a final run. Others make heavy use
of iPython notebooks – you might well have your own tried-and-tested approach.
Just make sure it’s tight.<o:p></o:p></div>
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6. “Measure twice, cut once”</h3>
<h2>
<o:p></o:p></h2>
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If you are really, really careful in the beginning, the
computational team will thank you, and may even forgive you for using Excel. Try
to get a test dataset out and put it all the way through as soon as possible.
This will give you time to understand the major confounders to the data, and to tidy things up before the full analysis.<o:p></o:p></div>
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You may be tempted to churn out a partial (but in a more limited sense ‘complete’) dataset early, perhaps even for a part-way publication. After some experience playing this
game, my lesson learned is to go for full randomisation every time, and not to have a partial, early dataset that breaks the randomisation of the samples against time or key reagents. The alternative is the commit to a separate, early pilot experiment, which explicitly will not be combined with the main analysis. It is fine
for this preliminary dataset to be mostly about understanding confounders and
looking at normalisation procedures.<br />
<o:p></o:p></div>
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<h3>
7. Communicate</h3>
<h2>
<o:p></o:p></h2>
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It is all too easy to gloss over the social aspect of this
kind of project, but believe me, it is absolutely essential to get this right.
Schedule several in-person meetings with ‘people time’ baked in (shared dinner,
etc.) so people can get to know each other. Have regular phone calls involving
the whole team, so people have a good understanding of were things stand at any
given time. Keep a Slack channel or run an email list open for all of those little exchanges that
help people clarify details and build trust. </div>
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Of course there will be glitches – sometimes quite serious –
in both the experimental work and the analysis. You will have to respond to
these issues as a team, rather than resorting to finger-pointing. Building
relationships on regular, open communication raises the empathy level and helps
you weather technical storms, big and small.</div>
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<o:p></o:p></div>
<h2>
Analysis</h2>
<div>
<br /></div>
<h1>
<o:p></o:p></h1>
<h3>
1. You know what they say about ‘assume’</h3>
<h2>
<o:p></o:p></h2>
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Computational team:<b style="mso-bidi-font-weight: normal;"> Don’t
assume</b> your data is correct as received – everything that can go wrong,
will go wrong. Start with unbiased clustering (heat-maps are a great
visualisation) and let the data point to sample swaps or large issues. If you
collect data over a longer period of time, plot key metrics v. time to see if
there are unwanted batch/time effects. For sample swaps, check things like
genotypes (e.g. RNAseq-implied to sample-DNA genotypes). If you have mixed
genders, a chromosome check will catch many sample swaps. Backtrack any
suspected swaps with the experimental team and fail suspect samples by default. Sample swaps are the same as bugs in analysis code - be nice to the experimental team so they will be nice when you have a bug in your code.<o:p></o:p></div>
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Experimental team:<b style="mso-bidi-font-weight: normal;"> Don’t
assume</b> the data is correct at the end of an analytical process. Even with
the best will in the world, careful analysis and detailed method-testing
mistakes are inevitable and flag results that don't feel right to you. Repeat appropriate sample-identity checks at key time
points. At absolute minimum, you should perform checks after initial data receipt
and before data release.<o:p></o:p></div>
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2. One thing you can assume</h3>
<h2>
<o:p></o:p></h2>
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You can safely assume that there are many confounders to
your data. But thanks to careful planning, the analysis team will have all
the metadata the experimental team has lovingly stored to work with. <o:p></o:p></div>
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<a href="https://www.blogger.com/null" style="mso-comment-date: 20160509T1308; mso-comment-reference: mtb_1;">Work with untrained methods </a> (e.g. straight PCA; we’re also very fond
of PEER in my group), and correlate the known covariates. Put the big ones in
the analysis, or even regress them out completely (it’s usually best to put
them in as terms in the downstream analysis). Don’t be surprised by strongly
structured covariates that you didn’t capture as metadata. Once you have
convinced yourself that you are really not interested, move on.<o:p></o:p></div>
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(Side note on PCA and PEER: take out the means first, and
scale. Otherwise, your first PCA component will be means, and everything else
will have to be orthogonal to that. PEER, in theory, can handle that non-orthogonality, but it's a big ask, and the means in particular are best removed. This means this is all wrapped up with normalisation, below.)<o:p></o:p></div>
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<h3>
3. Pay attention to your reagents</h3>
<h2>
<o:p></o:p></h2>
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Pay careful attention to key reagents, such as antibody or
oligo batches, on which your analysis will rely. If they are skewed, all sorts
of bad things can happen. If you notice your reagent data is skewed, you’ll have
to make some important decisions. Your carefully prepared randomisation
procedure will help you here.<o:p></o:p></div>
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<h3>
4. The new normal</h3>
<h2>
<o:p></o:p></h2>
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It is highly unlikely that the raw data can just be put into downstream analysis schemes - you will need to normalise. But what is your normalisation procedure? Lately, my mantra is,
“If in doubt, inverse normalise.” Rank the data, then project those ranks back
onto a normal distribution. You’ll probably lose only a bit of power – the
trade-off is that you can use all your normal parametric modelling without
worrying (too much) about outliers. <o:p></o:p></div>
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You need to decide on a host of things: how to correct for
lane biases, GC, library complexity, cell numbers, plate effects in imaging. Even
using inverse normalisation, you can do this in all sorts of ways (e.g. in a genome direction or a
per-feature direction – sometimes both) so there are lots of options, and no automatic flow chart about how to select the right option.</div>
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<o:p></o:p></div>
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Use an obvious technical normalisation to start with (e.g. read
depth, GC, plate effects), then progress to a more aggressive normalisation (i.e.
inverse normalisation). When you get to interpretation, you may want to present
things in the lighter, more intuitive normalisation space, even if the
underlying statistics are more aggressive.</div>
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<o:p></o:p></div>
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You’ll likely end up with three or four solid choices through this flow chart. Choose
the one you like on the basis of first-round analysis (see below). Don’t get
hung up on regrets! But if you don’t discover anything interesting, come back to this point and choose again. Taking a more paranoid approach, using two normalisation schemes through
the analysis will give you a bit of extra security - strong results will not change too much on different "reasonable" normalisation approaches.<o:p></o:p></div>
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5. Is the Data good?</h3>
<h2>
<o:p></o:p></h2>
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Do a number of ‘data is good’ analyses.<br />
<br />
<ul style="text-align: left;">
<li>Can you replicate the
overall gene-expression results? </li>
<li>Is the SNP Tv/Ts rate good? </li>
<li>Is the number of
rare variants per sample as expected? </li>
<li>Do you see the right combination of
mitotic-to-nonmitotic cells in your images? </li>
<li>Where does your dataset sit, when
compared with other previously published datasets? </li>
</ul>
<br />
These answers can guide you to the ‘right’ normalisation
strategy - so flipping between normalisation procedures and these sorts of "validation" analyses helps make the choice of the normalisation.<br />
<o:p></o:p></div>
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<h3>
6. Entering the discovery phase</h3>
<h2>
<o:p></o:p></h2>
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‘Discovery’ is a good way to describe the next phase of the
analysis, whether it’s differential-expression or time-course or GWAS. This is where one needs to have quite a bit more discipline in how to handle the statistics.<br />
<br />
First, use a
small (but not too small) subset of the data to test your pipelines (in Human,
I am fond of the small, distinctly un-weird chromosome 20). If you can make a QQ plot, check the QQ plot looks good (ie, calibrated). Then, do the whole
pipeline.<o:p></o:p></div>
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7. False-discovery check</h3>
<h2>
<o:p></o:p></h2>
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Now you’re ready to apply your carefully thought-through ‘false
discovery rate’ approach, ideally without fiddling around. Hopefully your QQ plot looks good (calibrated with a kick at the end), and you can roll out false discovery control now. Aim to do this just once (and when that happens, be very proud). <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
8. There is no spoon</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
At this point you will either have some statistically
interesting findings above your false discovery rate threshold, or you won’t have anything above threshold. In neither
case should you assume you are successful or unsuccessful. You are not there
yet.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
9. Interesting findings</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
You may have nominally interesting results, but don’t trust
the first full analysis. Interesting results often enrich errors and artefacts earlier on in your process. Be paranoid about the underlying variant
calls, imputation quality or sample issues.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Do a QQ plot (quantile-quantile plot of the P values,
expected v. observed). Is the test is well calibrated (i.e. QQ plot starts on
the expected == observed, with a kick at the end)? If you can’t do a straight-up
QQ plot, carry out some close alternative so you can get a frequentist P value out. In my experience, a
bad QQ plot is the easiest way to spot dodgy whole-genome statistics. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Spot-check that things make sense up to here. Take one or
two results all the way through a ‘manual’ analysis. Plot the final results so
you can eyeball outliers and interesting cases. Plot in both normalisation spaces (i.e. ‘light’ and
aggressive/inverse).<br />
<br />
For genome-wide datasets, have an ‘old hand’ at genomes/imaging/proteomics eyeball
either all results or a random subset on a browser. When weird things pop up
("oh, look, it’s always in a zinc finger!"), they might offer an alternative (and
hopefully still interesting, though often not) explanation. Talk with colleagues who have done similar things, and listen to the war stories of nasty, subtle artefacts that mislead us all.</div>
<div class="MsoNormal">
<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
10. ‘Meh’ findings</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
If your results look uninteresting:<br />
<br />
<br />
<ul style="text-align: left;">
<li>Double check that things
have been set up right in your pipeline (you wouldn’t be the first to have a
forehead-smacking moment at this point). </li>
<li>Put dummy data that you know <i style="mso-bidi-font-style: normal;">should</i> be right into the discovery
pipeline to test whether it works. </li>
<li>Triple-check all the joining mechanisms (e.g.
the genotype sample labels with the phenotype). </li>
<li>Make sure incredibly stupid
things have not happened – like the compute farm died silently, and with spite,
making the data files <i style="mso-bidi-font-style: normal;">look</i> valid when
they are in fact… not.</li>
</ul>
<br />
<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
11. When good data goes bad</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
So you’ve checked everything, and confirmed that nothing obvious
went wrong. At this point, I would allow myself some alternative normalisation approaches, FDR thresholding or confounder manipulation. But stay disciplined here.<br />
<br />
Start a mental audit of your monkeying around (penalising yourself appropriately in your FDR). I normally allow four or five trips on the normalisation merry-go-round or on the “confounders-in-or-out” wheel. What I really want out of these rides is to see a P value / FDR rate that’s around five-fold better than a default threshold (of, say 0.1 FDR, so hits at 0.02 FDR or better).<br />
<br />
Often you are struggling here with the multiple testing burden if there is a genome-wide scan. If you are not quite there with your FDRs, here are some tricks: examine whether just using protein-coding genes will help the denominator, and look at whether restricting by expression level/quantification helps (i.e. removing lowly expressed genes which you couldn't find a signal in anyway). </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
You may <i>still</i> have nothing over threshold. So, after a drink/ice cream, open up your plan to the “Found Nothing Interesting” chapter (you did that
part, right?) and follow the instructions. </div>
<div class="MsoNormal">
<o:p></o:p></div>
<br />
<div class="MsoNormal">
Do stop monkeying around if you can’t get to that 0.02 FDR. You
could spend your career chasing will-o-the-wisps if you keep doing this. You
have to be honest with yourself: be prepared to say “There’s nothing here.” If
you end up here, shift to salvage mode (it’s in the plan, right?). <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
12. But is it a result?</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Hopefully you have something above threshold, and are pretty
happy as a team. But is it a good biological result? Has your FDR
merry-go-round actually been a house of mirrors? You don’t want to be in any
doubt when you go to pull that final trigger on a replication / validation experiment. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
It may seem a bit shallow, but look at the top genes/genomic
regions, and see if there is other interesting, already-published data to
support what you see. I don't, at this point, trust GO analysis (which often is "non random"), but the Ensembl phenotype per gene feature is really freakily useful (in particular with its ‘phenotypes in
orthologous genes’ section) and the UniProt comments section. Sometimes you stumble across a complete amazing
confirmation at this point, from a previously published paper. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
But be warned:
humans can find patterns in nearly everything – clouds, leaf patterns,
histology, and Ensembl/UniProt function pages. Keep yourself honest by inverting the list of genes, and
look at the <i>least</i> interesting genes
from the discovery process. If the story is overtly consistent from bottom to
top, I’d be sceptical that this list actually provides confirmation. Cancer
poses a particular problem: half the genome has been implicated in one type of cancer
or another by some study.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<o:p></o:p></div>
<div class="MsoNormal">
Sometimes though you just have a really clean discovery dataset, with no previous literature support,
and you need to do the replication in place without any more confidence that
your statistics are confirming something valuable.<o:p></o:p><br />
<br /></div>
<h2>
</h2>
<h3>
13. Replication/Validation</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Put your replication/validation strategy into effect. You might have
baked it into your original discovery. Once you are happy with a clean (or as
clean as you can get) discovery and biological context, it’s time to pull the
trigger on the strategy. This can be surprisingly time consuming. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
If you have specific follow-up experiments, sort some of
them out now and get them going. You may also want to pick out some of the juiciest
results to get additional experimental data to show them off. It’s hard to
predict what the best experiment or analysis will be; you can only start
thinking about these things when you get the list.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
My goal is for the replication / validation experiments to be as unmanipulated as possible, and you should be confident that they will work. It's a world of pain when they don't!<br />
<br /></div>
<h2>
</h2>
<h3>
14. Feed the GO</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
With the replication/validation strategy underway, your analysis can
now move onto broader things, like the dreaded GO enrichments. Biology is very
non-random, so biological datasets will nearly always give some sort of
enriched GO terms. There are weird confounders both in terms of genome
structure (e.g. developmental genes are often longer on the genome) and
confounders in GO annotation schemes. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Controlling for all this is almost impossible, so this is
more about gathering hints to chase up in a more targeted analysis. Or to
satisfy the “did you do GO enrichment?” requirement that a reviewer might ask. Look at other things, like
related datasets, or orthologous genes. If you are in a model organism, Human
is a likely target. If you are in Human, go to mouse, as the genome-wide
phenotyping in mouse is pretty good now). Look at other external datasets you can bring in, for example Chromatin states on the genome, or lethality data in model organisms.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<h3>
15. Work up examples</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Work up your one or two examples, as these will help people
understand the whole thing. Explore data visualisations that are both appealing
and informative. Consider working up examples of interesting, expected and even
boring cases.<o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<h3>
16. Serendipity strikes!</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Throughout this process, always be ready for serendipity to strike. What might look
like a confounder could turn out to be a really cool piece of biology – this
was certainly the case for us, when we were looking at <a href="http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004798">CTCF across human individuals</a> and found a really interesting CTCF behaviour involved in X inactivation. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
My guess is that serendipity has graced our group in one out of every ten or
so projects – enough to keep us poised for it. But serendipity must be
approached with caution, as it could just an artefact in your data that simply
lent itself to an interesting narrative. If you’ve made an observation and
worked out what you think is going on, you almost have to create a new
discovery process, as if this was your main driver. It can be frustrating,
because you might now not have the ideal dataset for this biological question. In the worst case, you might have to set
up an entirely new discovery experiment. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
But often you are looking at a truly interesting phenomenon
(not an artefact). In our CTCF paper, the very allele-specific behaviour of two
types of CTCF sites we found was the clincher: this was real (Figure 5C). That was a
glorious moment.<o:p></o:p></div>
<h3>
<br />17. Confirmation</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
When you get the replication dataset in, slot it into place.
It should confirm your discovery. Ideally, the replication experiments fit in
nicely. Your cherry-on-the-cake experiment or analysis will show off the top
result. <o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<h3>
<br />18. Pat on the back if it is boring</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
The most important thing to know is that sometimes, nothing
too interesting will come out of the data. Nobody can get a cool result out every large scale experiment. These will be sad moments for you and the team, but be
proud of yourself when you don’t push a dataset too far - and for students and postdocs, this is why having two projects is often good. You can still publish an
interesting approach, or a call for larger datasets. It might be less exciting,
but it’s better than forcing a result.<o:p></o:p></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com5tag:blogger.com,1999:blog-1688712549144760104.post-2575025796949055072016-05-09T06:11:00.000-07:002016-05-09T13:23:09.217-07:00Advice on Big Data Experiments and Analysis, Part I: Planning<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: "verdana" , sans-serif;">Biology has changed a <i style="mso-bidi-font-style: normal;">lot</i>
over the past decade, driven by ever-cheaper data gathering technologies:
genomics, transcriptomics, proteomics, metabolomics and imaging of all sorts. After
a few years of gleeful abandon in the data generation department, analysis has
come to the fore, demanding a whole new outlook and on-going collaboration
between scientists, statisticians, engineers and others who bring to the table a very broad range of
skills and experience.<o:p></o:p></span></div>
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<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Finding meaning in these beautiful datasets and connecting
them up, particularly when they are extremely different from one another, is a detail-riddled
journey fraught with perils. Innovation is happening so quickly that trusty
guides are rather thin on the ground, so I’ve tried to put down some of my hard-won
experience, mistakes and all, to help you plan, manage, analyse and deliver these
projects successfully. <o:p></o:p></span></div>
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<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Without up-front planning, you won’t really have much of a
‘project’. Throwing yourself into data gathering just because it’s ‘cheap’ or ‘possible’
is really not the best thing to do (I've seen this happen a number of times - and embarrassingly I've done it myself). ‘Wrong’ experiments are time vampires: they
will slurp up a massive amount of your time and energy, potentially exposing
you to reputational risk in the event you are tempted to force a result out of
a dataset. <o:p></o:p></span></div>
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<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
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<span style="font-family: "verdana" , sans-serif;">This post, the first of three, is about having the strongest
possible start for your project via good planning.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
1. Buddy up</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">In the olden days, experimental biologists would generate a
bunch of data and then ask a bioinformatician how to deal with it. Well,
that didn’t work too well. At the very outset of a project, we have all learned that you
need to ensure there are two PIs: one to focus on the
experimental/sample-gathering side, and one to keep the analysis in their sights at all
times. These two PIs must have healthy, mutual respect, and be motivated by the same overall goal. There are a few, rare individuals who can honestly be described as being
both experimental and computational, but in most cases you’ll need two people
to make sure both perspectives are represented in the study’s design.</span><br />
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Now, I’m not saying that experimentalists are strangers to
analysis, or that bioinformaticians are strangers to data generation. It’s just
important to acknowledge that being able to ‘talk the talk’ of another
discipline does not, on its own, qualify you to manage that end of the project,
with all its complexities, gotchas and signature fails. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">As with anything you set out to do for a couple of years,
you’ll need to make sure you are working with someone you get on with. There
will be tense moments, and you’ll get past them if your co-PI shares your
motivation and goal. Provided you get on and share information as you go,
buddying up will save you resources in the long run.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Note to Experimental PIs: never assume you’ll be able to tack on an
analytic collaboration at the end, after you’ve gathered the data. You don’t
want to be caught out by not having considered some important analysis aspect. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Note to Computational PIs: Never assume you can delegate sample
management and experimental details to a third party through facility
technicians. You know there is a huge difference between experimental data and
good experimental data – you will need a trusted experimental partner who
understands all the relevant confounders and lab processes, and who can spot a serendipitous result if one pops out, if you’re going to have a successful project.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
2. Outlining</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">The idea that you can generate datasets first and then watch
your results emerge from the depths is simply misguided. It is really quite
painful (and wasteful) when a dataset doesn’t have what it needs to support an
analysis - it is a set-up for forcing results. Before you do anything, have a brief
discussion with your co-PI about the main questions you are looking to answer
and make a high-level sketch of the project.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">I’m not talking about a laboured series of chapter outlines
– the main thing is to determine the central question. Large-scale
data-gathering projects often focus on basic, descriptive things, like, “How much
of phenomenon X do we see under Y or Z conditions?” Sometimes the questions are
more directed, for example, “How does mitosis coordinate with chromosomal
condensation?” <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<span style="font-family: "verdana" , sans-serif;">Outlining your hypotheses need only be as simple as, “At the end, we will have a list of proteins in the Q
process.” If you’re hoping to test a hypothesis, aim for something straightforward, like, “I believe the B process is downstream of the Ras process.” </span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<span style="font-family: "verdana" , sans-serif;">Consider your possible hypothesis-testing modes, but avoid trying too hard to
imagine where the analysis might take you; your data and analysis might not agree with
your preconceptions in the end. </span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<span style="font-family: "verdana" , sans-serif;">Also, do not commit to specific follow-up strategy too early! Your
follow-up strategy should be determined after your initial analysis has been
explored, or your pilot study has been performed.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
3. Back-of-the-envelope ‘power calculations’</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Take some of the anxiety out of the process by doing a rough
calculation before getting into things too deeply. If you (or someone else) has
done a similar analysis well in the past, simply use their analysis as a basis
for your rough estimate. If you are on completely new ground, make sure you
factor in false positives (e.g. mutation calls, miscalled allele-specific
events, general messiness) and pay careful attention to frequencies (e.g. alleles,
rare cell types). <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Many a bad project could have been stopped in its tracks by a
half hour’s worth of power analysis. Unless you really need to impress
reviewers, you probably don’t need to go overboard – just make a quick sketch.
