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Since the human genome was mapped in 2003, researchers have made huge strides in genomics. The area of genetics in which the entirety of an organism's hereditary information is studied, genomics holds the key to personalized medicine and fine-grained insight into diseases. However, the science faces hurdles. Among them is managing the enormous amount of data required to evaluate genetic sequences.
To meet this challenge, GPUs — which today are used in areas as varied as PC gaming and weather prediction — are being deployed in genomics research. GPUs use parallel processing to break down complex computing problems into many smaller tasks that run simultaneously. In genomics and related fields where large-scale datasets are the norm, computing tasks can be handled in dramatically less time. In some cases, projects that once required supercomputers can now be run on individual machines.
Companies are fast at work to create new GPU-based technologies to advance the field. And researchers are beginning to put them to use in the most vital situations. The following examples are indicative of the efforts taking place:
The Beijing Genomics Institute (BGI), which contributed to the E.coli work and is one of the largest sequencing sites in the world, is using GPUs to speed up other genomics-related applications. For gene alignment, the Institute developed SOAP (Short Oligonucleotide Analysis Package), a free downloadable software program. Running on GPUs, SOAP can handle certain tasks up to 30 times faster than CPU-based methods. This will have dramatic impact for a range of applications, from medicine to agriculture, currently being worked on at BGI.
North Carolina-based Accelerated Technology Laboratories is also contributing to the burgeoning field of genomics. The company developed SeqNFind®, a hardware/software scalable cluster solution that leverages GPUs to address the need for fast, complete and accurate alignments of many small sequences against entire genomes.
"As next generation sequencing data grows into the realm of terabytes, scientists need tools to quickly locate point mutations, identify probe binding sites, or simply compare data," said Dr. Christine Paszko, vice president of sales and marketing at ATL.
ATL is working with several collaborators, including the University of North Carolina in Charlotte, to leverage wet chemistry in order to validate discoveries recently found via SeqNFind algorithms. "The findings are exciting and will unquestionably cause a paradigm shift in the bioinformatics industry," said Paszko.
For its part, NVIDIA developed the Tesla Bio Workbench, an online resource that turns a standard PC into a computational laboratory capable of running the complex bioscience codes of DNA sequencing. The workbench supports key programming languages and completes computing tasks more than 10-20 times faster than traditional methods.
At Virginia Tech, researchers are building a genome analysis platform that will make it easier to identify mutations and understand how cancer gets started in DNA. The GPU-based platform will be able manage the deluge of data produced by today's next-generation sequencers. Over time, the platform will be made broadly available as an open-source project. The NVIDIA Foundation granted Virginia Tech $100,000 for this project as part of its Compute the Cure initiative, which aims to support cancer researchers in the search for a cure.
"The advent of next-generation sequencing technologies promises to increase public genomic data by 10-fold every 18 months," said Wu Feng, associate professor of Computer Science and Department of Electrical & Computer Engineering at Virginia Tech. "In turn, the computational requirement for routine sequence analysis is expected to surge by 100-fold every 18 months, an astounding growth pace. By leveraging GPUs, we can accelerate computations that currently take hours and even days into near real-time, and in turn promote interactive investigation."
With the map of the human genome in hand and a constant stream of data flooding in, researchers using GPUs are advancing genomics to tackle the most formidable issues in healthcare.