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Sep 1, 2012 (Vol. 32, No. 15)

Big Data Requires Big Solutions

  • The GGA can also be complemented by the company’s second turnkey solution, the Genomatix Mining Station (GMS), which does high-performance NGS mapping. Each GMS node houses 64 gigabytes of memory to be able to handle the terabytes of data, which is necessary to cope with the NGS data output. The GMSs can be scaled-out, just like EMC Isilon units, to match the ever-growing volume of raw data produced by NGS sequencers. The interfaces of both machines are web-based and can be accessed by multiple users without the need to transfer big data.

    The company also plans to offer its tools via Illumina’s BaseSpace apps later this year to accommodate users with limited datasets, such as benchtop sequencers.

    In collaboration with the NIH, Genomatix software helped to discover synergies between several transcriptional regulatory factors directly influencing maintenance of mammalian photoreceptors. Using chromatin precipitation data and GGA technology, complex regulatory maps of rod and cone genes were assembled. These maps can model the effect of regulatory factors on photoreceptor expression and, thus, better understanding of retinal neurogenerative diseases.

    Another key collaboration with the Center for Prostate Disease Research revealed an early biomarker that may predict future metastatic progression of prostate cancer. Genomatix is invested in academic research collaborations, and is an SME partner in various consortiums like BLUEPRINT (generating 100 reference epigenomes), m4 Personalized Medicine (gene network analysis for prognostics and diagnostics on personalized basis), SYNERGY-ERASysBio+ (characterizing roles of nuclear receptors), and MedSyS (signaling in pluripotent stem cells).

    “Our approach facilitates translation of genetic data into biomedical knowledge, and we’re working hard to get it into diagnostics,” concluded Dr. Seifert.

  • Managing All that Data

    VIDEO

    Bob Masson, Convey Computer’s director of marketing, explains hybrid-core computing and how it helps researchers manage and process the expanding volumes of next-generation sequencing data.

    The Jackson Laboratory boasts roughly 5,000 strains of mice. It uses these models to conduct next-generation sequencing (NGS) analysis for a variety of purposes such as discovery of spontaneous mutations, strain-specific variation, and genome-wide analysis of gene expression. This year it added a Convey Computer hybrid-core computer to its lab with the goal of speeding current research and enabling scientists to undertake whole-genome studies that were previously impractical.

    “Once we could afford whole-genome sequencing, we found a significant bottleneck in the time required to process the data,” says Laura Reinholdt, Ph.D., a research scientist at the Jackson Laboratory. “That’s when biologists here began to seek tools and infrastructures to more expediently manage and process the expanding volumes of NGS data.”

    Data overload has become a growing problem when it comes time to analyze results of NGS experiments. The hybrid-core architecture of the Convey system aims to ease the bottleneck by pairing classic Intel processors with a coprocessor composed of field-programmable gate arrays (FPGAs). Particular algorithms—DNA sequence assembly, for example—are optimized and translated into code that’s loadable onto the FPGAs at runtime, accelerating performance-critical applications.

    Dr. Reinholdt’s group has used high-throughput sequencing to improve mouse models of ALS, Down syndrome, and Alzheimer’s disease. Performing alignment on the laboratory’s 32-core servers was a slow process, she says, noting that the HC-2’s higher throughput gives researchers more flexibility to adjust parameters, quickly perform multiple runs, and achieve better results.

    “You can end up spending weeks just trying to find the right parameters. If you can do two or three alignment runs in parallel, optimization of the alignment becomes much less time consuming,” comments Dr. Reinholdt.


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