But be honest with yourself! It is all too easy to fudge the numbers in a power
analysis to get an answer you want. Use it as a tool for looking honestly at what sort of results you could expect.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
4. Get logistical</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
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<span style="font-family: "verdana" , sans-serif;">Plan the logistics according to Sod’s Law. Assume everything
that can go wrong will go wrong at least once. This is particularly important if
you are scaling up, for example moving an assay from single-well/Eppendorf to an
array. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">For assays, give yourself at least a year for scale-up in
the lab (better still, do a pilot scale-up with publication before moving on to the real
thing). Pad out all sample acquisition with at least three months for general
monkeying around.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
5. Have a healthy respect for confounders</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Think about the major confounders you will encounter
downstream, and randomise your experimental flow accordingly. That is, do <i>not</i> just do all of state X first,
then progress to state Y, then Z. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Make sure you store all the known confounders (e.g. antibody
batch number, day of growth). Try to work off single antibody batches/oligo
batches for key reagents. If you know you will need more than one batch, remember
the randomisation! You <i>absolutely do not</i> want the key reagent batch being
confounded with your key experimental question, i.e. normal with batch 1,
disease with batch 2. Disaster!</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
6. Plan the analysis</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">If possible, stagger the experimental and analysis work. See if you can have your analysis postdocs come in to the project
later, ideally with some prior knowledge of the work (the best case is that they are around but on another project early, and then switch into this project about a year in). Unfortunately, because
funding agencies like to have neat and tidy three-year projects, this is often
quite difficult to arrange.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Determine when an initial dataset will be available, and time
the data coordination accordingly. Budget at least six months (more likely
12–18 months) of pure computational work. Use early data to ‘kick the
tyres’ and test different analysis schemes, but plan to have a single run of analysis that takes at least 12-18 months.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
7. Replication/validation strategy</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><i>You</i> know you’re not going to cook up the data and analysis,
but will you convince the sceptical reader? Make sure you have a strategy in
place.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">I find it helps to think of this as two separate phases:
discovery, and validation/replication. In discovery, you have plenty of freedom to try out
different methods and normalisation before settling. The validation/replication phase, for
a project of any size, features ‘single-shot’ experiments, which offer a minimal amount of flexibility.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Generally speaking, you should not be doing single-sample-per-state
experiments; rather, you should be carrying out at least two biological
replicates, which is enough to show up any problem. With five or more biological
replicates, you can make good mean estimates. The one exception to the "no single sample" rule is QTL/GWAS, when it
is nearly always better to sample new genotypes each time, rather than
replicate data from the same genotype (i.e. maximise your genetic samples first, and then improve on per-genetic individual variance).</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
8. Confront multiple testing</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">How many tests are you going to do? If it is genome-wide project, you will do a lot, so you need to control for your multiple testing. This is partly about the power calculation, but requires some up-front thinking. Will you do permutations,
or trust to the magic of p.adjust() (A wonderful R function that has a set of False Discovery Rate approaches)? What will you do if you find nothing? Is
finding nothing interesting in itself? <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">You’ll all have agreed to try and discover something
excellent, but make sure you have a serious conversation up front with your co-PI
about what you’ll do if you don’t find anything interesting. Is there a fall-back plan? <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Traditional, outright replication of an entire discovery
cohort needs as much logistical planning – if not more – as the discovery itself. You
might decide to use prior data to show how yours is at least solid and
good. Organise this beforehand.</span></div>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">
9. Publishing parameters</span></h2>
<h2>
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></h2>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">What would you consider to be the first publishable output
from this project? Could you put it into a technical publication (e.g. assay
scale up, bespoke analytical methods)?<span style="mso-spacerun: yes;">
</span>At the beginning of the project, you and your co-PI should agree on the broad
parameters of authorship on papers, and how multiple papers might be
coordinated. For example, will you credit two first authors and two last
authors, swapping in priority if there is more than one paper?<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: verdana, sans-serif;"><br /></span>
<span style="font-family: verdana, sans-serif;">If you are a more senior partner in a collaboration, be
generous with your “last last” position. Your junior PI partners need it more
than you do!</span></div>
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<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
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<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;"><br /></span></h2>
<h2 style="text-align: left;">
<span style="font-family: "helvetica neue" , "arial" , "helvetica" , sans-serif;">Next up</span></h2>
</div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">This is the first of three posts. Next up: Managing your Big Data project.</span></div>
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<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com1tag:blogger.com,1999:blog-1688712549144760104.post-1504007568761684852016-01-14T07:02:00.002-08:002016-01-14T07:55:49.699-08:00In defence of model organisms<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">I have written about the rise of <a href="http://genomeinformatician.blogspot.co.uk/2015/05/human-as-model-organism.html">human
as a first-class model organism</a>, and am an enthusiastic user of this
outbred, large vertebrate, which can walk right into pre-funded phenotyping centres
(hospitals). However, some scientists are (somewhat flippantly) predicting ‘the
demise of all non-human model organisms’ completely, only conceding the
necessity for using mouse in impossible-in-human verification experiments. Although
such positions tend to be put forward in jest, their underlying argument
resonates: given our obsession on human health, and how much we can do humans –
with broad outbred genetics, iPSC cell lines and organoids – why should we
bother with other systems?<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
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<span style="font-family: Verdana, sans-serif;">This is dangerous thinking, mainly because it vastly
overestimates our knowledge of biological systems at atomic, molecular,
cellular and whole-body scales. Like all areas of science, our grasp of biology
is variable in depth: there are a few areas we know surprisingly well, for
example our quantum grasp of some specific enzyme catalysis, or the behaviour
of groups of molecules relaying a signal in the cAMP signalling pathway. But
these are mere pinpricks of light in an otherwise obscure landscape. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">There is a seemingly solid foundation of at least the total
parts lists by using genomes: good representation of the entire human genome, and
a respectable catalogue of human protein-coding genes (though many annoying-to-nail-down
details need sorting). But this gives us a false sense of security about our
knowledge, both because this list is not complete in its details, but far more
importantly, the list is just the start of understanding life (human included)
and we are nowhere near the end of this journey.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Molecular scales</span></h3>
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<span style="font-family: Verdana, sans-serif;">Even with a complete parts list of proteins in humans, we
are still clueless about what vast tracts of well-characterised protein coding
genes actually do. For about 8000 proteins we have a good idea of at least one
of their roles; for another around 7000 proteins we have some hints. But even
this knowledge can be very partial. For example, the Huntingtin gene we know is involved in Huntington's disease via a trinucleotide expansion – yet we
have almost no knowledge of its molecular function. We know other genes <span style="mso-spacerun: yes;"> </span>are key mediators in disease, for example
C9orf72 in amyotrophic lateral sclerosis (of ice bucket fame), yet we know very little about about its cellular or molecular function. And
this patchy knowledge gets far worse as we move away from proteins. Every year
it feels like a new class of non-coding RNA is defined, but pinning down
functions for them (including potentially no function, the hardest thing to
show) is elusive, beyond some individual cases.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Cellular scales</span></h3>
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<span style="font-family: Verdana, sans-serif;">Imagine we knew the full parts list of proteins and RNAs, and
their individual functions. Somehow these proteins go on to make cells, and the
cells form organs. Huge unknowns dominate the landscape of cellular structure
and mechanism. For example, the massive, Death-Star-like Vault complex has a
large RNA component, hangs around in the nucleus and is quite easy to visualise
with electron microscopy - but we have no firm idea of what it is doing. Or take the
host of vesicles and membrane-bound structures zipping around every cell.
Presumably they’re doing some kind of cellular ‘housekeeping’ (specific to
different cell types), but we’re gloriously ignorant of the details. How do
they know where they’re going? How does the right thing get into the right
vesicle? As soon as you start poking into even the easiest-to-observe cellular
phenomena, there are a surprising number of unknown components and
their interactions.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Organ scales</span></h3>
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<span style="font-family: Verdana, sans-serif;">Research into nearly every combination of cells turns up far
more questions than answers. Even ‘simple’ multi-cellular systems, like the gut,
have mysterious ways of ensuring that the right cells divide and differentiate
at the right time. In more complex systems, interactions between cells give
rise to clearly observable (sometimes model-able) phenomena, for example beating
heart muscle, or the capture and excretion of toxins.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">We observe that assemblies of cells arise during development,
but can only just begin to describe their behaviour, sort of. As for our level
of ignorance about the four-billion-structured mass that is the human brain…
let’s just say there is much work to be done.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">The massive intellectual effort it takes to elucidate the
human system from atomic to organ scales is analogous to discovering, climbing
and mapping the entirety of the Himalayas in detail. In the dark. With constant
storms. It is challenging, tricky and demands both innovation and a huge
attention to detail – and done by a large number of people. We are in the
foothills of this task now – and it will take many</span><span style="font-family: Verdana, sans-serif;"> more</span><span style="font-family: Verdana, sans-serif;"> decades.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Just another vertebrate</span></h3>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">In the main, humans are 'just another vertebrate' – just one
of evolution’s run-of-the-mill metazoans (albeit with a high-end brain), part
of the great, wandering trail of cellular life on Earth. Despite the diversity
of the final life forms, evolution is mainly a tinkerer at the molecular and
cellular level, rather than radical innovator. Huge, key macromolecules, such
as the Ribosome, have basically the same fundamental structure and function
from seeming the very first living cell. This is true also in development; for
example, amazingly, the developmental program that sets up the humble fruit fly’s
pulsating tube of 36 cells – its proto heart – is quite similar to the one that
determines our (or even more impressively, the giraffe’s) - million-cell behemoth
of cardiac engineering. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">What this biological unity means for us as scientists is
that we have our pick of systems to work in, even if they have very different
properties to humans, and can still have confidence that we are understanding
humans in molecular detail. We can attack the hideously complex task of
untangling life at all scales from many different directions.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">The agony of choice</span></h3>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">So which of the millions of species and thousands of
tractable systems (including human!) should you use to tackle a biological
problem? This is the ultimate agony of choice. My contribution to easing some
of this pain: here are three rationales you might use to choose a system that
will ultimately have an impact on our understanding the biology of humans.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Verdana, sans-serif;">
1. To discover key facts about molecular and cellular life, including
humans</span></h4>
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<span style="font-family: Verdana, sans-serif;">Because of the unity of life by evolution, there are many
systems for which one can have one's pick of species and be confident the vast
majority of knowledge will map to human. For example, whichever species you study
to understand the eukaryotic ribosome in detail, the core components will work
pretty much the same. So choose the system(s) that provide the easiest material
to use, or the best system to manipulate. The same applies for things like the
Map kinase cascade, vesicle transport, microtubule function, liver function across
multiple cells, or short-term associative memory in neuronal structures. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">The information gained by studying these systems will map easily to human proteins/cells/organs, with only minor modifications. Choose your organism
partly for the ease of studying the molecules/system of interest, ensuring that
the system you are studying is ‘clearly homologous’ to human. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">For studying molecular and intra-cellular components, choose
one of the yeasts or human cellular systems (human is a fine system!). For
developmental and multi-cellular schemes, you could choose worm, fish, fly or Xenopus, among others. For organ-level systems you probably want a vertebrate (more
directly homologous organs), but you can be confident about choosing a more
exotic species if it really provides a novel angle on a particular system (e.g.
the structured nictonic acetylcholine receptors of electric eels, the giant
snail neuron, Takifugu’s minimal-vertebrate genome).</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Verdana, sans-serif;">
2. To elucidate key principles of biology</span></h4>
<div class="MsoNormal" style="text-align: left;">
<span style="font-family: Verdana, sans-serif;">Certain systems are particularly good for elucidating the key principles underlying biology. We don’t often know what a ‘good understanding’ of a complex system is: what you need to know to understand a signalling pathway, or development, or how a neuronal system works, for instance. For this kind of work you need to be able to dominate a system completely, usually by all means possible so you can build up layers of knowledge and test different schemes experimentally.<br /><br />For this rationale, it needn’t be the case that the components of your system will be directly homologous to human; rather, the argument is that the type of phenomenon you’re looking at is present in both species. Take the very un-human, stereotypical development of <i>C. elegans</i>, in which every individual has exactly the same set of cells. It is really worth studying in detail, precisely because you can have an intellectual dominance of the worm developmental scheme in a way that is near impossible in other systems. Similarly, the ability to manipulate and monitor the living fly or worm brain is exquisite not because we expect to find directly homologous neuronal circuits in humans, but because it helps us find out what we need to know to understand neuronal circuits fully (or, if we’re honest, at all well).<br /><br />This argument bears out, from quantitative ways in signalling pathways (e.g. in bacteria and yeast) through developmental to neuronal processes. Here, the choice of organism is driven by the technologies and measurement modalities, and as such will often be ‘traditional</span><span style="font-family: Verdana, sans-serif;">’, well-established
model organisms with many experimental techniques. However there are some
scenarios where using the diversity of life really helps understand principles,
usually by providing an insight into evolution. For example, studying primitive
metazoans, with proto-muscles and proto-nervous systems provides, via
evolutionary arguments, a huge insight into the evolution and role of these
cells.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h4 style="text-align: left;">
<span style="font-family: Verdana, sans-serif;">
3. Serendipity</span></h4>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">The third rationale is by far the most open: experience over
the past 100 years of research has shown that completely serendipitous
discoveries sometimes lead to radical new technologies. The current poster
child for this is CRISPR/Cas9, which is the now-ubiquitous gene-editing enzyme, which arose from research into bacterial immunity. But there are many others,
from PCR to the discovery of the apicoplast in the malaria parasite. </span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">For people who want to ‘think strategically’, choose the area of research with an outcome in mind. The successfulness of just
“exploring” might sound lazy, but nevertheless, the truth of science is that you can’t predict what you will find. Using arguments of serendipity gives <i style="mso-bidi-font-style: normal;">carte blanche</i> to a molecular biologist
to simply follow his or her nose. However, given the entirely open field, in
practice one has to have an ‘eye-catching’ reason to explore some aspect of
biology, and a good nose to convert this quirk of biology into insightful
knowledge. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">Biology doesn’t disappoint, though, for possibility: the Alaskan tree frog, which
freezes solid over winter (including their heart stopping) and then revives; the
Mexican Axolotl, which can regrow an entire limb as an adult; the tardigrades,
which can be revived from complete desiccation, even after a hard-vacuum
exposure; soil bacteria, in all their chemical weirdness… there is no shortage
of eye-catching stuff.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">It is almost too easy to find interesting things, and we
could probably take much more advantage of serendipity. One can only speculate
on what can be learned from these examples, which is fun in itself (suspended
animation? new chemical fuel stocks? re-growing entire limbs?). It all comes
down to the fact that we know evolution has been far more inventive than our collective
imagination could ever be.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Starting from disease</span></h3>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">Some may bristle at the idea of trying to understand human
disease by uncovering more basic biology in model systems. For those who start from
the standpoint of human disease, it can seem as though basic researchers do not
have their priorities straight – every missed opportunity to cure a disease (in
particular a disease that might be close to a patient advocate’s heart for personal
reasons) can feel like a mistaken prioritisation of funds and effort. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">A less personal argument centres on how new technologies
have radically improved our ability to focus. With the resurgence of human
genetics at scale, iPSCs, organoids and other features, there are plenty of
opportunities to study the human system directly – so why not simultaneously
study things with a clear connection to human disease? Surely such research
would uncover interesting aspects of basic biology and elucidate basic
biological processes along the way? <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">The counterpoint to this view is that one needs a mixed
portfolio, because we can be confident that there are massive tracts of things
we don’t know, and we know that each species and approach wont be good for all
of them. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">My fear is that a single-minded, human-disease-first
perspective – at the expense of all other approaches – will deliver gains on
the diseases for which our current molecular and cellular knowledge is already
adequate – but then stall. </span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">How many diseases are in this class of “tractable
given current knowledge”? It’s hard to know the percentage, but it’s definitely
low – our collective knowledge is poor. More importantly, we don’t know which diseases
will ultimately be tractable. Having chased down only the most promising cases,
we might valiantly throw ourselves against sheer cliffs of ignorance about the mechanisms
of the rest. With a better mix of approaches, we’d be creating paths along and
ladders up those cliffs, making our quest to cure diseases a more manageable
one.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Starting from basics</span></h3>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">Those starting from basic research often want to be honest
about their own motivation. Most basic researchers are just curious about how
life works.<span style="mso-spacerun: yes;"> T</span>hey hope that
their research will have benefits, often with a track record of this happening, sometimes in surprising ways. However there is sometimes a
distasteful process of having to artificially construct an argument to link their proposed research to
disease.<a href="https://www.blogger.com/null" name="_GoBack"></a> Much like the early explorers, they want to
study systems simply in the pursuit of knowledge, sure that it will take them
somewhere interesting but without any idea where it will take them – or how that
might change things – but knowing that what they discover is likely to be
useful.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">I sympathise with this point of view and appreciate its
intellectual integrity, but it opens up an apparent split with translational
research, and encourages people to consider these two worlds as being apart. </span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">There
are two reasons I feel that this “separation” of basic from translational
molecular biology is wrong. Firstly, ‘translation to health’ and ‘basic
research’ are not in opposition. Because of the unity of biology at the
molecular and cellular level, these are fundamentally intertwined intellectual
processes. </span><span style="font-family: Verdana, sans-serif;">Collectively, </span><span style="font-family: Verdana, sans-serif;">we should not need artificial arguments to link them. Secondly, we all have a moral imperative to contribute our skills to
efforts that benefit society, and the ultimate process of changing healthcare
practice due molecular understanding should be something we can all
support.</span><span style="mso-spacerun: yes;"> </span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif; mso-spacerun: yes;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">Importantly, healthcare is a massive part of the economy,
taxpayers and charities want real change and they are happy to fund it. Sustained
change to the quality of our healthcare, enabled by molecular understanding,
will only be delivered (and intelligently applied) with the on-going engagement
of basic science.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Tribal science is unhelpful</span></h3>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">Crude arguments that play “translation” and “basic research”,
or “human disease” and “fundamental discoveries” off each other are depressing.
