Cargo Cult Science and NT Factor®

A recent blog post on Chronic Fatigue Syndrome linked in passing to a ‘treatment’ called Mitochondria Ignite™ with NT Factor®. This product caught my attention as an example of what Richard Feynman called ‘Cargo Cult Science’; a company dressing up like scientists, using chemical names and precise sounding figures, without actually having any science underlying it.

However, the product is not arguably not exactly pure Cargo Cult Science; there is a small amount of science content present. The product page contains a number of references, some of which point to peer review journals, and some of which are actually studies of the effect of some of the contents of the drug on humans. Of cours,e taking apart the studies shows that the product is still unproven, despite the thin glaze of real science; I can’t help but feel that this sort of thing has slightly grim implications for the future of accurate consumer information.

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The Future of Second Generation Sequencing

Illumina, the major player in high-throughput sequencing these days, have announced the newest version of their second generation sequencing platform, the HiSeq 2000. The machine can produce a lot more sequence, and at lower cost, than the previous Genome Analyzer II.

I’m not going into much detail about the machine: for that, see posts at Genomics Law Report, Genome Web, Genetic Future, Pathogenomics and PolITiGenomics. What I really care about is what this machine implies for the future of sequencing, and specifically what we can predict about the coming 2nd verses 3rd generation sequencing battles that will be kicking off later this year.

PacBio’s 3rd generation machine, which will be arriving later this year, will have an initial throughput of around 3Gb a day, at a price of around 1.4$ per Mb in consumable costs. I don’t know the specs for Oxford NanoPore’s machine; my guess is that it will be similar, but we’ll know soon.

Compare PacBio’s capacity to the HiSeq 2000, which will produce 25 Gb per day, at claimed consumables price of $0.11 per Mb ($10 000 for a 30X genome). In short, the Illumina 2nd gen machine is going to be able to pump out much more sequence at a much higher rate than PacBio. Both will rapidly increase the power of their machines after release, but we don’t know who will push faster (Dave Dooling thinks Illumina could push the HiSeq to 450 Gb per run with existing technology).

Of course, the competition isn’t just based on pure throughput. Read length and error rates are also important; the 3rd gen machines will also have much longer read lengths than Illumina and SOLiD, and we expect that the quality of sequence will be higher as well, giving the possibility of some real Gold Standard genomes being produced from these machines, rather than the somewhat messy genomes we get from Illumina.

This all ties in to the conversation I had with the Illumina people at ASHG; Illumina think that it’ll be a good few years before 3rd Gen sequencing can catch up with their current machines. I expect that, between now and 2014 (when PacBio release v2 of their machine), the major sequencing centres will keep a combination of 2nd and 3rd gen machines. The 2nd gen machines will be used when a very large amount of low-quality sequence is required, such as for Genome-Wide Association Studies or RNA-seq. The 3rd gen machines will be used for assembling genomes, looking for copy-number variations and studying the genetics and epigenetics of non-coding and repetitive regions.

I guess what I’m trying to say is that, as exciting and cool as the single-molecule technologies of PacBio and Oxford NanoPore are, it is far too soon to announce the death of Second Gen sequencing. If Illumina continues to push its throughput as hard as it is doing now, 2nd generation machines will be widely used for a long while yet.

The future will become a bit clearer at the AGBT conference, where we should see some big announcements from PacBio, Oxford Nanopore, Complete Genomics, ABI and Illumina. Me and a host of other bloggers will be there to cover them.

Christmas Thoughts

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.

The Economist Mangles Disease Genetics

The Economist has a rather distressingly bad article by the evolutionary psychologist Geoffrey Miller, about the supposed general failure in human disease genetics over the last 5 years. The thesis is that Genomes Wide Association Studies (GWAS) for common diseases have been a failure that geneticists are trying to keep hidden, and that the new techniques required to solve the problem of disease genetics will raise ‘politically awkward and morally perplexing facts’ about the different traits and evolutionary histories of races. The former claim is pretty much the same as Steve Jones Telegraph article earlier this year, and is just as specious. I will look at both claims separately.

A quick point of terminology: Miller uses ‘GWAS’ to refer to studies that look for disease association in common variants using a genotyping chip, and acts as if sequencing studies are not, in fact, GWAS. In fact, a sequencing association study is just another type of GWAS, just looking at a larger set of variants.
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How Many Ancestors Share Our DNA?

