DNA Compression: Reprise

Around a year ago I wrote three blog posts about DNA sequence compression, based on the paper my student and I published early in 2011 and the start of cramtools by Guy Cochrane’s ENA team under the engineering lead of Rasko Leinonen, which is the practical implementation of these ideas. The previous blog posts first describe the thought process of compression, in particular the shift from understanding what information you wish to keep to what information you know you are happy to discard, then the practicalities of compression and the balancing between the different communities in the tool chain that need to work with it, and then finally whether compression is a credible answer for the DNA sequencing technology that doubles its cost effectiveness somewhere between every 6 to 12 months.

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Epidemiology for molecular biologists

As more and more of research into human health is switching to human subject research, including a considerable amount of molecular work, molecular biologists and bioinformaticians are going to have to get far more comfortable with epidemiology than ever before. There are some big elephant trap like mistakes you can make – and indeed I see people making them right now (and, embarrassingly, I’ve stepped in one of two of these traps myself; one lives and learns ). One reason why there are so many traps for the unwary is because our first collective foothold in epidemiology is (in effect) genome wide association studies. This has the rare property that one of the key variables being measured (genotypes) both (a) do not change over time and (b) are relatively uniformly distributed relative to other things we can measure about people. I’ll return to this below.

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East meets West (Science)

A couple of weeks ago, in sunny florida at AGBT, recovering from a somewhat too hard evening’s … “discussion” I chatted to someone about the future of sequencing. We ranged across the models of large scale centres vs smaller units; of the never-ending need of bioinformatics and trying to stay on top of everything. “Oh well” my lunch companion continued, “we’re all going to be competing with a billion chinese, so that will solve the informatics problem”. I was reminded about this only this weekend when someone from the Oil and Gas industry asked me about the scientific impact of China, citing the opportunity but also perceived threat to business.
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Career path: a bit more detail.

Last month, Rolf Apweiler and I were kindly asked by Nature Jobs to comment on our career paths to becoming Associate Directors at the EBI (which will happen in April). The final piece focuses on just one of our career paths (mine). You can read the article here: http://www.nature.com/naturejobs/science/articles/10.1038%2Fnj7383-123a

It is very positive about me, about the EBI and about the other institutions I went through. It’s a good thing, mainly because it raises the EBI’s profile. However, some important things were missed that I would like to mention here – in particular, I think it’s important to acknowledge the people I’ve been lucky enough to work with over the years, and who have made a positive impact on my career.
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My Emotions in Science

First off, apologies that I have not blogged recently. This is because I have been pushing hard on a large, consortium paper, and it felt bad to have a public evidence of not working on it when I was asking many other people for pretty continuous effort over a protracted period of months. This is not entirely logical – we all actually need some diversity in what we do scientifically, even when we’re on a big push, but somehow makes sense sociologically.

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Customising Ensembl Displays

Ensembl’s display customisation has become both far more detailed, and far easier toaccess. I’m goingto talk you through how to customise and what my favourite view is.

The default of Ensembl display is something like this. Nice, pretty, but very transcript/gene centric, and with a strong emphasis on splitting up the strands. I am now a more regulatory person, and genes really form the context of what I want to see, but most of the time I don’t want to see the details of the transcripts.

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Biology is a 1st class quantitative science

Last week I was at a “Town Hall” meeting for the UK High Performance Computing community (or at least part of it) which has a lot of computer people rubbing shoulder with what used to be called “hard” science scientists; high energy physics, astronomers, modellers (particularly climate change/oceanography) and the odd molecular modeller/chemist. High beard/non beard ratio, and almost 1:1 Y to X chromosome ratio. From their perspective I was a straight up biologist (though from the biologist’s perspective, I am a computer scientist. The curse of being interdisciplinary is that you are often seen as on the outside of all the disciplines you span. Hey ho.).

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