The idea that humans are the <i style="mso-bidi-font-style: normal;">only</i>
model organism for the future is simply misguided mischief, and opens up the
dangerous possibility that people might actually start to believe it – and it’s
just as frustrating to hear some people claim that one can only do
translational, healthcare related science in humans, and no profound basic discoveries will emerge from
human investigation. Taking an extreme position to make either point is
annoying, but it belies an underlying tension that needs to be resolved. I
think these arguments are more about conflict between of tribes of scientists
who are now interacting more and more. There is also an element of jockeying
for position, both as individuals and as tribes. We need to get beyond this.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<h3>
<span lang="EN-US" style="font-family: Verdana, sans-serif;">Balance is best (but not easy)</span></h3>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;">A ‘balanced portfolio’ sounds great, but there is no
straightforward recipe for compiling one. What does the best ‘balanced
portfolio’ of research look like? How much “pure” disease focus on humans, and what else to add? A tablespoon of yeast, a teaspoon of serendipity and a
dash of electric eel? What part of that portfolio would a particular funding
agency, charity, institute or scheme take on? How to assess a slew of wildly
different proposals, each with different rationales? <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
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<span style="font-family: Verdana, sans-serif;">There are no easy answers to these questions, but we do need
to realise that the lines between ‘basic’ and ‘translational’ research are now
fully blurred – both are essential parts of the same process of understanding
life, with massive spill-over effects across many practical aspects of our
world, our health above all.</span><o:p></o:p></div>
</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com26tag:blogger.com,1999:blog-1688712549144760104.post-60461966517808423192015-12-24T03:34:00.001-08:002015-12-24T03:34:25.616-08:0012th genome of Christmas: The platypus<div dir="ltr" style="text-align: left;" trbidi="on">
In 1799 George Shaw, the head of the Natural History Museum in London, received a bizarre pelt from a Captain in Australia: a duck bill attached to what felt like mole skin. Shaw examined the specimen and wrote up a description of it in a scientific journal, but he couldn't help confessing that it was "impossible not to entertain some doubts as to the genuine nature of the animal, and to surmise that there might have been practised some arts of deception in its structure." Hoaxes were rife at the time, with Chinese traders stitching together parts of different animals - part bird, part mammal - to make artful concoctions that would trick European visitors. Georgian London was becoming rather skeptical of these increasingly fantastical pieces of taxidermy. <br />
<br />
But the duckbilled platypus is no hoax. It is one of the last extant remnants of the egg-laying mammals, monotremes (along with the far more commonplace, but less exotic, Echidnas - hedgehog-like mammals). Duckbilled platypuses have a bizarre set of features: they are the only mammal with a specialised venom organ (one or two shrews have developed poisonous saliva; the duckbill has a specialised claw), with venom strong enough to incapacitate a human. They hunt in muddy streams and have a sixth sense: electrosensation, as in fish, thought to be sensed via their leathery bill. And of course the female lays leathery eggs, out of which hatch tiny little (and super-cute) “hatchlings”, which will first feed from milk patches on the female.<br />
<br />
So in the middle of the first decade of this century, when genome sequencing was becoming marginally more routine, it seemed obvious that at least one monotreme should be on the list. The duckbill simply had to take a star turn. Echidnas are also cute, but, frankly... far less weird.<br />
<br />
And the genome did not disappoint. One complicating factor was platypus sex chromosomes. Even before the genome was sequenced, it was clear that platypus sex was no simple affair. Like all other mammals, platypuses have sex chromosomes, but there are 10 of them in five pairs, rather than the usual two sex chromosomes in one pair. This could lead to 25 possible sexes, but it doesn’t seem like there's much diversity in platypuses. As it turns out, at the key point in meiosis (the process of making sperm and eggs) the five X chromosomes all line up together with the five Y chromosomes in a spectacular act of chromosomal ballet, and divide as one, such that each sperm either gets five X chromosomes or five Y chromosomes, but a mixture, say 3X and 2Y in one direction, and 2X and 3Y in the other. This means that each sperm was either all X or all Y.<br />
<br />
The genome sequence was even more surprising. Birds also have genetic sex determination (in contrast, many reptiles and fish do not). However, the avian system is on different part of the genome (there is no standard way of doing sex chromosomes). It's the other way around from mammals; females have the different chromosomes (ZW) whereas the males are the homogeneous set (ZZ). (An oddity in the naming system for sex chromosomes is that they are always called either XY or ZW, even though the X chromosome in mammals has absolutely no relationship to X in, say, fruitflies. It’s just a convention.) The bizarre split-five-ways sex chromosome in platypus is mainly similar to the Z chromosome (which is similar to the human chromosome 9). It's as if the platypus, genetically, is some mixture of bird and mammal: a bird-like sex determination, but flipped the other way around like mammals. <br />
<br />
The platypus genome also definitively put milk as the major mammalian innovation, before bearing live young. The thin milk produced by platypuses was unclear in its origin (it is also quite hard to study this, as the female is protective of her hatchlings) but the milk caesin gene is clearly in the same location in platypus as placental mammals. <br />
<br />
For me, working on the platypus genome drove home both the diversity of life (egg laying, milk producing, weird sex chromosome mammalian poisoner, anyone?) and its arbitrary nature. Perhaps there is an alternative planet Earth where egg-laying mammals are the dominant species, and the live-young-bearing placental mammals were the oddities. If we had these bizarre sex chromosomes, I am sure there would be all manner of speculation about how this system was somehow linked to our intelligence or dominance. <br />
<br />
But on this Earth, platypuses are the strangers, and serve every day as a reminder that biology is far, far more imaginative than we are. And we are all the richer for that.</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-10488386154651995412015-12-23T11:51:00.000-08:002015-12-23T11:51:31.998-08:0011th genome of Christmas: Us<div dir="ltr" style="text-align: left;" trbidi="on">
Ever since the discovery of DNA as the molecule responsible for genetics, in particular when it became clear that the ordering of the chemical components in this polymer was the information that DNA stored, scientists have dreamt about determining the full sequence of the human genome. For Francis Crick, who co-discovered the structure of DNA (along with James Watson, using data from Rosalind Franklin) this would be the final step towards unifying life and chemistry: demystifying the remarkable process that leads to us and all other living creatures. Back in 1953 this was a fantasy, but slowly and steadily over the ensuing decades it became a reality.<br />
<br />
The first step was developing a routine way to determine the order of the chemicals in the DNA polymer: sequencing. Fred Sanger, a gifted scientist and the only person with two Noble prizes in the same field under his belt, developed dideoxy-sequencing (a.k.a. “Sanger sequencing”) at the LMB in the 1970s. His laboratory, along with neighbourghing LMB labs including Sydney Brenner’s, produced a new generation of scientists: John Sulston, Bart Barrell, Roger Staden and Alan Coulson, who forged ahead towards the seemingly unobtainable goal of sequencing whole organisms – with human in their sights. First, they did the different bacteriophages (see my <a href="http://genomeinformatician.blogspot.com/2015/12/the-genome-days-of-christmas.html">First Genome of Christmas</a>). Then, in the 1980s John Sulston and colleagues started on mapping then sequencing the worm (see the <a href="http://genomeinformatician.blogspot.com/2015/12/elegant-genome-2nd-day-of-christmas.html">Second Genome of Christmas</a>).<br />
<br />
Of course this was not just a UK effort; many US scientists were involved in genomics. A scientist and technology developer , Lee Hood, looked at how to remove the radioactivity that came with Sanger sequencing, and created flourophore based terminators. These were far safer and, importantly, amenable to automation. This led to the ABI company's production of automated sequencers, which featured a scanning laser-based readout. Back in the UK, Alec Jeffreys made a serendipitous discovery: microsatellites – highly variable regions in the human genome that provided easy-to-determine genetic markers. This led to the rise of forensic DNA typing (first done for a criminal case near Alec’s native Leicester to provide evidence in a double murder case). A group of enterprising geneticists in France, led by Jean Weissenbach, used these microsatellites to generate the first genome-wide genetic map, based around Mormon families in Utah, who had kept impeccable family records. Clinician scientists were starting to use genetics actively: the first genetic diseases to be characterised molecularly were a set of haemglobinopathies (blood disorders such as sickle cell anaemia). In these cases, the clinicans were lucky that it was easy to track the protein itself as a genetic marker. A landmark breakthrough, by Francis Collins and colleagues, was the cloning of the gene for cystic fibrosis, using only DNA-based “positional” techniques, without knowing the actual defective protein. This was, at last, a clear, practical application of genomics.<br />
<br />
From 1985 through the first part of the 1990s, all of these technologies and uses of DNA were improving, and it became increasingly clear that it was at least possible to consider sequencing the entire genome. However, this was still more of a sheer cliff than a gentle slope to climb. The human genome has three billion letters, a million-fold larger than bacteriophages and 30 times larger than the worm. If the human genome was going to be tackled, it was going to take a substantial, coordinated effort. Debates raged about the best technologies and approaches, the right time to invest in production vs developing better technology, and who, worldwide, would do what.<br />
<br />
By the mid 90s things had settled down. The step-by-step approach used in the worm was clearly going to succeed, and there was no reason not to see the same approach working in human. The approach of mapping first, then sequencing was also compatible with international coordination, whereby each chromosome could be worked on separately without people treading on each other's toes. There was some jostling about which groups should do which chromosomes (the small ones were claimed first, unsurprisingly), and some grumbling about people reaching beyond their actual capacity, but it was all on track to deliver around 2010.<br />
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Five large centres offered the biggest capacity: </div>
<div>
<ul style="text-align: left;">
<li>The Sanger Centre (now the Sanger Institute), led by John Sulston with Jane Rogers and David Bentley as key scientists, funded by the Wellcome Trust, a UK charity; </li>
<li>US Department of Energy (DOE)-funded groups around the Bay Area in California (now the Joint Genome Institute, JGI), with Rick Myers in the early stages and Eddy Rubin pulling the configuration together;</li>
<li>Three US National Institutes of Health (NIH) centres, with oversight from Francis Collins, director of the NIH's National Human Genome Research Institute: </li>
<li>The Washington University genome center in St Louis, led by Bob Waterston with Richard Wilson and Elaine Mardis as key scientists (this was the Sanger's sister group on the worm as well); </li>
<li>Mathematician-turned-geneticist (and part time entrepreneur), Eric Lander, who formed the Whitehead Genome centre as part of MIT (now the Broad Institute); </li>
<li>An Australian transplanted into Texas, Richard Gibbs, at the Baylor genome centre. </li>
</ul>
</div>
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Two other groups claimed a chromosome in its entirety: Genoscope in France, led by Jean Weissenbach, had its sights on Chromosome 14, and a Japanese-led consortium took on Chromosome 21. </div>
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<br /></div>
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Very often, the genome would be depicted with tiny little flags superimposed, as if it had territories to claim. But happily there was an early landmark agreement, the Bermuda Principles, that stipulated all data would be put into the public domain within 24 hours.</div>
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<br />
For a few years, the Human Genome Project followed a steady rhythm: large-scale physical mapping followed by sequencing. Chromosome 22 was the first to be sequenced, by the Dunham team at the Sanger Centre. I remember poring over the sequence and gene models of this tiny human chromosome and thinking just how big the task ahead of us was. Chromosome 21 was heading to completion, and many other larger chromosomes were slowly being wrangled into shape.<br />
<br />
Then, the sequencing world was turned upside down.<br />
<br />
Craig Venter, a scientist/businessman had been around the academic genomic world for sometime, and realised perhaps better than anyone else the potential impact of automation. He had already published the first whole-genome shotgun bacteria and, inspired by a paper from Gene Myers (a computer scientist working on text analysis, and converting to biology) realised that a similar approach could work on human. Craig assembled an excellent set of scientists - Gene Myers, Granger Sutton and Mark Adams among others - and persuaded leading technology company ABI to set up a new venture to sequence the human genome - privately. This was at the end of the 1990s, at the start of the dotcom boom when it was anyone's guess what a viable business model would be. Certainly, holding a key piece of information for biomedical research 10 years before the public domain effort looked a pretty good bet. Celera was born, raised a substantial amount of money on the US stock market and purchased a massive fleet of sequencers and computers. </div>
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<br />
Naturally, this was quite a shock to the academic project. I remember John Sulston gathering all of the Sanger Centre employees in the auditorium (I was a PhD student at the time) and telling us that this was a good thing - but complex. Behind the scenes there were all manner of discussions, best read about in one of the numerous books that came out. By my own recollection, there was a sneaking respect for Craig's sheer chutzpa, coupled with a massive sense that one simply couldn't have one organisation - and certainly not a company - own this key information. </div>
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I later discovered that the Wellcome Trust, the large UK charity behind the Sanger Centre, took the important step of backing John Sulston to sequence the entire genome if necessary, to ensure it would be put it into the public domain (the US academic components were being asked whether their effort was value for money for the taxpayers). The ability for this charity to "buy in" the genome sequence to the public domain was critical to keeping the genome open (in fact, the US academic projects continued, but it is unclear what would have happened had this stance been taken). More publicly, there were some quite unseemly spats, for example on the feasibility of the whole-genome shotgun approach.<br />
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The academic project also responded to the new, higher-pressure timeline. Rather than keeping with the map-first, sequence second approach, people switched to sequence-and-map as one scheme, but still with mid-size pieces (BACs - around 100,000 letter regions) rather than reads (only 500 letters at a time). This was a half-way point towards whole-genome shotgun and, critically, allowed the five major centres to accelerate their production rate. The nice map with flags across the genome basically disappeared (though each chromosome would then be mapped and finished) and the five centres ploughed onwards, leaving footprints all over the nice, tidy, well-laid plan.<br />
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But this acceleration of rate caused another problem: bottlenecks in the downstream informatics. Celera started to crow a bit about their depth of human talent in computer science and the size of their computer farm. This became a real issue. The public project was facing a very real headache of having thousands of fragments of the genome without any real way to put them together. My supervisor, Richard Durbin, was the lead computational person at Sanger and stepped up along with other academic groups, notably the creative, enthusiastic computer scientist David Haussler in Santa Cruz. David and Richard had worked on and off on all sorts of things, bringing in parts of computer science methods into biology, and they - with us, their groups - began to try and crack this problem.<br />
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The first problem was assembly. Previously, we were guided by a "physical map" and assembly was effectively done by hand on a computer-based workbench. This needed to change. David was joined by ex-computer-gaming programmer Jim Kent, who felt he could do this. I remember discussing the details of assembly methods and concepts on a phone call, with Jim enthusiastically claiming it was doable and everyone agreeing that Jim should come to the Sanger Centre for a while to absorb the details of overlaps, dispersed repeats and other Sanger genome lore. He packed his bags and left that day, appearing 12 hours later in Hinxton: a jovial, very definitely west-coast Amercian, ready to get to work. Jim worked constantly for about six months (back in Santa Cruz) solid to create the "golden path assembler", which provided the sequence for the public projects. Jim also created the UCSC Browser, which remains one of the premier ways to access the human genome (though of course I am partial to a different, leading browser...).<br />
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And it didn't stop there. The public project and the private Celera project were now really swapping insults in public, and Celera said that even if the public project could assemble their genome, they wouldn't be able to find the genes in this sequence. Thankfully, three of us - Michele Clamp, Tim Hubbard and myself - had already started a sort of 'skunk-works' project at Sanger to be able to automatically annotate the genome. The algorithmic core was a program I had written, GeneWise, which was accurate and error-tolerant but insanely computationally expensive. Tim had a (in-retrospect, bonkers) cascading file system to try to match the raw computation with the arrival of data in real time. Michele was the key integrator. She was able to take Tim's raw computes, craft the right approximation (described as "Mini-seq") and pass it into GeneWise. This started to work, and we made a website around it: the Ensembl project, which provided another way to look at the genome. (Mini-seqs and GeneWise still hum away in the middle of Ensembl gene builds, and are responsible for the majority of vertebrate and many other gene sets.)<br />
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Even more surreally for me, the corresponding Celera annotation project was also using GeneWise (I had released it open source, as I would do everything), so I would have a list of bugs and issues from Michele and Ensembl during the day, and then a list of bugs and issues from Mark Yandell and colleagues from Celera overnight. The friendliness and openness of the Celera scientists - Gene, Mark Adams and Mark Yandell - was at complete odds to the increasingly bitter public stance between the two groups.<br />
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It was an intense but fun time. Michele and I worked around the clock to provide a sensible model of the genome and features (using - radically at the time - an SQL backend), and there were constant improvements to how we computed, stored and displayed information. We'd often work all day, flat out, and then head back to Cambridge, often in Michele's house where we'd snatch a quick bite and watch the latest set of compute jobs fan out across the new, shiny compute farm bought to beef up Ensembl's computational muscle. Michele's partner (now husband) James ran the high-end computers, so if anything went wrong, from system through algorithm to integration - one of us was on hand to fix it. As the first jobs came back successfully, we would slowly relax, and eventually reward ourselves with a gin and tonic as we continued to keep one eye on the compute farm.<br />
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Eventually it became clear that both projects were going to get there - pretty much - in a dead heat. Given that the public project's data could be integrated into the private version, Celera switched data production efforts to mouse, much to Gene Myers' annoyance as he wanted to show that he could make a clean, good assembly from a pure whole-genome shotgun. There was a brokering of a joint statement between Celera and the public project, and this led to a live announcement from the White House by Bill Clinton, flanked by Craig Venter (private) and Francis Collins (public), with a TV link to Tony Blair and John Sulston in the UK.<br />
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One figure in this announcement came from our work: the number of human genes in the genome. This is a fun story in itself - I can't do justice to it now - involving wild over-estimation for over two decades followed by extensive soul-searching as the first human chromosomes came out. I ended up running a sweepstake for the number whereby, in effect, we showed that in the absence of good data, even 200 scientists can be completely wrong. For the press release, it was our job to come up with an estimate of the number of human genes, so Michele launched our best-recipe-at-the-time compute. Bugs were found and squashed, and I remember hanging around, providing coffee and chocolate to Michele as needed (there is no point really in trying to debug someone else's code in a pressurised environment). Eventually an estimate popped out: around 26,000 protein-coding genes.<br />
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We looked at each other and shook our heads - clearly too low, we thought, and went into the global phone conference where the good and the great of genomics said "too low" as well. So we went back and calculated all sorts of other ways there could be more protein coding genes (after all, a biotech called Incyte had been selling access to 100,000 human genes for over five years). We ended up with the rather clumsy phrase, "We have strong evidence for around 25,000 protein-coding genes, and there may be up to 35,000."<br />
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In retrospect, Michele and I would have been better sticking to our guns, and going with the data. In fact, we now know there are around 20,000 protein-coding genes (though there are enough complex edge cases not to have a final number, even today).<br />
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The human genome was done in a rush, with enthusiasm, twice, in both cases in such a complex way that no other genome would be done like this again. In fact, Gene Myers was right. Whole-genome shotgun was "pretty good" (though purists would always point out that if you wanted the whole thing, it wouldn't be adequate). The public project, John Sulston above all, was right that this information was for all of humanity, and should not be controlled by any one organisation. </div>
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With all the excitement and personality of the "race" for the human genome, it is easy to forget what the lasting impact was. As with all of genomics, it is not the papers, nor the flourishes of biology or speculation about the future that makes the impact, but two features of this data: the genome is finite, and all biology, however complex, can be indexed to it. This is doesn't mean that knowing the genome somehow provides you with all the biology - quite the opposite is true. It is often the starting point for efforts to unravel biology. But there this was a major phase change in molecular biology, between not knowing the genome sequence and knowing it.<br />
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I was very lucky to be at the right place at the right time to be a part of this game-changing time for human biology. Crazy days.</div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com2tag:blogger.com,1999:blog-1688712549144760104.post-8643684706372835102015-12-22T03:40:00.003-08:002015-12-23T04:00:07.078-08:0010th genome of Christmas: The laboratory mouse<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Verdana, sans-serif;">After human, the most studied animal, by a long margin, is mouse. Or, more strictly, the laboratory mouse, which is a rather curious creation of the last 200 years of breeding and science. </span><br />
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<span style="font-family: Verdana, sans-serif;">Laboratory mice originate mainly from circus mice and pet “fancy” mice kept by </span><span style="font-family: Verdana, sans-serif;">wealthy </span><span style="font-family: Verdana, sans-serif;">American and European ladies in the 18th century. Many of these mice had their roots in Japan and China, where their ancestors would have been kept by rich households. Unsurprisingly, the selection of which mice to breed over the centuries came down to habituation to humans and coat colour rather than scientific principles. </span></div>
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<span style="font-family: Verdana, sans-serif;">The founding genetic material for the lab mouse was not just one species, the European house mouse (<i>Mus musculus domesticus</i>), but three: <i>Mus musculus domesticus</i>, <i>Mus musculus musculus</i> (mainly Asian) and <i>Mus musculus castaneus</i>. Because mice have been following humans around for thousands of years, the history of these three species or strains (everything gets a bit murky here, as mice mate if they meet - but Asia to Europe is quite a distance if you are a mouse) is complex, to say the least.</span></div>
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<span style="font-family: Verdana, sans-serif;"><br />Mice got their start in the genetics laboratory in a rather eccentric collaboration between a Harvard Geneticist (W. E. Castle) and a fancy-mouse breeder (Abbie Lathrop), who provided a series of mice with specific traits, such as Japanese Waltzing mice. Abbie arguably ran the world’s first-ever mouse house on her farm in Massachusetts. A student of Castle, C.C. Little, got involved in studying mice and transformed a small hamlet on the coast of Maine, Bar Harbor, into a research laboratory, later named the “Jackson Laboratory” after a generous donor. The Jackson lab (shortened to “Jax”) is still one of the world’s premier mouse research sites.</span><br />
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<span style="font-family: Verdana, sans-serif;">Mice are excellent mammalian models: they really do have all the cell types, tissues and organs that human has, and so many features (though not all) of human biology, from cellular to physiological, can be replicated and studied in this animal. But it is the detailed control we have over the mouse genome that makes it an exceptional species for helping us understand biology. This control is thanks to two key developments. First, because mouse embryonic stem cells can be produced so easily, there are mouse cells (which you can keep in a petri dish) that can be coaxed into making viable embryos. These embryos can be implanted in pseudopregnant mice, and become full grown individuals. Second, one can swap pieces of DNA </span><span style="font-family: Verdana, sans-serif;">in and out</span><span style="font-family: Verdana, sans-serif;"> </span><span style="font-family: Verdana, sans-serif;">in these stem cell lines at will - almost as easily as in yeast (and certainly more easily than in fly or worm). </span></div>
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<span style="font-family: Verdana, sans-serif;">The ability to swap, not just insert, DNA segments </span><span style="font-family: Verdana, sans-serif;">(“homologous recombination”) </span><span style="font-family: Verdana, sans-serif;">is key. </span><span style="font-family: Verdana, sans-serif;">This unique-in-animals genomic control of genetics means there are elegant, precise experiments that are only feasible in mouse. For example, one can 'humanise' specific genes (i.e. swap the human copy in for the mouse copy), or trigger the deletion of a gene at a particular developmental time-point by using a variety control elements, ending up with molecular 'cutters' that will turn on only when you want them to. Mice are far more than just a 'good' model for human - they are arguably the premier multi-cellular organism over which we have the most experimental control. </span><br />
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<span style="font-family: Verdana, sans-serif;">Given its importance to a massive community of researchers, mouse was clearly going to be the most important genome to sequence, after human.</span></div>
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<span style="font-family: Verdana, sans-serif;">The Black6 strain (Full name: C57BL/6) from the original breeding of C.C. Little was chosen as the strain to sequence, because it was the most inbred and the one most often used in experiments. Indeed, in the public/private race to the human genome (more on this in a later post), the company Celera switched to sequencing mouse when it was clear that the public human genome project was matching the Celera production rate. </span></div>
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<span style="font-family: Verdana, sans-serif;">Both the Celera mouse data and the public mouse genome data were based on a whole-genome shotgun sequencing approach. This was standard fare for Celera, but signalled the start of whole-genome shotgun sequencing for 'big' genomes academically (at least for 'reasonable' draft genomes). The inbred nature of mice, Black 6 in particular, simplifies the assembly problem for whole genome shotgun. It’s bad enough trying to put together a </span><span style="font-family: Verdana, sans-serif;">3 billion-letter-long </span><span style="font-family: Verdana, sans-serif;">genome from </span><span style="font-family: Verdana, sans-serif;">500 letter fragments - </span><span style="font-family: Verdana, sans-serif;">it’s even worse when you have two near-but-not-quite-identical 3 billion-letter-long genomes to reconstruct. </span></div>
