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Chinese Lab Speeds Through Genome Processing With GPUs
Eric Smalley writes "The world's largest genome sequencing center once needed four days to analyze data describing a human genome. Now it needs just six hours. The trick is servers built with graphics chips — the sort of processors that were originally designed to draw images on your personal computer. They're called graphics processing units, or GPUs — a term coined by chip giant Nvidia. This fall, BGI — a mega lab headquartered in Shenzhen, China — switched to servers that use GPUs built by Nvidia, and this slashed its genome analysis time by more than an order of magnitude."
Sounds like these newfangled "GPUs" are gonna change the world.
I always wondered what GPUs are. Thanks Slashdot!
Explaining what a GPU is in a slashdot summary? Come on.
This is similar to someone telling you a story about something funny happening to them while shopping at the store, pausing mid-story to inform you that a 'store' is a business where goods are displayed and exchanged for a papery substance called 'money'.
It's hardly news that GPUs can be used to speed up parallel tasks/computations, but even so this article is a useful reminder of two things; 1) there are still many important processes that can be sped up by using GPUs, and 2) this can be achieved pretty much anywhere in the world.
Excerpt:
At BGI, he says, they are currently able to sequence 6 trillion base pairs per day and have a stored database totaling 20 PB.
The data deluge problem stems from an imbalance between the DNA sequencing technology and computer technology. According to Dr. Wang, using second-generation sequencing machines, genomes can now be mapped 50,000 times faster than just a decade ago. The technology on track to increase approximately 10-fold every 18 months. That is 5 times the rate of Moore's Law, and therein lies the problem.
Obviously it would be impractical to upgrade one's computational infrastructure at that rate, so BGI has turned to NVIDIA GPUs to accelerate the analytics end of the workflow. The architecture of the GPU is particularly suitable for DNA data crunching, thanks to its many simple cores and its high memory bandwidth.
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The summary is pulled directly from the top of the article.
Here's the article from HPC Wire and some details from nvidia as well as the nvidia press release
So, a site dedicated to nerds needs to explain what a GPU is? Are we not nerds anymore?
Write boring code, not shiny code!
Although at least in my field the problem is that no one ever thought to set lower limits on the quality of what you can call a genome. So now we get "genomes" made up of 100,000 contigs (many only a couple of hundred base pairs long) and even counting all of those, the total sequence might account for only 70% of the total size of the genome. But it's still a "genome" paper, which is still an instant ticket to Nature Genetics (or Nature Biotechnology if the assembly is REALLY bad).
BGI is certainly one of the biggest offenders (Cucumber and Pigeonpea are both examples of the sort of terrible genomes-in-name-only BGI puts out) but I think the real problem is that Illumina sequence data is so cheap people keep trying to use it to sequence genomes, thinking if they throw enough raw data and enough mate-pair libraries at the problem it'll eventually make up for the fact that Illumina reads are so short. Illumina data is great for a lot of things. Calling SNPs, measuring gene expression, studying methylation patterns.
But, at least for any genome significant transposon content, it simply does not work.
... this is what a Chinese lab looks like.
Ah, arrogance and stupidity, all in the same package. How efficient of you. -- Londo Mollari