Author Archives: Luke

Alright, it’s time to address the meat of the matter of AGBT; the state of play of sequencing technology. I’ll go through each of the major companies in turn, and talk about what they’ve brought to the table, and what the future holds for them.

As usual, for more in depth information you can follow me on twitter (@lukejostins). Other coverage can be found on Genetic Future, MassGenomics, Fejes, GenomeWeb and Bio IT World.

Illumina

I covered Illumina on day zero. Basically, the GAIIx can now generate 7Gb/day, with 2x150bp, and error rates universally under 2%. The HiSeq generates 31Gb/day, 2x100bp, with error rates under 1%; this will soon be pushed to 43Gb/day with a slight decrease in accuracy. For sheer volume of sequence, no-one can match Illumina.

454

As I said yesterday, 454‘s median read lengths are climbing into the 700-800 range, but with error rates being pretty high beyond 600 or so. Not bad, but after all the fuss over 1000bp reads, also a little disappointing.

454 have been pushing their work on assembly; they’ve worked pretty hard to make an easy-to-follow recipe, involving both single-end and paired-end sequencing, and the program Newbler. Many interesting critters have had this treatment, including bonobo, panda and Desmond Tutu (in order of majesty).

SOLiD

I found the SOLiD content of this conference very cool. Focusing more on the medical genomics side of things, SOLiD is involved in various clinical trials to see whether genomic information can increase cancer survivial times, and emphasizing the importance of accuracy in a clinical setting.

Lots of cool new tech too: For instance, mixing 2-base and 1-base encoding, apparently making error rates of 1 in 10^6 possible. Apparently library prep errors now dominate, so SOLiD has been working on finding more gentle enzymes for amplification. Particularly cool was a throw-away slide on running the ligase on single molecules and actually getting signal (though actual single-molecular sequencing probably isn’t economic).

Pacific Biosciences

Pacific Biosciences have produced an extremely interesting product; it is a game-changer, though exactly what it means for sequencing is not immediately obvious. I am going to hold back on writing about PacBio right now, because I have a more in-depth post on the exact specs and implications of the PacBio, in comparison to their nearest equivalent Oxford Nanopore, in the works.

Complete Genomics

Complete Genomics have gone from “interesting idea” to “thriving technology” in a very short period of time. They’re scaling up their sequencing centre as we speak; they’ll have 16 machines in the next few months, generating 500 40X genomes a month. Over the year, providing they get more orders, they’ll scale up to 96 machines, with a predicted 5X increases in capacity per machine as well. If this all goes well, in theory they are on target for their 5000 genomes by the end of the year.

Complete also have some very interesting new technologies on the horizon, which they will be discussing tomorrow; check the twitter feed for coverage. A lot of people underestimate Complete Genomics, but it is starting to become evident that they are as much game-changers as more flash technology.

Ion Torrent

Ion Torrent wins both my major awards this year: the “most surprising release” award and the “sounds most like a soviet weapons project” award. Ion torrent burst onto the scene with its tiny machine (GS Junior sized); the first major non-florescence-based method in a long time, using the emission of hydrogen ions from the the DNA polymerase reaction to measure incorporation in a 454 stylee.

The machine can produce a rapid 150Mb or so in a single hour run, for about $500 in disposables. The machine itself costs a tiny $50k. From what I’ve heard, a lot of people are interested in a machine like this for fast library validation, though it also has applications in diagnostics and microbiology. Unfortunately, it looks like the error rates are currently high, though they claim these will drop by release time.

Summary

Overall, we are starting to see a divergence in sequencing technologies, as each tech concentrates on having clearly defined advantages and potential applications that differ from all others. This means that the scientists themselves can more closely tailor their choice of tech to fit their situation. Are you a small lab that needs 10 high-quality genomes on a budget? Go to Complete. Want a cheap, fast machine for library validation? Use Ion Torrent. Setting up a pipeline for sequencing thousands of genomes? Go Illumina.

I suppose this was all driven by the fact that Illumina’s machine has such high yield that chasing them is a fool’s game, so everyone else is concentrating on what they can do that Illumina doesn’t. This is pretty good for science as a whole; we are moving away from the One-Size-Fits-All approach to high-throughput sequencing, and moving into a time of more mature, application-based methods.

The second day (or the first day, depending on if you count yesterday’s pre-sessions) of AGBT is nearly done. There has been a lot of things going on today, but I’m only going to cover one; once again, you can get more detail on all the talks I’ve seen on my Twitter feed (@lukejostins).

Other things that I’ve done: I had a very interesting talk with Geoff Nilsen at Complete Genomics, in which I got to ask various questions, including: “Why don’t they use color-space?”, “It confuses customers, and the error model is good enough already”. “In what sense is Complete ’3rd Gen’?”, “Because it’s cheaper”. I also saw a set of presentations from 454 on de novo assembly, and the new Titanium 1k kit, which actually contains virtually no 1kb reads: mean read length is about 800bp, but beyond 600 the error rates get very high.