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<span style="font-family: Verdana, sans-serif;">But in many ways, the mouse genome brought us into a new era of genome sequencing: one of routine, 'pretty good' drafts from whole-genome shotgun, with fairly routine automated annotation. This was in stark contrast to the step-by-step approach taken with previous genomes, coupled with a more involved, manual annotation. </span></div>
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<span style="font-family: Verdana, sans-serif;">Given the importance of mouse to researchers, both the genome and the annotation have been regularly upgraded. Though they had broken the back of the big-genome quandary, like many problems, the last 10% of the work, sorting things out, has turned out to be as annoying and involved as the first 90% of the job. After the first draft mouse genome, the next five years was about nailing down the frustrating ~10% of the genome that wasn't easy to assemble from shotgun, and attending to all the details.</span><br />
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<span style="font-family: Verdana, sans-serif;">Mouse is also likely to lead us in future to a more graph-based view of reference genomes. As there are inbred lines of mice, one can really talk about "individual" genomes in a solid way, knowing that others can 'order up' the same strain and work on them. Thomas Keane and colleagues have been building out the set of mouse strains beyond Black6, and doing increasingly independent assemblies, strain by strain. The resulting set of individual sequences absolutely shows the complex origin of laboratory mice; at any point, some mouse strains are as divergent as two species, and some are more like two individuals from a population. This complex web is best represented as a graph of sequences, rather than a set of edits from one reference, which is the current mode. </span><br />
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<span style="font-family: Verdana, sans-serif;">In 1787 Chobei Zenya (from Kyoto) wrote a book, "The Breeding of Curious Varieties of the Mouse", which apparently had "recipes" for making particular coat colours for breeding strategies. There are far earlier documents from China on mouse strains, including the "waltzing" mouse (which we now know is a neurological condition). In some sense this is both the rootstock of this laboratory species and part of the motivation for and discovery of evolution and genetics (though Darwin spent more time looking at pigeons than mice). </span><br />
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<span style="font-family: Verdana, sans-serif;">Given the laboratory mouse's flexible genetic manipulation, we will studying this species for at another 200 years.</span><br />
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-83221883793456116392015-12-21T11:17:00.002-08:002015-12-21T11:17:25.735-08:009th genome of Christmas: Medaka and friends<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Verdana, sans-serif;">My ninth genome of Christmas is a bit of an indulgence: the gentlemanly, diminutive Medaka fish, or Japanese rice paddy fish. <br /><br />When Mendel’s laws were rediscovered in the 1900s, many scientists turned to local species they could keep easily to explore this brave, new world of genetics. In America, Thomas Hunt chose the fruit fly. Scientists in Germany explored the guppy and Ginuea pigs. In England, crop plants were the focus of early genetics. In Japan, researchers turned to the tiny Medaka fish, a common addition to many of the ornamental ponds maintained in Japanese gardens. </span><br />
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<span style="font-family: Verdana, sans-serif;">Medaka fish are regular tenants of rice paddies and streams all through east Asia, from Shanghai through the Korean peninsula and the islands of Japan, with the exception of the very northern set of islands in Japanese archipelago. (Naturally, every country has a different name for this fish, but it is most widely used for study in Japan so I am using the Japanese terms.) Fishing for Medaka is as common for Japanese children as fishing for guppies or fry is for European children, and is widely depicted in </span><span style="font-family: Verdana, sans-serif;">19th century </span><span style="font-family: Verdana, sans-serif;">Japanese wood blocks.</span></div>
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<span style="font-family: Verdana, sans-serif;"><br />Medaka also has the honour of being the first organism to show us that cross-over on the sex chromosomes does occur. We now know this to be commonplace, but at the time of its discovery this was a novel observation.<br /><br />As genetics developed, Japanese researchers continued to inbreed Medaka fish, creating one of the most diverse set of inbred individual invertebrates from a single species in the world. Being fish, they have all the cell types and nearly all the organs that a mammal has: tiny, two-chambered hearts, livers, kidneys, muscles, brains, bones and eyes. Conveniently, one can keep lots and lots of them, far more cheaply than mice, and they reproduce regularly, with a generation time of around three months.<br /><br />But then a different fish rose to prominence in molecular biology in the 1980s. Zebrafish, native of the Ganges, was chosen by the influential Christiane Nusslein-Volhard as the basis for redoing her Nobel-Prize-winning forward genetic screens in <i>Drosophila</i>, this time in a vertebrate. </span></div>
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<span style="font-family: Verdana, sans-serif;">I’ve not yet asked Christiane whether she ever thought about using Medaka rather than Zebrafish, but I am sure that a couple of details to husbandry made Zebrafish very attractive: it lays 1000 eggs at a time, providing for excellent single-female progeny, and is transparent during its embryonic stage, allowing for easy light microscopy of the developing fish. In contrast, Medaka lay only around 30 eggs, and they stick to the female rather than being spurted out, so harvesting them is somewhat complex. Plus, the eggs have an opaque glycoprotein layer, which skilled scientists can remove but again makes it harder to study the embryo</span><br />
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<span style="font-family: Verdana, sans-serif;">So why am I so interested in Medaka? Well, I was having a beer with my colleague Jochen Wittbrodt, who is one of the rare Medaka specialists outside of Japan, and we were discussing the next stage of experiments. Medaka fish has a neat trick by which one can introduce foreign DNA (e.g. human) coupled to a reporter (green fluorescent protein from jellyfish is a favourite - easy to pick up using a microscope). Even on the first injection, the foreign DNA will often go into every cell. For most other species, you have to get lucky for the foreign DNA to go the germline, and then hope it will breed true. Jochen had done a number of successful reporter experiments based on designs from my group, and we were discussing whether we could draw on the long history of Medaka research with its rich tapestry of inbred lines to explore the impact of natural variation on these reporter experiments. So, I asked him how many inbred Medaka lines there were, and Jochen nonchalantly replied that he had no idea - after all, his colleague, Kiyoshi Naruse, made one or two new lines from the wild every year or so.<br /><br />My jaw hit the floor. From the wild? I checked. Jochen confirmed. And then I explored some more, and discovered that there was a whole protocol for creating inbred individual Medaka from the wild.<br /><br />This might sound trivial, but it is not. Keeping vertebrates in a laboratory is hard. Keeping them in a laboratory when they are inbred, such that their diploid genome is identical everywhere, is extremely difficult. Doing this routinely from the wild is basically unheard of (although this </span><span style="font-family: Verdana, sans-serif;">“self’ing” </span><span style="font-family: Verdana, sans-serif;">happens all the time in plant genetics). </span></div>
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<span style="font-family: Verdana, sans-serif;">Standard theory holds that every individual, whatever the species, has a number of recessive lethal alleles, which will kill the animal </span><span style="font-family: Verdana, sans-serif;">if you make them the same</span><span style="font-family: Verdana, sans-serif;">. The trick to making an inbred line that is truly the same everywhere (i.e. homozygous) is regular brother-sister mating and an awful lot of patience, as at some point you have to find the combination of alleles in an individual that does not have a lethal effect. Normal animal husbandry lore would have it that this was such hard work, particular with wild individuals, that it would be best to just continue propagating the hard work carried out by the original founders of whichever organism you are using.</span></div>
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<span style="font-family: Verdana, sans-serif;">Now, this theory does not hold true for plants, and plant geneticists have enjoyed making inbred lines from the earliest days. And Trudy Mackay, looking at the tricks you can play, created a set of inbreds from wild <i>Drosophila</i> lines. One can </span><span style="font-family: Verdana, sans-serif;">study developmental changes by </span><span style="font-family: Verdana, sans-serif;">looking at different individuals from the same genetic line</span><span style="font-family: Verdana, sans-serif;">, but it has to be at different times. One can study the interaction of genes and environment by raising genetically identical individuals in different environments, but it must be done across a panel of strains that represent a wild population. The model plant <i>Arabidopsis</i> has been used by geneticists to do this for decades; fly geneticists are just starting to. </span></div>
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<span style="font-family: Verdana, sans-serif;">This kind of work would have been considered madness in vertebrates. You can’t even keep one or two laboratory zebrafish lines fully inbred - you often need to add back a bit of diversity. There are established inbred laboratory mice, but from a weird multi-species hybrid. Single, wild-derived mice strains have been established, but not at scale - not least because of the complications inherent to keeping mouse facilities pathogen-free, which makes everyone a bit paranoid about wild mice in a laboratory setting. </span></div>
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<span style="font-family: Verdana, sans-serif;"><br />But in Medaka, it could be doable. Impressive.<br /><br />Jochen introduced me to Felix Loosli, the best Medaka breeder outside of Japan, and Kiyoshi Naruse, one of the leading breeders in Japan. The four of us have undertaken to generate and characterise a Medaka inbred panel from a single wild population (unsurprisingly, very close to Kiyoshi’s lab, in Nagoya). </span></div>
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<span style="font-family: Verdana, sans-serif;">The Medaka genome has of course been sequenced, in a relatively standard, somewhat quirky way by a Japanese group. This genome is a pretty standard fish genome, around the a third the size of human. Medaka are close to some other </span><span style="font-family: Verdana, sans-serif;">evolutionarily</span><span style="font-family: Verdana, sans-serif;"> interesting fish: the stickleback, beloved of ecologists thanks to the numerous species that form in different river and lake systems; cichlids, with a similarly diverse set of species living around the African lakes and Fugu (and loved by sushi gourmands because of the powerful neurotoxin which, so long as it is only in trace amounts, produces an intriguing taste), and loved by genomicists as the vertebrate with the smallest genome. </span></div>
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<span style="font-family: Verdana, sans-serif;">Together, these four funky fish will, I hope, push forward research into vertebrate genetics with evolution, ecology, and environment. Our own contribution is in creating the first ever inbred-from-the-wild panel in vertebrates.<br /><br />Watch this space.</span></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-27286154316332507872015-12-20T10:35:00.000-08:002015-12-28T11:35:33.868-08:008th genome of Christmas: the greatest chemists in the world. <div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Verdana, sans-serif;">You might think that the best chemists on earth are humans, living perhaps in Cambridge, Heidelberg, Paris, Tokyo or Shenzhen, beavering away in laboratories filled with glassware, extraction hoods and other human-made things. But then you would be discounting a multitude of bacteria that have cracked all sorts of chemistry problems over the course of their long evolution, and that still harbour secrets about how they manipulate molecules. One inventive clade of bacteria, the cyanobacteria, quite literally changed the world, and built the foundations of modern life.<br /><br />Some 2.5 billion years ago, the ancestor of present-day cyanobacteria made a radical chemical innovation to improve the way they supplied the electrons that feed through various photosynthetic systems. Rather than drawing on more exotic sources of electrons, they used the ubiquitous water molecule. Stripping out the electrons and hydrogens from water could release molecular oxygen: a powerful, reactive molecule, which of course drifted away as a gas. For the first 200 million years or so after this innovation, this gas reacted with reduced inorganic things, for example iron deposits. We can see the resulting change in earth's oxidation state today by drilling down through sediments. But eventually all those sinks were used up, and oxygen started to accumulate in the atmosphere.<br /><br />This was a massive change to our planet. Molecular oxygen (O2) is thermodynamically unstable; the vast majority of the time it wants to form molecules with other atoms (though the kinetics of these processes gave some opportunities). As oxygen built up in the atmosphere, pumped out by cyanobacteria, every other living organism had to either adapt to cope with (and often exploit) this radical new oxidising agent, or hide itself away in any anaerobic place it could find, which was usually deep inside the Earth. There was no middle ground.<br /><br />Most life forms adapted. Indeed, they exploited the presence of this oxygen, particularly when it let them control the oxidation of other molecules (such as carbon) to capture energy. Cyanobacteria brought about the source of energy for most living organisms, by enabling carbon capture in combination with various creative uses of oxygen. <br /><br /> The cyanobacteria themselves had to adapt. It’s quite possible, too, that this oxygen crisis triggered some of the most successful collaborations on the planet: alphaproteobacteria worked out how to use oxygen productively, only to be engulfed by the bigger, more motile archaea-like proto-eukaryotes, emerging as mitochondria. Then, these eukaryotes joined forces with ancestors of cyanobacteria to form algae and plants, with the ancestral cyanobacteria becoming the chloroplast, which collects light energy and fixes CO2 for growth. <br /><br /> There is pretty much nothing in our current world, from the diversity of life through the energy we use every day, that is not dependent on cyanobacteria's great innovation.<br /><br />This is just one of many chemical innovations brought to us by bacteria. Billions of years before Fritz Haber worked out how to capture gaseous molecular nitrogen and convert it into the very useful ammonia, bacteria had worked out how to crack into the kinetically resistant N2 gas. Interestingly, even after intense, concerted efforts we still don’t understand how bacteria pull this off at room temperature. (Many scientists are still at work to crack this; we know the genes involved and have some sense of the awesome redox potentially needed, but how it actually works is still a mystery.) Some bacteria produce hydrogen, which is consumed by other bacteria; some bacteria eke energy out of the redox shifts between the oxidation of metals - everything from iron through to uranium. Bacteria can live in the weirdest environments, from the “hot smokers” of volcanoes underground to the clouds drifting above us. <br /><br /> Bacteria are usually pretty efficient organisms. They live life close to the margin, and every carbon they don't spend on growth is considered a carbon wasted. They have far smaller genomes than the sloppy, energy-rich eukaryotes - and these days it is almost a trivial task to sequence bacterial genomes.But the challenge is neither the size nor the complexity of each genome, but rather simple incredible diversity of bacteria. They are everywhere, finding any possible option for growth. The first bacteria sequenced for the purpose of understanding its chemistry (rather than its laboratory behaviour, or to target it as an infectious agent against humans) was probably <i>Synechocystis</i> in 1997 by a Japanese group. But so many more have sequenced: - over 10,000 - that it is impossible even for the naming systems to keep up. </span><br />
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<span style="font-family: Verdana, sans-serif;">Bacterial genomes don't magically tell us how they perform such innovative chemistry, but they do give us the building blocks of the proteins involved, and allow us to start to study them - and sometimes use them - separately. And we have only really started to explore bacterial diversity.<br /> <br /> We often consider ourselves and our mammalian cousins as the apogee of evolution, but really the greatest success stories on this planet belong to bacteria, which have radically changed the world.<br /></span> </div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-79474086344220352132015-12-19T06:29:00.000-08:002015-12-19T06:29:41.060-08:007th genome of Christmas: Bread, Beer and Wine.<div dir="ltr" style="text-align: left;" trbidi="on">
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">When you first think of domesticated organisms, dogs might come to mind (our earliest domestication), or perhaps wheat, or cattle or rice. But you might easily overlook single-celled yeast: the key active agreement in both bread and alcohol, and a great enabler of the agricultural revolution in Europe. </span></span><br />
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">Wild yeast lives on fruit and seeds, and is dispersed by the wind. The earliest use of these wild organisms involved capturing them to make alcohol (wine) and to make sourdough bread rise. For the routine production of beer and bread, brewers and bakers kept cultures of 'good' yeast, eventually selecting for specific strains of <i>Saccharomyces cerevisiae</i>: a single-celled fungus that can live both in aerobic (oxygen present) and anaerobic (no oxygen) conditions.</span></span></div>
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">As genetics and molecular biology took shape, researchers fell in love with this miniature fungus. It is a eukaryote, with a nucleus, signalling pathways, cell division and other conserved features. From the laboratory husbandry point of view, it is closer to bacteria: you grow it on media plates, its commonest life cycle stage is haploid (one copy of the genome) rather than the more commonplace diploid. Despite its mainstay 'growth' haploid mode, yeast also has a sex life (becoming diploid), which you can manipulate and use for genetics. </span></span><br />
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">After <i>E. coli</i>, it probably has the most manipulable DNA, letting you swap in or out any piece of DNA (you can even insert entire chunks of DNA from other species if you want to, making “YAC”, Yeast Artificial Chromosomes).</span></span></div>
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">So many basic molecular discoveries have their origins in yeast that it is impossible to list them all. Everything from understanding the cell cycle (though a separate African brewer’s yeast, <i>S. pombe</i>, took a star turn as well), through mapping intracellular signalling pathways, to laying down the fundamental aspects of transcription (making RNA from DNA) and translation (making proteins from RNA). Each discovery shows in some way that the vast majority of the cellular machinery one can study in yeast is pretty much at work - sometimes gene-for-gene - in each and every one of our own cells. </span></span></div>
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">So it's not surprising that yeast was an early target for genome sequencing. This life form was sequenced by a consortium of individual labs all over the world, using the early, more manual technologies. There was some automation and factory-like sequencing, but a lot of the work was done Old School: individual postdocs and technicians pouring gels and reading off each piece of DNA in a </span></span><span style="font-family: "verdana" , sans-serif; line-height: 22.08px;">bespoke fashion. This was much in the tradition of</span><span style="font-family: "verdana" , sans-serif; line-height: 1.38;"> crafting brewers-yeast-based beers, and as such can be considered an artisanal genome sequence. In 1996 it was published: the first eukaryotic genome.</span></div>
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<span class="s1"><span style="font-family: "verdana" , sans-serif;">Yeast is one of the most engineered of all species. One can order up any gene knockout, or a collection of all yeast genes knocked out, with barcodes. One can have any protein tagged, and use that tag for cellular imaging or mass spectroscopy. Huge systematic crosses, direct evolution or growing in controlled environments are feasible on a robotic scale with yeast - and the genome sequence provides a pivotal part of the infrastructure for this kind of work.</span></span><br />
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<span class="s1"><span style="font-family: "verdana" , sans-serif;"><span style="line-height: 22.08px;">So raise a glass to yeast! Its genome sequence</span><span style="line-height: 22.08px;">, coupled with its amazing genome engineering and ease of growth, has placed it firmly in the premier spot as an organism for both basic cellular biology and biotechnology. </span></span></span></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-12032797428834394312015-12-18T05:28:00.000-08:002015-12-18T06:29:41.578-08:006th genome of Christmas: the deadly Plasmodium... plant?<div dir="ltr" style="text-align: left;" trbidi="on">
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<div style="line-height: 1.38;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">If humans have an arch enemy, it might well be the tiny, blood-borne parasite </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium falciparum</i></span><span style="font-family: "arial"; font-size: 14.666666666666666px; white-space: pre-wrap;">. This nasty beast causes most of the malaria in sub-Saharan Africa and, together with its cousins, in many tropical zones throughout the world</span><span style="font-family: "arial"; font-size: 14.666666666666666px; white-space: pre-wrap;">. It kills huge numbers of children every year, and constantly cycles through the bloodstreams of its many survivors. It has been with us since our explosive migration out of east Africa, and in fact many genetic diseases (including sickle-cell aneamia and thalassemias) are </span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;">tolerated by human populations because they</span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;"> confer an advantage against this nasty parasite.</span></div>
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<b id="docs-internal-guid-3f59f1d7-b46d-ca4e-9434-da852fe940a5" style="font-weight: normal;"><br /></b>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">This intimate, long-standing, dysfunctional relationship makes it all the more weird that </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium falciparum</i> is, in part, ancient, degenerate algae.</span></div>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Genomics made it possible to untangle this story. As people honed in on the DNA of the </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> parasite, they noticed that the genome was very biased: there were far more A+T than G+C pairings. (The base-pairing rule says there must be the same </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>amount</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> of A+T and G+C because of the double-stranded nature of DNA, but the </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>ratio</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> of A+T to G+C can be different.) This bias caused all sorts of issues, but there was one bit of DNA that looked very different. </span><br />
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><br /></span>
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">In the 1970s and 80s, people thought this must be the mitochondrial DNA of the parasite. (Mitochondria, the power plants of cells, have their own tiny genome, a remnant of the ancient merging of their ancestors as free-living bacteria with eukaryotic cells. </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">, being a eukaryote, must have mitochondria.) But </span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;">PCR experiments on classic mitochondrial conserved regions did not turn up anything to support this hypothesis.</span></div>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
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<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">In the mid 1990s this "anom</span><span style="background-color: white;"><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">alous" p</span><span style="background-color: white;"><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">art of the </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> genome </span></span></span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;"><span style="background-color: white;"><span style="background-color: white;"> </span>was cloned by a g</span>roup from NIMR, the MRC institute in north London now merging to become part of the Crick. A real surprise was that this was not a mitochondrial genome at all - it was a plastid genome, that is to say, the photosynthetic organelle found in all plants and algae (look for the plastid in another Christmas-genome post). </span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;">The chloroplast was also free-living bacteria before symbiosing with eukaryotes to give rise to plants and algae as we now know them. Furthermore, the whole set of parasites had this </span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;">degenerate plastid (“apicoplast”)</span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;">, and so were promptly renamed “apicomplexans”.</span></div>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Quite why a presumably free-living-algae-related organism decided to chuck in a photosynthetic, light-powered life to become one of the world’s deadliest parasites to many species, one can only speculate. </span><br />
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><br /></span>
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">The apicoplast seems to be important in the parasitic life cycle. One might imagine an organelle specialised for light gathering and carbon fixation might seem pretty superfluous for an endo-parasite (but apparently it's not). This does clear up why a number of anti-malarials, such as Quinine, also act as herbicides; their anti-plastid action hurts both plants and malaria (but of course not all herbicides can be anti-malarial drugs). </span></div>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
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<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Given the importance of </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">, sequencing and assembling the full genome was a priority. However, the traditional step-by-step approach, by which individual pieces of genome were cloned </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">within</span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>E. coli,</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> did not work. </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>E. coli</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> spat out the A+T rich DNA most of the time, or (even worse) chopped it up and rearranged it. </span></div>
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<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><br /></span></div>
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<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">So Bart Barrell and colleagues at the Sanger took to chromosomal sorting and whole-genome shotgun sequencing to sort out the </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium</i><i style="font-style: normal;"> </i>genome</span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> - another epic undertaking for its time. With its extreme A+T richness, this genome was a weird beast. You could almost predict coding-sequence regions by eye, as there had to be more G+C to support the amino acids that were clearly present. </span><span class="Apple-style-span" style="font-family: "arial"; font-size: 15px; line-height: 20px; white-space: pre-wrap;">Furthermore, the ends of chromosomes ('sub-telomeric regions', in the jargon) were freak<span style="background-color: white;">ishly similar, and full of a sophisticated molecular “chaff” (called 'Rifins') that sit on the outside of the parasite as any ever-changing coating,</span><span style="background-color: white;"><span style="background-color: white;"> to </span><span style="background-color: white;">confuse the host immune system and prevent an effective response from the host. </span></span></span></div>
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<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><br /></span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">The community is continuing to sequence other species of </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodium</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">, most notably in light of its specialisation to a specific host (i.e., us) - other </span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><i>Plasmodia</i></span><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 14.666666666666666px; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> are less fussy about their host (far broader choice of mammals will do for most parasites), but also less deadly. Furthermore, understanding variation in this parasite, in particular those variations that affect drug resistance, is a monumental, on-going effort at the heart of our struggle to defeat this malicious plant.</span></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com2tag:blogger.com,1999:blog-1688712549144760104.post-34185811564587930602015-12-17T06:24:00.003-08:002015-12-17T07:02:56.557-08:005th genome of Christmas: The Fly<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: "verdana" , sans-serif;">The humble
fruit fly – <i>Drosophila melanogaster,</i> to be specific – has played a central role in the
history of genetics and molecular biology and continues to be important in research.