This post was written four years ago, using a quick-and-dirty model of recombination to answer the question in the title. Since then a more detailed and rigorously tested model has been developed by Graham Coop and colleagues to answer this same question. You can read more about the results of this model on the Coop Lab blog here and here. Graham’s model is based on more accurate data, more careful tracking of multiple ancestors and a more realistic model of per-chromosome recombination, and thus his results should be considered to have superseded mine.

Over at the Genetic Genealogist, Blaine Bettinger has a Q&A post up about the difference between a genetic tree and a genealogical tree. The destinction is that your genealogical tree is the family tree of all your ancestors, but your genetic tree only contains those ancestors that actually left DNA to you. Just by chance, an individual may not leave any DNA to a distance descendant (like a great-great-great-grandchild), and as a result they would not appear on their descendant’s genetic tree, even though they are definitely their genealogical ancestor.

At the end of his post, Blaine asks a couple of questions that he would like to be able to answer in the future;

  • At 10 generations, I have approximately 1024 ancestors (although I know there is some overlap). How many of these ancestors are part of my Genetic Tree? Is it a very small number? A surprisingly large number?
  • What percentage, on average, of an individual’s genealogical tree at X generations is part of their genetic tree?

I think that I can answer those questions, or at least predict what the answers will be, using what we already know about sexual reproduction.
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ASHG: Quantifying Relatedness and Active Subjects in Genome Research

Well, the American Society of Human Genetics Annual Meeting is coming to a close for another year. My talk is done and dusted, so I no longer have to lie awake at night worrying that I will forget everything other then the words to “Stand By Your Man” when confronted by the crowd. My white suit is now more of an off-white suit, with regions of very-off-white and pretty-much-entirely-out-of-sight-of-white. I’m looking forward to getting back home to catch up on my sleep.

For the last time, I’m going to give a little summary of talks today that I thought were interesting, or gave some indication of where genetics may be heading in the future. I will write up some more general thoughts about the meeting in the next few days, as soon as the traveling is out of the way and my mind has recharged.

If you would like some second opinions on the conference, GenomeWeb has a number of articles, including a couple of short summaries, as well as a nice mid-length article about the 1000 Genomes session; there are also a number of articles over at In The Field, the Nature network conference blog.
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ASHG: Finding Mendelian Mutations and Inclusive Population Genetics

Third day down, one to go. I am starting to suffer from conference fatigue somewhat. I’m not going to any other talks this evening, so I am going to try and get some relaxation time in from this point on. But first; the summary of Day 3.

Today I saw a lot of talks over three sessions, and many of them were very interesting. However, I won’t talk about everything, or even my favourite talks. I’ll go for the talks that seem to tie together into nice stories about a few directions genetics seems to be heading in.
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ASHG: Statistical Genomics and Beyond GWAS in Complex Disease

The second day of the American Society of Human Genetics Annual Meeting is drawing to a close; here’s a lowdown of what talks I’ve enjoyed today.

Remember, follow @lukejostins on Twitter if you want more up-to-the-minute details on the ASHG talks.
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ASHG: Chatting with the Sequencing People

While I am here, I though I’d take the chance to chat to the people at the booths for the three major Second Gen sequencing platforms (Illumina, SOLiD and 454). It was surprisingly fun, the guys I talked to all seemed enthusiastic, and it was nice to find out where the scientists in the companies think the technology is going.

In the interests of openness: the 454 booth gave me a cool T-shirt and poster, so this may well have biased my opinion of them
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ASHG: Rare Variants, and the 1000 Genomes Project

Hello all (it is taking every bone in my body not to say ‘Aloha’ here).

So, today was the first real day of the ASHG Annual Meeting; after accidentally falling asleep for basically all of yesterday, it was good to finally see some familiar faces and dig my teeth into some real science.

I’m going to write a little about the first couple of sessions I’ve seen, and say what sort of themes are being shouted loud enough to get into my jetlagged mind. I have also been tweeting the conference at quite a high frequency (about 30 tweets so far), and in more detail than I have given here; follow me on @lukejostins if you are interested. To see all the ASHG twittering, check out #ASHG2009.

The blogs posts over the next few days will be aimed mostly at those who are, at least vaguely, In The Know about genomics. However, if there are people who would like a less jargonistic lowdown of the conference, please leave a comment and I’ll see what I can do.
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