There has been some other blog coverage of AGBT from our army of bloggers: MassGenomics has some first impressions, and Anthony Fejes is uploading his detailed notes about all the talks. You can also follow a virtual rain of tweets on the #AGBT hashtag.

Fun with Exome Sequencing

Debbie Nickerson (again!) gave a talk about sequencing genomes to hunt down the genes underlying Mendelian disorders. The process is very simple; you sequence a 4-10 exomes of suffers, look for non-synonymous mutations shared in common between them, and then apply filters (such as presence in HapMap exomes) to find SNPs that are likely to be causal. Debbie is in the process of sequencing 200 exomes for 20 diseases, and has a real success story under her belt in tracking down the genes for 2 disorders. She raised the interesting question of how to validate the discovered genes, given that Mendelian disorders tend to have a large number of independent mutations.

Stacey Gabriel gave a related talk on exome sequencing, focusing on using the method Debbie described to track rare variants for complex traits. To do that, you ‘Mendelianise’ the trait, by only picking extreme individuals; She did this for high and low LDL-choloresoral, giving some candidate genes, but no smoking gun.

Let’s look slightly closer at this; you sequence a number of individuals with extreme traits, look for genes with shared non-synonymous mutations, and look for functional effects. This is a linkage study! A very small and underpowered linkage study, with a variant-to-gene collapse method (like a poor-mans lasso), and some sort of manual pathway/functional analysis (a poor-mans GRAIL), but linkage all the same. This is really re-inventing the wheel, without really learning any of the lessons that the first round of linkage analysis taught us (or even stopping to ask whether, if such variants existed, they would have been picked up by linkage in the first place).

It is not that Stacey Gabriel is doing anything wrong; it is just that she is failing to consider that she is attempting to solve non-statistically a problem that statisticians have worked on for decades. In short, she is risking taking the statistics out of statistical genetics.

A late Merry Christmas, and a marginally early new year to all. A few Christmas-based observations, from various lines of thought that have been knocking around my head over the festive period.

Cooking and the Internet

The internet can sometimes birth amazingly useful things in unexpected fields. The one I am thinking of at the moment is in cooking; I have been cooking a lot of food this Christmas, and I have built up a reputation in my family for being someone who can make traditionally ‘complicated’ things (pie crust, Yorkshire pudding, etc). To clarify, I am not a good cook, or at least the food I invent myself is not liked by others (I like my mustard and chili pasta sauce, or my three-mushroom fried rice, but no-one else seems to). However, I have been shown by some more food-literature friends of how the internet can turn someone like me into a competent chef.

The hidden secret is the BBC Good Food website (which is distinct from the BBC Food website, presumably dedicated to bad food). The website consists of a crazily large number of recipes written by professionals; however, the real secret is that there is a very dedicated readership of amateur cooks who report their experiences, and rate the recipes on a scale of 1-5. It is this later part that really makes the website great; while a random recipe from a chef will often be relatively good, those that have been rated as 5 stars by the community are, virtually without fail, excellent.

The interesting thing is that many of the recipes look very weird at first, but turn out to work amazingly well. Cut the skin off gammon, and cut slits before roasting? Add flour to the filling of an apple pie? Pastry that should ‘look like scrambled egg’? These are the sort of thing that make you look to your family like an expert cook; you end up doing things that to them (and indeed you) look like madness, despite actually working.

Keynesian Christmas

I read a fun and insightful essay, reprinted at OpenDemocracy, about the Keynesian economic bases of a Christmas Carol. The idea is that the early 1840s were a time of deflation; the value of money was shrinking, and the value of goods were shrinking faster still. The economy was sinking into recession; deflation meant that investment wasn’t worth while, but because goods were worth less each year, people avoided buying things as well. Added to this was a Malthusian attitude that the world was too full to support population growth, and that saving and parsimony was the order of the day.

These fears combine to make a villain that is both indicative of, and the cause of, the recession. The miserly rich man, fearful of financial uncertainty, who hoards money without spending it either on themselves or others. And when Scrooge learns the spirit of Christmas, he also learns to be the sort of person that the economy needs for recovery; someone who gives and spends without thought for the cost, who buys things for the sheer joy of doing so, not because they are good value or even needed.

There is a similar feel to the carol Good King Wenceslas, which was also written in the 1840s; the Saint, upon seeing a poor man in the cold, on a kind-hearted whim calls out for flesh, wine and firewood to make a feast for the peasant. It is the spontaneity, the lack of economic calculation, that makes him a Saint; he spends on others for the sheer joy of doing so.

Oddly, these values are close to what we now call consumerism; buying things for the sake of it, not because they make your life better. This ties nicely into a post by Ed Yong; consuming goods, spending on yourself, does not give you happiness (most of us have more than we need anyway). However, spending on others, like Scrooge or Saint Wenceslas, can bring you happiness.

Saint Nicholas

As a final Christmas thought, before I put away childish things for the year, is this: Has anyone ever considering going to the Basilica di San Nicola at Christmas Day, in order to visit Santa Claus’ grave? One for the kids, perhaps.