Championed by the legendary Thomas Morgan at the start of the 20<sup>th</sup>
Century, <i>Drosophila</i> provided a practical foundation for genetics – long before the discovery of DNA as vehicle for passing down heritable information through generations. Morgan and colleagues developed the concepts of 'gene'
and 'linkage', and so we have 'Morgans' (and more commonly, centi-Morgans, cM) as the basic units of genetic maps.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">You could argue
that even the modern approach to genetics and molecular biology
research was formed around this creature. The fly has influenced the way laboratories choose a direction
of study and the way they share materials and data internationally,
which was as critical to the success of early genetics as it is now.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">After this strong start, <i>Drosophila</i> kept its momentum during the discovery of DNA, molecular
biology and early DNA cloning. Performing large-scale, 'forward genetic' screens, where (one hopes) every possible gene has been knocked out at least once so one can look for
specific phenotypes, has unearthed a rich seam of genes involved in development.
These days the innovation continues with, amazingly, fly-brain manipulation at a neuronal level.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">You can see
the footprints of <i>Drosophila</i> research everywhere. The playful <i>Drosophila</i> naming scheme allows for gene names such as “tinman” (mutant flies that don’t have a
heart), “dunce” (unable to navigate simple fruit-fly mazes), and “<span lang="EN-US">Antennapedia</span>”
(antennae are swapped for legs), which permeate biology. The human gene
“Sonic Hedgehog” is named after its “hedgehog” ortholog in fly. The “polycomb” in
“polycomb repressive complex” (one of the key genome-switching mechanisms) comes
from the subtle mutation that adds more bristles (i.e., a comb) onto the fruit-fly's back. Fly molecular biologists are part of a long and great tradition, and are understandably proud of their community’s impact and continuing influence.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">This explains a bit why fly genomicists were feeling a bit frustrated in the late 90s, when it became clear that the worm – usually a bit of a 'junior partner' in the metazoan model-organism world – was going to have its genome completed well before the fly. The fly
genome project had done quite a bit of groundwork: a century of research
had produced excellent genetic maps, helped by a clever trick involving the
salivary gland chromosomes (which, bizarrely, duplicate so much that you can see
them easily under a microscope). But the project had not
committed to the same step-by-step sequencing efforts that the worm community had.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">And then
came a golden opportunity.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">Craig
Venter had aligned both investors and technologists to “overtake” the public
human genome project with a privately funded project led by the company Celera.
To do so, he had assembled a group of scientists including the brilliant computer scientist Gene Myers, who claimed the piecemeal approach taken
by the worm and human projects was not necessary. Instead, he posited that a whole-genome
shotgun approach was computationally feasible (more on this in another 'Christmas genome' post).
Many people didn’t believe him. Others who might have given him the benefit of the doubt found it to be too risky a strategy. Craig and team were ready to bet on it.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">But they
needed a test project - a genome that was not as big human, but complex and
worth doing.</span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">So the Great
and the Good of <i>Drosophila,</i> notably Gerry Rubin and Michael Ashburner, pitched the fly to Celera. In 1998/1999, its genome was 'shotgunned' and the genome became the first large, whole-genome
shotgun assembly – published in 2000. </span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span><span style="font-family: "verdana" , sans-serif;">Although shotgun
assembly and automatic (computational) annotation are now commonplace, at the
time this was radical stuff. There was talk of the largest computational farm
ever assembled for biology at Celera, of this whole upstart world of
bioinformatics and computational biology being poised to revolutionise biology. This
was the dot-com era, so at the same time people were talking about new business
models, and how the internet was changing everything.</span><span style="font-family: "verdana" , sans-serif;"><br /></span><br />
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<span style="font-family: "verdana" , sans-serif;">The <i>Drosophila</i> genome work was happening when I was just ending my PhD. I went to Celera for the <i>Drosophila</i> genome jamboree, and GeneWise - my insanely computationally expensive software for error-tolerant protein or protein HMM alignment - was run across the genome. I also met and chatted with Gene for a while, which was my first exposure to the guts of the assembly problem. But perhaps most of all I realised that the geeks were definitely at the top table - designing and creating the experiments, not just processing the data.</span><br />
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themarytoddhttp://www.blogger.com/profile/01781413043369448707noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-54678015138893221002015-12-16T02:01:00.000-08:002015-12-16T08:07:36.793-08:004th genome of Christmas: the hexaploid bread wheat genome. <div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: Verdana, sans-serif;">The first technological innovation to radically change human
society was agriculture. The ability to cultivate – rather than hunt or pick –
food had a profound change on everything from our immune system to our societal
structures. It encouraged specialisation, favoured robust, complex
inter-generational knowledge transmission and enabled the explosive growth of
this bipedal ape.<o:p></o:p></span></div>
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<span style="font-family: Verdana, sans-serif;"><br /></span></div>
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<span style="font-family: Verdana, sans-serif;">Arguably, the centrepiece of agricultural innovation is
wheat. If you look at the ancestral grass from which it was bred, wheat looks
just like… grass. With tiny seeds sticking out of its head at harvest time. Some
10,000 years ago in Anatolia, enterprising farmers bred the biggest, most
consistent of these grasses year after year. By selecting for the size of the
wheat ears, they brought about changes in the genome that gave rise to larger
and larger wheat. One type of change was duplication, by which two individuals (often different subspecies) were
bred together <i style="mso-bidi-font-style: normal;">without</i> first splitting
their genomes in half. In ancient times, single duplications like this gave
rise to varieties like emmer, or durum wheat, and the first duplication looks like a wild, common place process. More recently, a second merger with a third subspecies was introduced in bread wheat in more "modern" times (around 5000 years ago), making its genome three times the size of the
basic grass genome. (In case you’re new to genomics, the genome comes in
pairs: one maternal and one paternal, so this three-way increase is described
as <i style="mso-bidi-font-style: normal;">hexa</i>ploid, three times the normal <i style="mso-bidi-font-style: normal;">di</i>ploid.)<o:p></o:p></span></div>
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<span style="font-family: Verdana, sans-serif;">The basic Anatolian grass genome is about the twice size as
the human genome: around 6 billion bases (6 Gigabases), and the 3-fold hexaploid wheat is around 16 Gb. Annoyingly, every bit of DNA in
wheat has three pretty similar copies, even when the strain is completely
inbred (for outbred wheat plants, one expects 6 loci, 2 from each triplicated loci). In technical terms, this is described as a “nightmare” for genome
assembly and analysis. For a long time, the wheat genome was a seemingly unobtainable
goal for agricultural genomics research.<o:p></o:p></span></div>
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<span style="font-family: Verdana, sans-serif;"><br /></span></div>
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<span style="font-family: Verdana, sans-serif;">It’s not uncommon for plants to duplicate their genome in
the wild (indeed, this seems like the starting point of the ancient wheat), but it’s a regular practice in agriculture when selecting for larger
fruits/seeds. Strawberries are octoploid (four duplicates). Brassicas (cabbages,
broccoli, cauliflower and friends) are all tetraploid strains from different
mixtures and tweaks of three different base lines genomes. It makes my head
hurt just thinking about the genetics. Commercial sugar cane is duodecaploid (6
duplications) and, as we propagate it using cuttings, even its cells have completely
lost the desire to even keep track of their chromosomes.<o:p></o:p></span></div>
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<span style="font-family: Verdana, sans-serif;"><br /></span></div>
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<span style="font-family: Verdana, sans-serif;">Despite its fiendish complexity, the community has finally,
slowly and steadily, tamed the big, bad bread-wheat genome. First-off-the-mark
survey skims of the wheat genome were generated, then compared against the
smaller <i style="mso-bidi-font-style: normal;">Brachypodium</i> grass genome.
The Barley genome (far saner but still annoyingly big) followed. Heroically slogging
through chromosomal sorting, people started to tease apart the specific
components of the genome. And just recently, excellent work by Matt Clarke and
colleagues at TGAC brought us a solid, draft assembly using a clean sequencing
protocol, a custom-tweaked assembly algorithm for wheat and a <i style="mso-bidi-font-style: normal;">very</i> large computer. <o:p></o:p></span></div>
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<span style="font-family: Verdana, sans-serif;">I am delighted that this has happened for many reasons. First,
the work is a <i style="mso-bidi-font-style: normal;">tour-de-force</i> by Matt
and his team. Second, I know a good draft genome will unleash a whole series of
experiments – from diversity panels to chip-seq. Thirdly, it allows wheat to
come into the fold of species-we-have-a-reasonable-genome for, so we don’t need
to treat it like a special case any longer with tricky, bespoke systems (though
there is still a need for these, given wheat’s endless annoyances - for example, it is very important to know the relationship between the 3 copies). <o:p></o:p></span></div>
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<span style="font-family: Verdana, sans-serif; font-size: 12.0pt; mso-ansi-language: EN-GB; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: "MS 明朝"; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin;">This draft bread-wheat genome is just a step
along the way to a very high-quality wheat resource. After all, wheat is far
too important to the health of people on this planet to skimp on quality. But
it is a great step forward, and I hope will be a transformative one in our long
and fine tradition of innovating – starting with this very first piece of
technology, agriculture.</span><!--EndFragment-->
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com1tag:blogger.com,1999:blog-1688712549144760104.post-65996611547744037062015-12-15T09:53:00.002-08:002015-12-16T08:11:47.489-08:003rd genome of Christmas: the Denisovan little finger. <div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">In the early 90s Svante Paabo, a charismatic, energetic
innovator, made a bold proposal: that to study human origins one would do well
to sequence the DNA of ancient hominids, in particular those species which had
gone extinct. After all, DNA could be detected in their bones, provided they
were not too old and kept dry and cold. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">At first this seemed like science fiction, as people were
just starting to get their heads around the sequencing of an entire human
genome. But even in the 1990s it was in fact feasible, if technically
challenging. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Svante and his team started with mitochondrial DNA from
Neanderthal bones. (If you’re new to genomics, mitochondria are the power
plants of the cell and have their own, tiny genome, inherited only from the
maternal line.) Along the way, they discovered (much to their chagrin) all the myriad
ways one could contaminate a DNA sequencing experiment with trace amounts of
modern human DNA (there are many). They also figured out how to prevent them,
for example by keeping bones cold and ‘dirty’ before extraction in the clean
room.<span style="mso-spacerun: yes;"> </span><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">The mitochondrial genome they published in the late 90s confirmed that all human mitochondrial diversity lies outside of Neanderthal
mitochondria – in other words, the out-of-Africa hypothesis of human origins
was correct.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">The next-generation sequencing revolution of the mid-2000s
spurred Svante and his team to push for the next level: sequencing the
Neanderthal genome (not just the mitochondrial one). But the ‘main’ genome, unlike
the tiny mitochondrial one, couldn’t be amplified simply using a PCR reaction. It
was far, far too big for that. Direct (random) shotgun sequencing of ancient
bone will catch everything in the bone sample, which includes a lot of dead
bacteria. They screened many, many bones, and finally found one in which 2% of
the DNA sequence was hominid. That might not sound like much, but it’s enough. The
Neanderthal genome team sequenced around one-fold coverage<span class="MsoCommentReference"><span style="font-size: 9.0pt;"><span style="mso-special-character: comment;"> </span></span></span>
of this ancient hominid (usually you need >10-fold coverage (redundancy) to get an accurate genome; one-fold coverage tells you a lot, but one has to accept gaps and errors).<o:p></o:p></span></div>
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">But there was a huge surprise: a consistent statistical
trend put the main Neanderthal genome slightly closer to European and Asian human
genomes than to sub-Saharan African ones. This agonised the analysis team (on
which I had the pleasure of playing a small part) and debates raged about all
the complex ways in which trace contamination or complex analysis artefacts could
have given rise to such a result. But at the end of the day, thanks in part to
the innovation and persistence of Ed Green (on the experimental/processing
side) and David Reich (on the population genetics side), it came down to a low
level of inbreeding between Neanderthals and modern humans, presumably just
after modern humans left Africa. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Needless to say, many people disputed this analysis, and put
forward other, more complex hypotheses to explain what could have generated
this weak signal.<o:p></o:p></span></div>
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Then there was a remarkable discovery: the tip of a little
finger from what was thought to be a Neanderthal in a cave in the Urals, close
to the small town Denisova. This turned out to be one of the best ancient bones
ever sequenced, as around 70% of the DNA was hominid. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Another huge, compound surprise: this bone was not
Neanderthal, rather a separate hominid species. Furthermore, this set of
hominids had very clearly interbred with Papua New Guineans and Aboriginal
Australians, presumably <i style="mso-bidi-font-style: normal;">en route</i> as
they migrated from Africa towards the Australian continent.</span><br />
<span style="font-family: "verdana" , sans-serif;"><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">This time, the signal for inbreeding was far larger. Arguments
that this was some kind of complex contamination were clearly a big stretch. There
are, sadly, not many native Papua New Guineans or Aboriginal Australians in
research. On top of the new question of whether Neanderthals had interbred with
the out-of-Africa migration, there was now clear-cut evidence of both a new
hominid species (with minimal fossil evidence) and more extensive interbreeding.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-indent: 36.0pt;">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">But ancient hominid DNA sequencing doesn’t stop there. Another
Neanderthal bone unearthed in the Urals showed evidence of a fourth hominid
genome, originating somewhere in central Asia. And the rather amazing “cave of
bones” in Spain has brought forth evidence of deep-lineaged, modern human
mitochrondria. <o:p></o:p></span></div>
<span style="font-family: "verdana" , sans-serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Suddenly, the neat, ‘out-of-Africa’ theory, that a single explosive
species replaced all previous hominids, was mainly right - but not quite so neat. There is a web of multiple
hominid populations, all originating in east Africa as far as we can tell and setting
forth around the globe, meeting each other and having occasional sex. It’s
interesting to speculate how much communication and collaboration – or warfare
– happened amongst us and our cousins.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-indent: 36.0pt;">
<span style="font-family: "verdana" , sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: "verdana" , sans-serif;">Expect more surprises in the coming years – we are just
starting to understand the amazing story of how we evolved.</span><o:p></o:p></div>
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Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com1tag:blogger.com,1999:blog-1688712549144760104.post-68738780101110977472015-12-14T00:53:00.002-08:002015-12-16T08:19:18.689-08:00Elegant genome - the 2nd day of christmas<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Verdana, sans-serif;"><br /></span>
<h4>
<span style="font-family: Verdana, sans-serif;">
On the second day of christmas, my true love sent to me:</span></h4>
<span style="font-family: Verdana, sans-serif;"><b><i>The </i>C. elegans (worm)<i> genome</i></b>. The lowly nematode worm is probably the "newest" widespread model organism, developed by Sydney Brenner and colleagues in the 1960s at the Laboratory for Molecular Biology (LMB) in Cambridge as something between the complexity of fly and the simplicity of yeast. It was an inspired choice: you could keep the worm in the laboratory easily (it eats a lawn of bacteria, very often <i>E. coli</i>), and setting up crosses was easy and remarkably (and this shows how lucky Sydney is) it has completely stereotypical development. Every adult<i> C. elegans </i>worm has an <i>identical </i>number of cells (John Sulston was one of the key people to work this out who would later lead the worm and genome project). It is as if every cell has name, with one tree providing the single way of going from a genome to a collection of cells.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">So, as the techniques of genome mapping and sequencing grew up in the 1980s, led by innovations again from the LMB in Fred Sanger's group, and amazing pronouncements were made that we should sequence the human genome but perhaps test technology out on some simpler organisms, John Sulston choose the worm, and set up what seemed like an insanely ambitious plan to sequence the whole of the ~100 MB genome. </span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">In the early 80s an American MD PhD, Bob Waterston, came around to map more worm genes and got hooked into first the mapping and then the sequencing with John Sulston and Alan Coulson. This started a long-time collaboration between Bob and John, ending in the human genome, and when Bob went back to St louis, there was a constant flow of people between the two groups. Later on, when John had set up the Sanger Centre (it changed to the Sanger Institute) and Bob the Genome Science Centre, whole teams would exchange between these two sites.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">And it was madness - in the late 80s - to state that you were going to sequence all of the worm genome. Remember, no bacteria had been sequenced at that time, and it seemed like there should be perhaps a bigger focus on technology development. But John and Bob both realised that it was feasible (with effort: a veritable "factory" of people) and that many innovations were small, not large. Many of the technical aspects came from this drive. For example, the use of shotgun (random sampling of DNA pieces) on individual fragments of DNA, followed by the process of "finishing" where specific experiments were designed came to be standard (giving rise to a new laboratory role: 'finishers'). Roger Staden's "Staden package" was the first computational workbench for this.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">And the worm was sequenced in 1998, a mere year after <i>E. coli</i>. Due to another quirk of biology - that <i>C. elegans</i> do not have complex, repeat dense centromeres - it still remains the only metazoan genome with a complete sequence end-to-end. Both its genome and its development somehow capturing the species name "elegans" well.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">I had arrived as a young PhD student just before the final publication, and started to work a little, on the side, on worm genome annotation. Because of the organisation around the worm genome the annotation of the genome had kept pace with the sequencing. The annotation happened in this rather amazing software system, called ACeDB, which one would now call a No-SQL graph database, written by Richard Durbin (my then supervisor) and Jean Theirry-Mieg, a French physicist (you knew when you hit a bug in Jean's code, as it would crash with a series of french expletives on the console). </span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">ACeDB was completely before its time - a graph/document database; the concept of using hyperlinks between display items (before the web) and integrated, customised graphical displays on the database. The annotation of the worm happened via a blend of automated programs/scripts and dedicated manual effort, confirming, and sometimes tweaking, each exon and splice site by hand. My beast of a gene prediction program (GeneWise) started to pump out information at hideous computational cost to go into this. The worm therefore led also in the concepts around annotation and information storage, being the first "modern" model organism database.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">The worm community - both in genomics and more broadly - remains extremely tight. You can just about fit the entire community in one conference, and there is a sense of camaraderie that runs deep. Worms continue to lead a lot of developmental biology, in particular in neuronal circuits where they remain the only organism with a complete map of their small, primitive (but perfectly formed) brain. The genome is just one part of the completeness of this organism, but also has a huge part in the history of genomics.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<br /></div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com0tag:blogger.com,1999:blog-1688712549144760104.post-37097355423291771882015-12-13T14:29:00.001-08:002015-12-16T14:01:55.759-08:00The Genome Days of Christmas: 1st day of Christmas, E. coli and friendsInspired by a very boring train stoppage last year, I am going to add, one a day, to this of great / interesting genomes until christmas day.<br />
<br />
<br />
<h4>
On the first day of christmas, my true love sent to me:</h4>
<br />
<span style="background-color: white;"><b style="color: #252525; font-family: sans-serif; font-size: 14px; font-style: italic;">Escherichia coli and its associated phages. </b><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">This humble bacterium is one of our commensal organisms; it hangs out in our gut being, usually, useful to us. But the reason why every molecular biologists knows about this critter is that it is also the bedrock of DNA manipulation. Molecular biologists shuttle DNA from all sorts of different organisms through E. coli constantly. It is the assembly line for much of molecular biology - where you capture, grow up, extract DNA. The smell of the growth media to grow E. coli infuses all molecular biology labs. E. coli has its own parasites - phages - which are viruses that infect E.coli, and these are as useful as their bacterial host.</span></span></span><br />
