Monthly Archives: December 2011

Sequencing for relics from the Sanger era part 1: getting the raw data

Sequencing in the good old days

In another life, way back in the mists of time, I did a Ph.D. Part of my project was to sequence a bacterial gene which encoded an enzyme involved in nitrite metabolism. It took the best part of a year to obtain ~ 2 000 bp of DNA sequence: partly because I was rubbish at sequencing, but also because of the technology at the time. It was an elegant biochemical technique called the dideoxy chain termination method, or “Sanger sequencing” after its inventor. Sequence was visualized by exposing radioactively-labelled DNA to X-ray film, resulting in images like the one at left, from my thesis. Yes, that photograph is glued in place. The sequence was read manually, by placing the developed film on a light box, moving a ruler and writing down the bases.

By the time I started my first postdoc, technology had moved on a little. We still did Sanger sequencing but the radioactive label had been replaced with coloured dyes and a laser scanner, which allowed automated reading of the sequence. During my second postdoc, this same technology was being applied to the shotgun sequencing of complete bacterial genomes. Assembling the sequence reads into contigs was pretty straightforward: there were a few software packages around, but most people used a pipeline of Phred (to call base qualities), Phrap (to assemble the reads) and Consed (for manual editing and gap-filling strategy).

The last time I worked directly on a project with sequencing data was around 2005. Jump forward 5 years to the BioStar bioinformatics Q&A forum and you’ll find many questions related to sequencing. But not sequencing as I knew it. No, this is so-called next-generation sequencing, or NGS. Suddenly, I realised that I am no longer a sequencing expert. In fact:

I am a relic from the Sanger era

I resolved to improve this state of affairs. There is plenty of publicly-available NGS data, some of it relevant to my current work and my organisation is predicting a move away from microarrays and towards NGS in 2012. So I figured: what better way to educate myself than to blog about it as I go along?

This is part 1 of a 4-part series and in this installment, we’ll look at how to get hold of public NGS data.
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#arseniclife: the genome

It’s about one year since the science story dubbed #arseniclife hit the headlines. November 30th saw the release of a draft genome sequence for Halomonas sp. GFAJ-1, the bacterium behind the furore.

As Iddo pointed out on Twitter, sequencing the DNA from GFAJ-1 is itself strong evidence against arsenate in the DNA backbone, since the sequencing chemistry would be highly unlikely to work in that case. However, if like me you think that a new microbial genome provides the most fun to be had in bioinformatics [*], you’ll be excited by the availability of the data.

In this post then: where to get it, some very preliminary analysis and some things that you might like to to with it. Projects for your students, perhaps.

[*] note to self: why, then, am I working on colorectal cancer?
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A Friday round-up

Just a brief selection of items that caught my eye this week. Note that this is a Friday as opposed to Friday, lest you mistake this for a new, regular feature.

1. R/statistics

  • ggbio
  • A new Bioconductor package which builds on the excellent ggplot graphics library, for the visualization of biological data.

  • R development master class
  • Hadley Wickham recently presented this course on R package development for my organisation. I was on parental leave at the time, otherwise I would have attended for sure.

2. Bioinformatics in the media
DNA Sequencing Caught in Deluge of Data

I described this NYT article as a “surprisingly-good intro article“. Michael Eisen described it as “kind of silly“.

I think we’re both right. Michael’s perspective is that of an expert in high-throughput sequencing data; I’m just pleased to see an introduction to bioinformatics for non-specialists in a mainstream newspaper. And I note that they have corrected the figure caption which offended Michael.

As to the “deluge”: yes, there are other sciences that generate more data and yes, we probably don’t need to archive/analyse a lot of the raw data. However, I’d contend that the basic premise of the article is correct: we are sequencing faster than we can analyse. The solution, obviously, is more bioinformaticians.