Chinese Lab Speeds Through Genome Processing With GPUs 408
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."
A better article (Score:5, Informative)
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.
Re:This article is almost painfully dumbed down... (Score:5, Informative)
The summary is pulled directly from the top of the article.
Here's the article from HPC Wire [hpcwire.com] and some details from nvidia [nvidia.com] as well as the nvidia press release [nvidia.com]
Re:A better article (Score:5, Informative)
Part of the problem is Low Standards (Score:4, Informative)
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.