<span style="background-color: white; color: #252525; font-family: sans-serif; font-size: 14px;"><br /></span>
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">As well as being this practical, laboratory workhorse, these organisms play a key role in understanding life. We know most of the early layout of gene sequences and protein translation due to studies in E. coli. A number of the classic experiments on gene regulation involved the lambda phage infecting E. coli. It still remains one of the "best" complete systems to study, with aspects from complete metabolic modelling through to understanding how bacteria sense chemicals and change swimming direction.</span></span></span><br />
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;"><br /></span></span></span>
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">The two common experimental phages to E. coli - PhiX174 and lambda phage - are probably the two most sequenced pieces of DNA in the world. PhiX174 is tiny, only 5,386 bases long, and was the first genome sequenced by Fred Sanger and his team. Lambda phage is bigger (~44 KB) but both PhiX174 and Lambda are useful not only because of their small size, but also because you make bucket loads of the DNA which is near identical not only in their sequence, but also in the details of the precise behaviour. PhiX174 was run as a "control lane" on illumina machines for almost 5 years, meaning that 1/8th of the world's sequencing capacity was dedicated to this; Lambda phage is the "burn-in" experiment provided with Oxford Nanopore's kit to ensure the machines work in the remote laboratory successfully. Seeing traces of PhiX174 or lambda phage in a sequencing experiment is common place; in the large centres, there has been micro-evolution of some phages which you track through their publicly accessible projects.</span></span></span><br />
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;"><br /></span></span></span>
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">The E. coli genome itself - a mere 4MB - is a bit of a sad tale. In theory it should have been clearly the first genome to be sequenced, but the consortium to sequence it got locked in too early to an unautomated sequencing technology, and took a rather painstaking approach to complete it (finished in 1997). It was soundly beaten by </span></span></span><span style="background-color: white;"><span style="color: #292f33; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; white-space: pre-wrap;">Haemophilus influenza</span><span style="white-space: pre-wrap;">e</span></span><span style="background-color: white; color: #252525; font-family: sans-serif; font-size: 14px;"> in 1995 from Craig Venter's lab with a scale up of the shotgun technique on (at the time) state of the art fluorescent dye automated sequencers. This would not be the last technological tussle in genomics.</span><br />
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;"><br /></span></span></span>
<span style="background-color: white;"><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">The final thing to note about E.coli is - like all bacteria - it doesn't really have one genome. Different individual bacteria swap around substantial amounts of genomic sequence all the time, like a giant game of trading cards, endlessly looking for the perfect combination for the situation each individual is in. Unfortunately for us, sometimes this shuffling leads to nasty - sometimes deadly combinations - where additional pieces of DNA change this mild mannered, laboratory stalwart into a dangerous infectious </span></span></span><span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">enemy. One example was the outbreak in 2011, eventually traced to a German bean sprout plant (but not before many other possibilities were touted). This was notable in that for the first there was a crowdsourced, internet response to both sequencing and analysing this genome. </span></span><br />
<span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;"><br /></span></span>
<span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;"><br /></span></span>
<span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;">(Many thanks to Mark Pallen for catching both factual and grammatical errors)</span></span>
<span style="color: #252525; font-family: sans-serif;"><span style="font-size: 14px;"><br /></span></span>Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com1tag:blogger.com,1999:blog-1688712549144760104.post-57281454561514662015-10-22T03:28:00.000-07:002015-10-23T01:21:13.658-07:00Genomics and Big Data in Medicine<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
<span lang="EN-GB">One of the great challenges – and
opportunities – over the coming decade is the perfusion of molecular
measurement, and accompanying data analysis, into general medicine. This will be
nothing new for clinical genetics and other niche disciplines, but as medicine
begins to mine the rich data streams from genomics, transcriptomics and
metabolomics research, we will start running into some rather tricky
integration problems. This is interesting both scientifically and socially, as
a huge wave of technology pushes us to create clinical utility out of a
confluence of molecular data, high-resolution imaging and data from continuous-sensing
devices.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Opinion-makers have been grappling with
these issues publicly for a while, and there are programmes in place in many
different countries to enable, exploit and empower this change. Futuristic
language like "The End of Medicine" and "The Revolution in the Clinic" is
bandied about, and governments, charities and companies are all keen to get involved.
<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">It’s all very exciting.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">I have two different perspectives on this
issue. First, as one of the world’s major sources of reference molecular
information, EMBL-EBI is a trusted adviser and public data and knowledge provider.
Our medical strategy is in place, supported by our advisory boards and ready
for implementation (I will be writing a paper on this strategy with Rolf
Apweiler). As always, we are prepared to help different sectors and communities
deal with ‘big data’ storage, standardisation, integration and knowledge
management.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">On a more personal level, my research
collaborations with clinician scientists have opened my eyes to the challenges and
opportunities of practical medicine – some of which I mentioned in my blog post
on <a href="http://genomeinformatician.blogspot.com/2015/05/human-as-model-organism.html">human as model organism</a>.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">I also think we should go back to looking
at how different technologies have enriched – but not fundamentally changed –
medicine, and at how medicine has adopted new technologies over the years. For
me, there is no better example than X-rays (I am indebted to the excellent
essay and references from “X-rays as Evidence in German Orthopedic Surgery,
1895–1900”, by <a href="http://europepmc.org/abstract/MED/16114797">Andrew Warwick, Isis, 2005, 96:1–24</a> )
<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<h3>
Technology, medicine and consumers</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-cKYuLtUDEt0/Vii1YkcAiJI/AAAAAAAAAPs/7er5gTuqcGg/s1600/First_medical_X-ray_by_Wilhelm_Ro%25CC%2588ntgen_of_his_wife_Anna_Bertha_Ludwig%2527s_hand_-_18951222.gif" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="http://4.bp.blogspot.com/-cKYuLtUDEt0/Vii1YkcAiJI/AAAAAAAAAPs/7er5gTuqcGg/s1600/First_medical_X-ray_by_Wilhelm_Ro%25CC%2588ntgen_of_his_wife_Anna_Bertha_Ludwig%2527s_hand_-_18951222.gif" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Picture of Anna Bertha Röntgen's hand</td></tr>
</tbody></table>
<span lang="EN-GB">The fact that X-rays could reveal internal
aspects of the human body were discovered serendipitously in 1895 by Wilhelm
Conrad Röntgen at the University of Würzburg, when his wife accidentally put her hand between radium (a strong X-ray emitter) and photographic film, during
Röntgen’s systematic analysis of these new electromagnetic rays. The iconic
image of her bones and wedding ring shows at a glance that he had discovered a
new way to image living tissue, and in that gestalt moment it seems Röntgen himself
understood that this would be useful in medicine.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">But it took more than 20 years for X-rays
to be used widely in medicine, for a number of reasons. For one, the early
developers and adopters of X-ray machines were driven not by medical altruism,
but by the need to capture the public’s interest and sell kit – notably in the
wealthy, technology-obsessed US at the time. Fair grounds in the northeast
began offering ‘bone portraiture’ salons: amusing devices with live radium
exposed provided either a picture you could take away or even a fluorescent
screen for a live “show”. Such portraits were quite a fad in 1900s New York,
with many families proudly mounting pictures of their own X-rays in their houses
as a talking point. (This does rather remind me of genomics and genotyping
being marketed directly to modern self-obsessed consumers)<o:p></o:p></span></div>
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<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-z-Rl1namUSU/Vii1-3jaN4I/AAAAAAAAAP0/qt6s8p2ZdiY/s1600/x-ray-hand.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="http://1.bp.blogspot.com/-z-Rl1namUSU/Vii1-3jaN4I/AAAAAAAAAP0/qt6s8p2ZdiY/s1600/x-ray-hand.jpg" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Advertisement for a bone portraiture studio</td></tr>
</tbody></table>
<span lang="EN-GB">There was both enthusiasm and scepticism in
clinical circles – mainly the latter. X-rays made bones visible to the naked
eye, but didn’t do much else in terms of treatment. The resolution wasn’t good
enough to pick up hairline or in-place fractures, and an obviously broken bone
didn’t require X rays to diagnose. One German doctor in the 1890s,
disillusioned by the complexity in even getting an X-ray machine to work,
declared that widespread use of this new technology was “an idle fantasy”. As
the poisoning effect of radiation became clear, notably on fairground ‘bone
portraitists’, many people in the mainstream medical establishment hardened
their view that this technology was mere quackery, useless for clinical
practice.<o:p></o:p></span></div>
<h2>
</h2>
<h3>
</h3>
<h3>
</h3>
<h3>
Shooting from the hip</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<span lang="EN-GB">But the ability to see inside the body remained
tantalising to many clinicians and scientists, who continued to work on the
technology. One group of clinical innovators saw the possibility to improve
gallstone treatment. Gallstones are painful, dangerous and difficult to remove,
but with the advent of general anaesthetic surgery was becoming a practical
option. What was missing was a way to diagnose the presence of gallstones in
patients without having to carry out surgery. However, for those keen to make
use of X rays, there was a catch. Gallstones, despite being quite solid, were
in fact transparent to the ‘hard’ X-rays used at that time – unless they had
become calcified, which happened in only 5% of cases. So the clinicians had the
right idea: make a better diagnosis to inform a clinical action (surgery) that
helps the patient. But a key technical detail made it only occasionally successful.
As one might imagine, the anti-X-ray crowd pointed to this failure as
indicative of the futility of using the technology at all. <o:p></o:p></span></div>
<div class="MsoNormal">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-VxlHaZWT8Dg/Vii2y2KET2I/AAAAAAAAAQA/B-kGmMxYyug/s1600/800px-Dislocated_hip.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="256" src="http://1.bp.blogspot.com/-VxlHaZWT8Dg/Vii2y2KET2I/AAAAAAAAAQA/B-kGmMxYyug/s320/800px-Dislocated_hip.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Modern Xray of childhood hip displacement</td></tr>
</tbody></table>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Clinicians were also at the time arguing over
whether surgery or manipulation was the best way to treat childhood hip displacement.
Manipulating the hip joint into the socket without surgery (under anaesthetic
in a medical setting) seemed to work well enough. However, this non surgical approach
was traditional, largely carried out by informal medical help, and scorned by the
more professionalised medical establishment. As the dispute deepened, the
manipulation group (interestingly, led by a surgeon) started taking X-rays
before and after treatment to show how the hip joint moved into the correct
place following their treatment. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<h3>
All systems go</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<span lang="EN-GB">Andrew Warwick uses this example to explore
how evidence (i.e. X-rays) gains currency in practical medical discourse.
Having convinced the medical establishment of the utility of X-rays, more and
more practitioners began to buy X-ray machines and engineers began to develop the
technology to make and control X-rays in earnest in a clinical rather than
physics laboratory setting. Both led to a more widespread adoption for things
like orthopaedic interventions and, latterly, the breakthrough use of X-rays to
diagnose Tuberculosis.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">The use of X-rays was also catalysed by
historical events. The Great War called for all manner of medical innovation,
and in 1915 Marie Curie and her daughter famously set out to help doctors on
the battlefields of France see bullets, shrapnel, and broken bones in their
patients in the new Red Cross Radiology Service. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">X-rays, radiology and imaging have become essential
tools for any medical practice. Every clinician must have at least some working
knowledge of the different types of images (and the different risks to the
patient). They have practical applications in many disciplines, including
neurology, internal medicine and cardiology. The discipline of medical imaging,
where rather geeky clinicians work with physicists to push the limits of MRI, X
Rays (of all sorts) and echolocation, brings a vast range of technology to bear
on improving our ability to look inside the body – and sometimes intervene – in
finer and finer detail.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<h3>
Hindsight is 20-20</h3>
<h2>
<o:p></o:p></h2>
<div class="MsoNormal">
<span lang="EN-GB">Innovative clinicians who believe in the
potential of genomics and big-data analysis can learn a lot from the story of
X-rays. The enthusiasm of those who grasped the potential of X-rays early on –
including visionaries like Röntgen – can serve as a caution, reminding us not
to be overconfident about predicting early success. My take-home is that we
need to explore many avenues simultaneously – we cannot easily predict where
the quickest win will be. Perhaps rare disease diagnosis, or personalised
cancer treatment, or infectious biology? But this uncertainty in having to
spread our bets to find the first beneficial area should not really deter the
long term view that genomics and data analysis will become an every day part of
medicine. Fundamentally this is about understanding ourselves at finer and
finer detail, and this information will be useful when we are ill.<span style="mso-spacerun: yes;"> </span>Who can imagine medicine today without
medical imaging? Twenty years from now, will medicine before genomics be
recognisable?<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Direct-to-consumer genomics has enjoyed
rapid uptake from early adopters (myself included!) who may be motivated to
show off their knowledge, or who may be keenly interested in their ancestry. But
the direct-to-consumer market is not the same as integration into healthcare
systems; genomics and data analysis is not an “end run” around medical
practice, rather another tool for the never-ending quest in trying to
ensure our good health.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Just as X-rays and imaging were eventually
absorbed and codified into clinical practice, genomics and data science will
become so ingrained that we will not remember what it was like before. Medical
imaging is a rigorous discipline in its own right but remains firmly rooted in traditional
medical structures to ensure it fits seamlessly into practical clinical
practice. Because of this, it remains a familiar sight to any patient with a
habit of falling out of trees (and their frantic parents).<o:p></o:p></span><br />
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">Similar to medical imaging, genomics and
data science will not change the fundamentals of clinical practice; skilled professionals who have seen many similar (but not identical) examples in others can use their experience and knowledge to diagnose and hopefully treat disease. However, it’s
quite likely there will be unexpected setbacks and surprising successes in their
use, in particular at the start. New medical disciplines (clinical genomics? clinical
bioinformatics?) will emerge inside clinical structures, which will provide the
bedrock of routine practice. Every clinician will be expected to have a grasp
of the fundamentals of these techniques, and specialists will offer more in-depth
knowledge. Society is sure to become more comfortable with this new flavour of information,
and with more self-monitoring (on devices, at home) changing how information is gathered around an individual - those same people who are already more motivated to research on the Internet before the visit to the clinician. But the need for
intelligent, skilled individuals who have seen many examples of particular
scenario will still be needed to guide, inform and treat, whatever the density of information gathered.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
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<div class="MsoNormal">
<span lang="EN-GB">Genomics and data science, once they’ve
shown their worth in practical day-to-day practice, will help clinicians make better
decisions for their patients. Some diseases will transition from problematic to
routine diagnosis and treatment. Some diseases will not be as affected by these technologies. There may be plenty of glitches, dead ends and troubling
uncertainties, but if we learn from innovators of the past, using these
technologies will quickly become as routine as going to the X-ray department to
examine<a href="https://www.blogger.com/null" name="_GoBack"></a> a hairline fracture. <o:p></o:p></span></div>
</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com6tag:blogger.com,1999:blog-1688712549144760104.post-29083762325169159492015-10-05T11:38:00.002-07:002015-10-06T05:29:05.764-07:0010,000 Up<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">I've just passed my 10,000th follower on Twitter, and similar to when I went past <a href="http://genomeinformatician.blogspot.co.uk/2014/01/new-media-for-science-3-years-in.html">5,000 followers</a> this feels like a good point to reflect on this open, 'blog-and-tweet' world evolving around me.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">Many of the comments I made two years ago have stood the test of time: Twitter is still fundamentally a conversation, broadcast not just to your lunch queue but worldwide, and blogs remain lightweight, informal platforms for review and commentary. And as with any conversation you have to consider your audience first, and as with all public writing everyone still need and editor [<i>sic</i>].</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">So, happily, the basic principles hold true. But with this up tick, I want to reflect on whether these media/platforms are actually right for everyone, and the future of open discussion.</span><br />
<h3 style="text-align: left;">
<span style="font-family: Verdana, sans-serif;"><br /></span></h3>
<h3 style="text-align: left;">
<span style="font-family: Verdana, sans-serif;">
You don't have to join Twitter</span></h3>
<span style="font-family: Verdana, sans-serif;">I get a lot out of Twitter, and part of that includes sharing interesting papers or blog posts I've spotted in my twitter stream or adding new ones to the mix. I find myself asking people if they are on Twitter so I can be sure to reference them when their work comes up. </span><span style="font-family: Verdana, sans-serif;">This sometimes elicits a somewhat sheepish response along the lines of, "I don't do Twitter," or "Twitter makes me feel uncomfortable," or, in rare cases, a flash of rage against the world of informal and all-too-short critiques/discussions. </span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">I try to be positive about my Twitter experience while acknowledging that Twitter is not for everyone, and recognising its pitfalls: it can be distracting, people are often tempted to over-share aspects of their lives, etc. Twitter is certainly not a requirement, and blogs are rather personal. While I am not alone in finding these media useful, I wouldn't say that you are missing out on crucial information if you don't jump in. Mainstream scientific interaction is still via published, peer-reviewed papers and giving talks at conferences and workshops, and that is where the really important stuff is to be found.</span><br />
<h3>
<span style="font-family: Verdana, sans-serif;"><br /></span></h3>
<h3>
<span style="font-family: Verdana, sans-serif;">
You don't have to interact on Twitter</span></h3>
<span style="font-family: Verdana, sans-serif;">Many people use Twitter as a sort of personalised, text-based radio station: something you can tune into when you want, that gives you a personalised mix of news. I am pretty sure no one else has my mix of interests in worldwide science, UK, European and US politics, cricket and events in the North Tyne valley (in Northumberland). Twitter is useful just in this "consumption" mode.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span><span style="font-family: Verdana, sans-serif;">Twitter is also inherently ephemeral - people shouldn't expect you to be on it all the time (although it seems like some people are!), and no one can expect that a comment (when someone @'s you) will always elicit a response. People have lives, holidays, emergencies and so forth, and all of those things make it perfectly reasonable to ignore this global conversation.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif;">But if you do start a conversation about a topic, I think it is reasonable for others to expect that you will respond and follow through on sensible comments. Some conversations spark a need for deeper discussion (i.e. "we're going to need to meet up face to face for this one..."), and some might dwindle because you have to take your kids to a pirate party, sleep, etc. And that's fine. The instant, global nature of Twitter makes it empowering in some ways, but it also needs to be tamed to fit into your life.</span><br />
<h3>
<span style="font-family: Verdana, sans-serif;"><br /></span></h3>
<h3>
<span style="font-family: Verdana, sans-serif;">140 characters: plenty of room for misinterpretation</span></h3>
<span style="font-family: Verdana, sans-serif;">Twitter's limitation of 140 characters can be challenging, and it's surprisingly easy to misinterpret a tweet - in particular when it is intended to be funny, or when it involves a disagreement. Without body language, vocal nuance or the comfort of expository paragraphs, it is extremely easy to blunder on, not realising that the other person </span><span style="font-family: Verdana, sans-serif;">has</span><span style="font-family: Verdana, sans-serif;"> taken offence, become confused or both.</span><br />
<span style="font-family: Verdana, sans-serif;"><br /></span><span style="font-family: Verdana, sans-serif;">So I stand by my rules of avoiding 'jokes' on Twitter (unless I'm <i>very</i> confident that the people in the conversation will get it), and avoiding disagreements, particularly the complex ones. Science is full of ambiguity and disagreement, which is part of what makes it exciting - but Twitter's natural limitations turn those elements into confusion all to easily. It just is not the right medium for this kind of engagement (more on this below).</span><br />
<h2>
<span lang="EN-GB" style="font-family: Verdana, sans-serif;"><br /></span></h2>
<h2>
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">Blogs</span></h2>
<h3>
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">Post-publication peer review is cool</span></h3>
<div class="MsoNormal">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">The rise of open, “pre-peer review”
platforms in molecular biology communication is a big step change.
It started with the quantitative biology community putting papers on the Physics <a href="http://arxiv.org/">Arxiv</a> site, which quickly became the mainstream way to communicate in high energy and theoretical physics. Two platforms have
been added to this: <a href="http://biorxiv.org/">Bioarxiv</a>, which has a similar model to Arxiv but with a stronger emphasis on biology papers (e.g., they process Word files). The second is <i><a href="http://f1000research.com/">F1000Research</a></i> which combines submission, peer review and publication production workflows seamlessly as one stream - and insists on open peer review, so the peer-review comments and replies are all published together with the paper – everything in the
open.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">In this new publishing environment, blogs are starting to feel very … 2000 to me. Why am I am blogging when I could write a pre-publication with a DOI,
which can be cited and made a more formal part of the scientific discourse? Blogging is nice, because it is accessible to a broader
audience and allows for a more chatty, 'natural language' style – but if the main purpose is to communicate with scientists, pre-publication servers are a better way to go. If the goal is to communicate with a broad set of interested people, an on-line magazine or aggregation site might be better. The independent
blog is on borrowed time (which does make me wonder why I’m writing this).<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">There is a lot of social innovation happening in science, and each one of them takes a bit of adjustment. (For example, keeping track of
comments on <i>F1000Research</i> articles can be a bit exhausting.) In general, the move towards a more rapid, open discourse is almost certainly a good thing... Except...</span></div>
<h3>
<span lang="EN-GB" style="font-family: Verdana, sans-serif;"><br /></span></h3>
<h3>
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">Open, recorded, textual criticism is
complex</span></h3>
<div class="MsoNormal">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">These new, open communication platforms share three key
characteristics: they are text based, they can be close to immediate (like spoken conversation) and, importantly, they are 'on the record', indefinitely. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">This is an explosive combination for
constructive criticism. The fluid, conversational nature of the media makes it easier to voice criticism, but it demands a certain level of nuanced writing skill to make up for the
absence of voice and body language. These platforms make it too easy for people to barrel on with their
views, offering no mechanism that advise about, for example, the responding tone of (or meaningful silence from) the other participants. Disagreements can flare up, triggered by trivial things like different use of common definitions, or by deeper problems like a fundamental misunderstanding of the important elements of different positions in a debate. </span><br />
<span lang="EN-GB" style="font-family: Verdana, sans-serif;"><br /></span>
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">The global nature of these communications adds many layers of cultural complexity, as they are (in science) mainly carried out in the English language, which provides endless opportunities for misunderstandings and perceived callousness due to people's different use of English. But it is perhaps the </span><span style="font-family: Verdana, sans-serif;">permanent, public, </span><span style="font-family: Verdana, sans-serif;">recorded nature of these critical conversations that raises the stakes highest. Onlookers can read motivations in the text that they wouldn’t interpret in the same spoken conversation. One cannot easily forget such a disagreement when it has been so public and lasting, in particular
when it becomes personal.</span></div>
<div class="MsoNormal">
<br />
<h3 style="text-align: left;">
<div class="MsoNormal" style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;">
</div>
</h3>
<h3 style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-style: normal; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;"><br /></span></h3>
<h3 style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-style: normal; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">Online constructive criticism in science</span></h3>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-style: normal; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;">
<span style="font-family: Verdana, sans-serif;">I don’t think the social
mores are in place for how we handle criticism on these communication media. There are so many cultures and subtleties - there is a reason, after all, why science writing in papers is so dry and literal. In online conversations criticism is often so
polite as to be obtuse, or impossible to understand. More upsettingly, criticism can be so vitriolic
and personal that it can't be absorbed at all by the person receiving it or the audience observing the dialogue. </span></div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-style: normal; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-style: normal; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px;">
<span style="font-family: Verdana, sans-serif;">Science is not the
only field that has to work out a social contract for on-line communication,
and I have a feeling that it’s going to be the generation that grew up with
FaceBook, Twitter and SnapChat that's going to work out how best to do this. But I don’t think the current behaviour is right. Constructive criticism –
honest but aiming for the best science – is such an important part of our our world that we need to create the right environment for it online. </span></div>
</div>
<div class="MsoNormal">
<span style="font-family: Verdana, sans-serif;"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB" style="font-family: Verdana, sans-serif;">Overall, I am still positive about open communication. I find Twitter interesting, informative and fun, and I find blog posts provide an outlet and means of communication that lets me write more broadly and accessibly about science. These media will evolve - just as every other medium has - and be used by more and more scientists. I still enjoy being part of this evolution. </span></div>
</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com3tag:blogger.com,1999:blog-1688712549144760104.post-83156556176984176582015-09-16T12:04:00.000-07:002015-09-16T12:05:03.401-07:00Anatomy of an mainstream science piece<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
<span lang="EN-GB">Last week, the <i style="mso-bidi-font-style: normal;">Guardian</i> published a Comment by me entitled, ‘</span><span style="mso-ansi-language: EN-US;"><a href="http://www.theguardian.com/science/blog/2015/sep/11/why-im-sceptical-about-the-idea-of-genetically-inherited-trauma-epigenetics">Why I'm sceptical about the idea of geneticallyinherited trauma</a>'</span><span lang="EN-GB">. In this blog post, I’d like to go
through what happened behind the scenes when someone from the mainstream press
asked for my views, what my thought process was before I started drafting a
response, and why I believe we should all participate more in public discourse on science.</span><span style="mso-ansi-language: EN-US;"><o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">The motivation for the Comment was a short
story, published almost a month ago in the <i style="mso-bidi-font-style: normal;">Guardian,</i> about a paper that made claims about the transmission of some molecular memory
from Holocaust victims to their children via epigenetics. The paper itself was
pilloried on Twitter, rightly so in my opinion (one scientist remarked that
this is a perfect example of how <i style="mso-bidi-font-style: normal;">not</i>
to run an epigenetics study). The story was prepared by a reporter covering for Ian Sample (science editor for the <i>Guardian</i>), who was away on holiday; an objective
expert opinion was not found for the story, and I think that when Ian returned he felt motivated to redress the
balance. Probably because of my active role on Twitter, and with
support from other scientists (<a href="http://www.theguardian.com/profile/matthew-cobb">Matthew Cobb</a> in
particular), the door was open for me to pester Ian for a response.<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">Before you rush to blame the reporter (or
journalism, generally) for rushing a story through, it’s important to keep in
mind that the media operates under some fairly stringent constraints. First,
news must be new, or it isn’t news – so there is always time pressure. In
addition, any piece needs to meet two criteria to be broadcast (in print,
online, on radio or TV): (1) it must be interesting, and (2) it must be comprehensible
to a general audience.</span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">Interestingness</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">Everyone involved in a large media apparatus
(radio, TV, print, etc.), including journalists, editors, subeditors,
producers, fact checkers, camera operators and many other professions, aims to
maximise interest, accessibility and delivery. For science journalists,
“interesting” can mean, broadly, one of three things:</span><br />
<br />
<ol style="text-align: left;">
<li><span style="text-indent: 36pt;">It has, or
could have, a direct bearing on the reader/listener/viewer’s life, or in the
case of large events, on the lives of a large number of people. Most health
pieces and environmental disasters fall into this category.</span></li>
<li><span style="text-indent: 36pt;">It is quirky
and gives a refreshing perspective on the world, often making use of a cool
picture. Big kit falls squarely into this category (robots/satellites sending back
the first pictures of a planet / asteroid</span><span style="mso-spacerun: yes;">
</span><span style="text-indent: 36pt;">/ comet automatically max out on this criterion).</span></li>
<li><span style="text-indent: 36pt;">It is of
global significance and is aligned with burning issues in mainstream politics,
for example climate change or epidemics. If this category is mined heavily by
many outlets at once (i.e. fuels the bandwagon), then at a certain point everyone
has to run some part of the story to avoid alienating their audience.</span></li>
</ol>
<span style="text-indent: 36pt;">The best popular
science stories weave in elements of at least two categories: e.g. local
flooding and epidemics, or a striking image of nature/human anatomy with a
healthcare angle. Making a story appealing and digestible is a big part of the
science editor’s craft (more on this below), and a priority for the readers/viewers,
etc.</span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">Is ‘right’ interesting?</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">Whether the science is ‘right’ or not doesn't always have a lot to do with whether it is interesting.
This aspect of a story is usually the domain of the science journalist and/or editor,
whose personal credibility is on the line (and who must avoid landing in
legal hot water;<span style="mso-spacerun: yes;"> </span>though for the
purposes of legality, checking the “peer reviewed article” box seems usually to
be enough). Science stories can easily be ‘interesting’ but not ‘right’ – and science
editors get bombarded with press releases focused far more on the interesting. <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">Thankfully, the science editors I know <i style="mso-bidi-font-style: normal;">do</i> care a good deal about the
correctness of the pieces they put in – but that doesn’t help them find interesting
stories that will capture their audience’s attention. And because covering
‘science’ means fielding everything between astrophysics and zoology, it is a
lot to expect that everyone involved will have enough of a working knowledge of
the science and communities involved to present a balanced view of any subject.</span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">Pitch in.</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">So, in short, reporting on science is a
hard job – though it is also fun to be able to write about science all the time.
If you are ever contacted by a science editor to give your opinion, please,
please respond! And think carefully about the overall message. If the science
is sound, it won’t hurt to allow the ‘interestingness’ to come through. If you
do not think the science is sound, say so, outright and clearly (the journalist
can canvass others for opinions; be frank, but not harsh).</span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">What’s the angle?</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">So
when I was asked to write the Comment for the <i style="mso-bidi-font-style: normal;">Guardian </i>(triggered by that epigenetics piece), here is what went
through my head:<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoListParagraphCxSpFirst" style="mso-list: l0 level1 lfo1; text-indent: -18.0pt;">
<br />
<ol style="text-align: left;">
<li>I’ll need to explain the hideously
complex, overlapping definitions of ‘epigenetics’, and give some insight into
the disputes around this. Could easily use as examples the excellent pieces by <a href="http://www.cell.com/current-biology/abstract/S0960-9822(07)01007-X">Mark
Patshne</a>, or the (very British) ‘broad church’ compromise definitions by <a href="http://www.nutshell-videos.ed.ac.uk/adrian-bird-epigenetics/">Adrian Bird</a>,
and touch on the almost 50 years of debate around this word. Caveat: Good to
humanise science, but is this too ‘inside baseball’ (to use an Americanism)? However
interesting this is to scientists, it doesn’t score well on any interestingness
criteria for the general audience.</li>
<li>I could delight in the sheer
inventiveness of biology around epigenetics processes… X chromosome
inactivation in females, for example: tortoise-shell (or calico in the US) cats, women
with multi-coloured irises. Lots of potential for visuals with those. Or the
Calipyge mutation (‘beautiful buttocks’) in sheep: a serious use of
differential imprinting, and you get to use the phrase ‘beautiful buttocks’ – a
sure win! Plus, pictures of proud farmers looking at their prize lambs... For
additional interest, could also write about the long-standing
discovery/exploration of epigenetics in flowering time in plants. Absolutely
satisfies category 2 criteria: a quirky look at the world, with good visuals.</li>
<li>Is this a good example of the
limits of using peer review as the sole measure of quality/correctness? This is
a really important issue, but this paper is by no means the first or last case
of things going wrong. But, realistically, this is almost certainly
uninteresting to most readers. More a matter for scientists to explore amongst
themselves in specialised press, or for a science journalism forum.</li>
<li>Use this as an opportunity to
explore general misunderstandings of genetics (and, in part, epigenetics)? The
theme of genetic determinism is currently too strong in mainstream media, and
this needs counter balancing.</li>
</ol>
</div>
<div class="MsoNormal">
<span lang="EN-GB">Clearly, I went for 4 – but 2 would have
been an easy choice (indeed, I think that article should be written sometime!).
The other options would never make the cut.</span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">Can I go beyond just explaining this (bad) piece of
science?</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">For a long time I have been watching our
culture embrace genetic determinism, more and more. In addition to the
commonplace usage of ‘DNA’ as a metaphor for ‘core values’ (see my <a href="http://genomeinformatician.blogspot.co.uk/2014/05/dna-as-cultural-icon.html">previous
blog post about this</a>, and <i style="mso-bidi-font-style: normal;">Private Eye</i>’s
satirical weekly column, ‘DNA’), I’ve had conversations with non-scientists about
the predictive power of DNA that bring up such concerns as, “I wouldn’t want to
know my genome because I don’t want to know the time of my death”. The person I
was speaking with in that case was quite convinced that the end point of all
these genetic discoveries was a sort of complete roadmap of a ‘healthy’ life, and
certainty about which diseases one would get and when. For him, personally, it
was simply better ‘not to know’.<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">In contrast, there is no shortage of
opportunities for science journalists to investigate claims made about beauty
creams and health schemes ‘tailored’ to a person’s individual genetics. </span><br />
<br />
The
genetics of skin and of exercise response are indeed interesting, but it is
hard to establish (to say the least) any clear-cut genotype-specific
environment effect. Many of the people marketing these schemes are playing on
the ‘DNA is the real you’ meme as if it is a given, rather than checking
whether there is any actual scientific basis for that assumption.</div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">The drafting process</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">As with all my public pieces, the article
was a collaboration with both Mary Todd Bergman, who I bounce ideas off and she
also edits much of my work (including this blog piece). For the <i style="mso-bidi-font-style: normal;">Guardian</i> piece, Ian Sample also went
through several iterations with me to improve readability. This process is
invaluable – it is so hard as a practicing scientist to read your words back as 'lay person', and to stick with using everyday words rather than using
specialised (and thus precise, but inaccessible) terms. <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">I also had to plot a careful course around the
word “epigenetics”. It is a very frustrating topic with at least three 'mainstream' meanings, and numerous attempts to define it. After I had a
reasonable draft I sent it around a broader group of people (I will mention
many of them below) but in particular to <a href="http://www.gen.cam.ac.uk/directory/anne-ferguson-smith/">Anne Ferguson-Smith</a>, who I consider
one of the best scientists studying epigenetics. She helped change the tone of
the piece, in particular stressing the importance and soundness of
developmental and environmental changes in the epigenome (contrasting that to
trans-generational epigenetics).<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">Going through multiple drafts greatly
improves accessibility (the soul-mate of interestingness) – what you <i style="mso-bidi-font-style: normal;">write</i> is not necessarily what people <i style="mso-bidi-font-style: normal;">read</i>. Of course, some things are lost in
the smoothing process. For example, explaining imprinting didn’t make the cut. Getting
across certain ideas was very tricky, for example the fact that in genetics
we’re classifying the proportion of variance in some populations, but often
deliberately sampling where we fix many other features (I hope the thought
experiment of setting the same exam to a mixture of French and English people
is a good one).<o:p></o:p></span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">The broader group?</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">In science, nobody works alone – just as in
writing this piece. But news pieces are written back-to-front, with the
conclusion right up top – so things like references, citations and background
tend to get squeezed out. (Anyone who writes a lot of science papers will know
how weird it can feel <i style="mso-bidi-font-style: normal;">not</i> to include
a list of references.) It doesn’t feel right to see just my own name at the top
of that <i style="mso-bidi-font-style: normal;">Guardian</i> piece. Really there
are four contributors to this article: myself, Ian, Mary and Anne. Furthermore,
the ideas I express here are the result of decades of discussion with people.
Science is always a ‘team sport’ and even in a paper you can seldom provide a
reference for all the things you’d like to. <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">I’ve been discussing the ‘over-reach’ of
the public’s general grasp of DNA for a while, and as with all really
interesting questions my views have been heavily influenced by many of my
colleagues. In particular discussions with both George Davey-Smith and John
Danesh have really helped me understand the epidemiologist’s view of this.
George and John live and breathe human variation every day of the week. Long-standing
epigenetics experts like Anne Ferguson-Smith, Stephan Beck, Eileen Furlong and
Ian Dunham, and the people I met in ENCODE, including Brad Bernstein, Peggy
Farnham and John Stamatopolous, were great teachers in the complex world of
histone modifications and (somatic) epigenetic components. Clinician-Scientists
such as Nazneen Rahman, Helen Firth and Stuart Cook have opened my eyes to the
complexity of ‘well established’ genetic variants, and discussions with people
in the broader quantitative genetics community, including Peter Visscher, Trudy
Mackay, Jonathan Flint and Chris Haley, and on the Genome Campus Oliver Stegle,
Nicole Soranzo, Jeff Barrett, Carl Anderson and Matt Hurles have educated me
about the details of these models and the real-life world of complex trait
genetics. (I still often only have an intuitive understanding of some of the
maths, which gets amazingly tricky – like when you are diagonalising matrices
inside equations with Kroneker product schemes, and dealing with non-negative
aspects of the matrices, and fixing it all up…). <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-GB">Discussions with people in all these very
different fields have helped shape my position about the way we have
communicated with the wider world about genetics and genomics.</span></div>
<h3 style="text-align: left;">
<span lang="EN-GB"><br /></span></h3>
<h3 style="text-align: left;">
<span lang="EN-GB">DNA is not your destiny, and we need to say it loud</span></h3>
<div class="MsoNormal">
<span lang="EN-GB">I hope the piece in the <i style="mso-bidi-font-style: normal;">Guardian</i> contributes positively to the
public discourse around genetics. We are going to be making more and more
detailed discoveries about human traits – diseases, yes, but also everyday
traits such as human height, normal organ processes, mechanisms that drive behaviours
like risk taking, exam achievement, spelling ability, musicality, criminality…
Each of these is a totally sensible thing to study. But interpreting the
results, both scientifically (read the Methods carefully!) and, more importantly,
in a social sense, is going to be difficult and demand a huge time investment. <o:p></o:p></span></div>
<div class="MsoNormal">
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<span lang="EN-GB">As practicing scientists, we need to
continue laying the groundwork started long ago by many others (see Matt
Ridley’s <a href="http://www.theguardian.com/books/2003/mar/30/scienceandnature.features"><i style="mso-bidi-font-style: normal;">Nature via Nurture</i></a>, 2003), engaging
consistently and non-judgmentally with our communities and policymakers about
out work. There is a real task ahead of us in providing an accessible way for
people to digest this information. We should take every opportunity to
communicate on every level, from the most basic to state of the art. Only then
can society really use the hard-earned information gleaned from genetics appropriately,
and for the greater good.<o:p></o:p></span></div>
</div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com3tag:blogger.com,1999:blog-1688712549144760104.post-19325999281032077522015-05-07T04:03:00.001-07:002015-05-07T04:08:46.336-07:00Human as a model organism<div class="MsoSubtitle">
<i><span lang="EN-GB">Model organisms have provided the
foundation for building our understanding of life, including human disease. </span><span lang="EN-GB" style="font-style: normal; mso-bidi-font-style: italic;">Homo sapiens</span></i><span lang="EN-GB"><i> has joined this select group, adding knowledge we can apply to our
myriad companion species. But to resolve even one small part of the moving,
shifting puzzle of life, we need them all.</i><o:p></o:p></span></div>
<div class="MsoSubtitle">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Biology is incredibly complex. Even the
simplest bacteria make intricate decisions and balance different demands, all
via chemical reactions happening simultaneously in what seems like just a bag
of molecules, called a cell. Larger organisms all start as a single cell and
eventually become living creatures that can fly, or slither, or think –
sometimes living for just a day and sometimes for centuries. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Whatever the process, whatever the outcome,
it all begins with information, recorded in a tiny set of molecules (DNA) in
the very first cell. How that information made it this far, and how it is now
composed, comes down to the twin processes of random change (mutations) and
competition between individuals, giving rise to evolution. Evolution has, quite
amazingly, given rise to everything from uranium-feeding bacteria to massive
sequoias and tax-filing, road-building, finger-painting humans.<o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">Modelling life</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Unpicking this complexity is hard, in part
because so many things are happening all at once. We’ve been working on it for
centuries, building layer upon layer of knowledge collectively, in many labs
throughout the world, usually relying on specific organisms where we accumulate
large amounts of knowledge on the processes of life. These ‘model’ organisms,
for example the gut bacteria <i style="mso-bidi-font-style: normal;">E. coli</i>,
are selected for their ease husbandry and other features of their biology. Interestingly,
most of them have been our companions or domesticated in some way throughout
our explosive growth as a species. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">To create models of animal life processes
at the simplest level, we use organisms like European and African yeast (used
for both baking bread and making beer), which has a nucleus (like all animals,
they are eukaryotes). We use the humble slime mould, which spends most of its
time as a single cell but, in extremis, will band together to form a
proto-organism that has given us insights into signalling. Taking it up a
notch, we are helped by pests that have lived off our rubbish since our
earliest days in Africa: fruit flies, mice and rats provide profound insights
into animal life. Even the model worm <i style="mso-bidi-font-style: normal;">C.
elegans</i>, which helps us understand development, could be considered ‘semi-domesticated’
(though no one really knows where ‘wild’ <i style="mso-bidi-font-style: normal;">C.
elegans</i> might live). <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Each of these models has its strengths and
weaknesses: the time it takes to breed generations, the effort involved in
handling them, the availability of automated phenotyping systems, the
flexibility (or lack thereof) of their cellular lineage, and more exotic
features, such as balancer chromosomes, RNAi ingestion, chromosomal engineering.
But they all share one distinct quality: they are not human.<o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">Using ourselves?</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Using <i style="mso-bidi-font-style: normal;">Homo
sapiens</i> as a model species to understand biology has many advantages, and
some important drawbacks. Leaving aside for a moment the interaction with research
into human disease, what are the benefits of using ourselves as an organism on
which to model basic, fundamental life processes?<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Humans are large, so we can
acquire substantial amounts of material from consented individuals either from
living persons (e.g. blood) or via autopsy;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">The extremely large population
can be accessed relatively easily, with no on-going husbandry costs;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Wild observational studies (i.e.
epidemiology) are feasible to deploy at scale, though at considerable cost;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">The population has good genetic
properties: it is outbred, and mating is fairly random with respect to
genotypes, usually with only geographic stratification;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Many phenotyping systems are
designed explicitly for this organism, in some cases with a high level of
automation;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">An on-going, proactive
screening process for rare, interesting events (i.e. ‘human clinical genetics’)
are available in many parts of this population at the scale of millions of
screening events each year;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB"><br /></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Cells from this organism can be
cultured routinely using iPSC techniques, and these cellular systems can be
genetically modified and made into functional tissue-scale organoids;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Limited intervention studies are
feasible (if expensive);<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l1 level1 lfo1;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Research on this organism is
well funded, thanks to widespread interest in human disease. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">The drawbacks:<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">There are no inbred lines for <i style="mso-bidi-font-style: normal;">Homo sapiens</i>;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">The large size and tissue
complexity of this species, in particular the brain, presents significant
challenges to understanding cellular and tissue behaviour;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">The organism cannot be kept in
a strictly defined environment (though an increasing number of aspects can be
monitored in observational studies);<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Explicit genetic crosses cannot
be done (though the large number of individuals make it possible to observe
many genetic scenarios in the population);<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Genetically modified cells cannot
be used to make an entire organism;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Intervention studies are quite
limited by both safety and expense;<o:p></o:p></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><br /></span></span></div>
<div class="MsoListParagraph" style="mso-list: l0 level1 lfo2;">
<!--[if !supportLists]--><span lang="EN-GB" style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><!--[endif]--><span lang="EN-GB">Ethical issues, which are
important when studying any species, are more involved for Human – even for
basic research.<o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">An old story</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Using <i style="mso-bidi-font-style: normal;">Homo
sapiens</i> as a model species is not a new idea – it has been around since the
dawn of genetics and molecular biology. Studies of human height motivated the
early theory around quantitative genetics. Quite a bit of mammalian (and
general eukaryotic) biochemistry and genetics was originally uncovered by
discoveries of inborn errors in human metabolism in the 1960s and 1970s, and
was confirmed by biochemistry studies in cow and pigeon tissue. And robust
cancer-derived cell lines – most famously HeLa cells – have been used in molecular
biology for decades. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">But the downsides to using humans as a
model species are far fewer in number now than they were two decades ago, when
the human genome was considered to be so large that a major, global consortium was
required to generate it. But the human genome is dwarfed in size and complexity
by bread wheat and pine, whose genomes are being untangled today. The cost of
human genetics studies has plummeted so that large populations are more
accessible and easily leveraged (a genotyping array now costs under €50 and
sequencing under €1,000), which is a major benefit for doing statistically
robust studies. The result has been a resurgence of common and rare genetic
approaches. The drop in sequencing cost has allowed more scalable assays, such
as RNA-seq and ChIP-seq, which let us work routinely on the scale of a whole human
genome.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">A decade ago there was a far wider gap
between experiments that were feasible on <i style="mso-bidi-font-style: normal;">Drosophila</i>,
<i style="mso-bidi-font-style: normal;">C. elegans</i> or the yeasts, but not on
human beings. The landscape has changed. <o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">Human disease</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">The global economy is a human concept
(though it affects all species) and a big chunk of it (10% to 17% in
industrialised economies) is spent on healthcare. That is a huge amount of
money. A considerable amount is already spent on clinical research, but the
advent of inexpensive techniques to measure DNA, RNA, proteins and metabolites presents
massive, new opportunities. It is now possible to blend scientific approaches
that have traditionally been separate – experimental medicine and genomics, or epidemiology
and bioinformatics – to exploit these measurement techniques alongside
traditional clinical approaches. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">The primary motivation for all this
activity and expense is to understand and control human disease. But health and
disease are constantly in flux, in humans as in all species, and often the
process of understanding human disease is really just the same as understanding
human biology – and that’s not so different from understanding biology as a
whole. Fitting all the pieces together requires taking the best from all
fields, and that is in itself a huge challenge.<o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">Traditional models, rebooted</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">There is justifiable excitement around new
opportunities to study humans as a model organism, but it is simply not the
case that the established model organisms will become less and less relevant. Placing
too strong an emphasis on Human studies could lead to inadvertently hindering
research on other organisms, which would be counterproductive. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">‘Model’ organisms help us create ‘models’
of life processes – they do not serve as ‘models’ for human organisms. Our
grasp of molecular biology is still quite basic indeed: we have a firm grasp on
only just over a third of protein coding genes in humans, and this number is
not much higher in simple, well-studied organisms such as yeast. Even in cases
where we have ‘established the function’ of a set of genes and can tie them to
a specific process, we still have huge gaps in our comprehension of how these
particular molecules can create exquisitely balanced, precise processes.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Leveraging the unique properties of
different model organisms provides opportunities to innovate. For example, one remarkable
paper demonstrates how a worm ‘thinks’ in real time, monitoring the individual
firing of each (specifically known) neuron in the animal as different cues are
past over its nose. The growing set of known enhancers in <i style="mso-bidi-font-style: normal;">Drosophila</i> allows for the genetic ablation of many cells, and the
incredible precision of mouse genetic engineering allows precise triggering of
defined molecular components. None of these experiments would be even remotely
feasible in Human. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB">We would be very foolish to take a
laser-like focus on this rather eccentric bipedal primate, however obsessed
might be with keeping it healthy, happy and long-lived.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Our understanding of development in
organisms, of homeostasis within organs, tissues and cells, and of the
intricacies of behaviour is only just starting to develop. Metaphorically
speaking, we have lit a match in a vast, dark hall – the task of illuminating
the processes that drive these molecules to create full systems (that go on to
type blog posts) is daunting, to say the least.<o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">Hedging our bets</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">There are many hard miles of molecular and
cellular biology ahead to improve our understanding of biology, with leads to
follow in many different models (including human!) using many different
approaches. This deeper understanding of biology will directly impact our
understanding of human disease in the future. We need to spread our bets across
this space.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Clinical researchers might have a harder
time managing this, as the necessary focus on Human to understand human disease
makes it all to easy to dismiss the future impact of other organisms on
understanding human biology. The majority of molecular knowledge they currently
deploy in their research is built on studies of a very diverse set of
organisms. Useful and surprising insights and technologies can be gleaned from
any organism. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Basic researchers, on the other hand, might
dismiss the advent of human biology because it places inappropriate emphasis on
applied research into the specifics of human disease. All human studies are not
necessarily translational, and in any case the interweaving between
understanding biology and understanding disease makes it impossible to really
separate these two concerns.<o:p></o:p></span></div>
<h3>
<span lang="EN-GB"><br /></span></h3>
<h3>
<span lang="EN-GB">To Human and back again</span></h3>
<div>
<span lang="EN-GB"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-GB">Over the next decade, the integration of
molecular measurements with healthcare will deepen. This will almost certainly
have a beneficial impact on the lives and health of many people worldwide. It also
provides huge opportunities for the research community – obviously for applied research
but also for curiosity-driven enquiry, as this massive part of our economies will
generate and manage information on ourselves. <o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-GB"><br /></span></div>
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<span lang="EN-GB">We should exploit this to its fullest so
that we can understand life, on every scale, in every part of the world we
inhabit.<o:p></o:p></span></div>
Anonymoushttp://www.blogger.com/profile/11426862213901409974noreply@blogger.com3tag:blogger.com,1999:blog-1688712549144760104.post-71604305275807414762015-01-19T09:14:00.000-08:002015-01-19T09:19:20.092-08:00Untangling Big Data<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: Verdana, sans-serif;">"Big Data" is a trendy, catch-all phrase for handling large datasets in all sorts of domains: finance, advertising, food distribution, physics, astronomy and molecular biology - notably genomics. It means different things to different people, and has inspired any number of conferences, meetings and new companies. Amidst the general hype, some outstanding examples shine forth and today sees an exceptional <a href="http://dx.doi.org/10.1038/nbt.3102">Big Data analysis paper</a> by a trio of EMBL-EBI research labs - <a href="http://www.ebi.ac.uk/research/stegle">Oliver Stegle</a>, <a href="http://www.ebi.ac.uk/research/marioni">John Marioni</a> and <a href="http://www.ebi.ac.uk/research/teichmann">Sarah Teichmann</a> - that shows why all this attention is more than just hype.</span></div>
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<span style="font-family: Verdana, sans-serif;">The paper is about analysing single-cell transcriptomics. The ability to measure all the RNA levels in a single cell simultaneously - and to do so in many cells at the same time - is one of the most powerful new technologies of this decade. Looking at gene regulation cell by cell brings genomics and transcriptomics closer to the world of cellular imaging. Unsurprisingly, many of the things we've had to treat as homogenous samples in the past - just because of the limitations of biochemical assays - break apart into different components at the single-cell level. The most obvious examples are tissues, but even quite "homogenous" samples separate into different constituents.</span></div>
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<span style="font-family: Verdana, sans-serif;">These data pose analysis challenges, the most immediate of which are technical. Single-cell transcriptomics requires quite aggressive PCR, which can easily be variable (for all sorts of reasons). The Marioni group created a model that both measures and accounts for this technical noise. But in addition to technical noise there are other large sources of variability, first and foremost of which is the cell cycle. </span></div>
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<span style="font-family: Verdana, sans-serif;">Cell cycle redux</span></h4>
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<span style="font-family: Verdana, sans-serif;">For the non-biologists reading this, cells are nearly always dividing, and when they're not they are usually paused in a specific state. Cell division is a complex dance: not only does the genome have to be duplicated, but much of the internal structure has also be split - the nucleus has to dissassemble and reassemble each time (that's just for eukaryotic cells, not bacteria). This dance has been pieced together thanks to elegant research conducted over the past 30 years in yeast (two different types), frog cells, human cells and many others. But much remains to be understood. Because cells divide multiple times, the fundamental cycle (the cell cycle) has very tightly defined stages when specific processes must happen. Much of the cell cycle is controlled by both protein regulation and gene regulation. Indeed, the whole process of the nucleus "dissolving", sister DNA chromosomes being pulled to either side, and the nucleus reassembling has a big impact on RNA levels. </span></div>
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<span style="font-family: Verdana, sans-serif;">When you are measuring cells in bulk (i.e. 10,000 or more at the same time), the results will be weighted by the different 'lengths of stay' in different stages of the cell cycle. (You can sometimes synchronise the cell cycle, which is useful for research into the cell cycle, but it's hard to do routinely on any sample of interest). Now that we have single-cell measurements, which presumably tell us something about cell-by-cell variation, we also have an elephant in the room: namely, massive variation due to the cells being at different stages of the cell cycle. Bugger.</span></div>
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<span style="font-family: Verdana, sans-serif;">One approach is to focus on cell populations that have paused (presumably in a consistent manner) in the cell cycle, like dendritic cells. But this is limiting, and many of the more interesting processes happen during cell proliferation; for example, Sarah Teichmann's favourite process of T-cell differentiation nearly always occurs in the context of proliferating cells. If we want to see things clearly, we need to somehow factor out the cell-cycle variation so we can look at other features.</span></div>
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<span style="font-family: Verdana, sans-serif;">Latent variables to the rescue</span></h4>
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<span style="font-family: Verdana, sans-serif;">Taking a step back, our task is to untangle many different sources of variation - technical noise, the cell cycle and other factors - understand them, and set them to the side. Once we do that, the interesting biology will begin to come out. This is generally how Oliver Stegle approaches most problems, in particular using Bayesian techniques to coax unknown, often complex factors (also called 'latent variables') from the data. For these techniques to work you need a lot of data (i.e. Big Data) to allow for variance decomposition, which can show how much each variable contributes to the others. </span><br />
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<span style="font-family: Verdana, sans-serif;">But even the best algorithm needs good targeting. Rather than trying to learn everything at once, Oli, John and Sarah set up the method to learn the identity of cell-cycling genes from a synchronised dataset - learning both well-stablished and some anonymous genes. They brought that gene list into the context of single-cell experiments to learn the behaviour of these genes in a particular cell population, paying careful attention to technical noise. <i>Et voil</i><span style="font-size: 12pt;"><i>à</i>:</span> one can split the variation between cells into 'cell-cycle components' (in effect, assigning each cell to its cell-cycle stage), 'technical noise' and 'other variation'. </span></div>
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<span style="font-family: Verdana, sans-serif;">This really changes the result. Before applying the method, the cells looked like a large, variable population. After factoring out the cell cycle, two subpopulations emerged that had been hidden by the overlay of the variable cell cycle position, cell by cell, and those two subpopulations correlated to aspects of T-cell biology. Taking it from there, they could start to to model other aspects as specific latent variables, such as T-cell differentiation.</span></div>
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<span style="font-family: Verdana, sans-serif;">You say confounder, I say signal</span></h4>
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<span style="font-family: Verdana, sans-serif;">We are going to see variations on this method again and again (in my research group, we are heavy users of Oliver's latent-variable work). This variance decomposition is about splitting different components</span><span style="font-family: Verdana, sans-serif;"> apart and showing them more clearly</span><span style="font-family: Verdana, sans-serif;">. If you are interested in the cell cycle, cell-cycle decomposition, or how certain details of factor changes differ between cell populations, it will be incredibly useful</span><span style="font-family: Verdana, sans-serif;">. <span style="background-color: white;">If you are interested in differentiation, you can now "factor out" the cell cycle. In contrast, you might only be interested in the cell cycle and prefer to drop out other biological sources of variation.</span><span style="background-color: white;"> E</span>ven the technical variation is interesting if you are looking at optimising the PCR or machine conditions. "Noise" is a pejorative term here - it's all variation, with different sources and explanations. </span></div>
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<span style="font-family: Verdana, sans-serif;">These techniques are not just about the cell cycle or single-cell genomics. Taken together, they represent a general mindset of isolating, understanding and ultimately modelling sources of variation in all datasets, whether they are cells, tissues, organs, whole organisms or populations. </span><span style="font-family: Verdana, sans-serif;">It is perhaps counter-intuitive to consider that if you have enough samples with enough homogenous dimensions (e.g. gene expression, metabolites, or other features), you can cope with data that is otherwise quite variable by splitting out the different components. </span><br />
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<span style="font-family: Verdana, sans-serif;">This will be a mainstay for biological studies over this century. In many ways, we are just walking down the same road that the founders of statistics (Fisher, Pearson and others) laid down a century ago in their discussions on variance. But we are carrying on with far, far more data points and previously unimaginable abilities to compute. Big Data is allowing us to really get a grip on these complex datasets, using statistical tools, and thus to see the processes of life more clearly. </span></div>
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