Seeking a silver lining

Golden Helix, Expression Analysis work together on RNA sequencing

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BOSEMAN, Mont.—Golden Helix Inc. and Expression Analysis areteaming up to
develop an affordable and streamlined cloud-based analyticsolution that reduces the barriers to adoption of RNA sequencing.
RNA sequencing is quickly becoming a tool of choice for geneexpression studies, as it can facilitate the investigation of phenomena beyondthe reach of traditional microarrays, such as novel transcripts and isoforms,alternative splice sites and allele-specific expression. Additionally, itprovides greater coverage and higher quality genetic data than microarrays.
Golden Helix will implement Expression Analysis' secondaryanalysis pipeline technology in a scalable, robust, user-friendly andcloud-based architecture, and then build tightly coupled analytic tools thatnot only facilitate researchers' access to that data, but also enable andinspire them to make otherwise impossible discoveries. When users are ready to"make sense" of this data (tertiary analysis), Golden Helix will providedifferential expression workflows optimized for RNA-Seq data in its SNP &Variation Suite (SVS) alongside additional workflows for DNA variant analysisand genetic association testing. 
Expression Analysis will provide customers with acomplimentary, desktop-based Genome Browser designed to supplement the analyticworkflows offered in the cloud.
Steve McPhail, president and CEO of Expression Analysis,says Golden Helix proved to be a good partner for the collaboration because ofits "experience in creating outstanding genomic browsers and in tertiaryanalysis of DNA sequencing datasets."
Golden Helix CEO and President Dr. Christophe Lambert sayshis company needed a partner with a solid presence "upstream" of informatics,and one with similarly close ties into academic and commercial researchers, andExpression Analysis fit the bill.
"Given our focus on accuracy and reliability, though, we alsoneeded to find an organization as obsessed with quality and continuousimprovement as we," he says.
McPhail notes that there are several issues with RNA-Seq asit currently exists.
"Many of our clients' bioinformatics pipelines have not beenoptimized to deal with sequencing datasets," he explains. "These datasets tendto be extremely large and require a different scale of storage and computeinfrastructure. By putting our bioinformatics pipeline on the cloud andcreating web-based tools for data browsing and analysis, we believe we overcomemany of the current structural limitations associated with large sequencingdatasets."
Lambert points out that while cost long has been the primaryconcern for RNA-Seq, prices have dropped enough where the sample processingitself is less of a consideration in comparison to array-based studies. 
"Now the challenges are in the downstream bioinformatics,"he says. "Over the years, there has been substantial investment in array-basedinformatics pipelines, and researchers are not too keen on walking away fromthat investment or the confidence they have in the results and familiarworkflows. With sequencing, we introduce a paradigm where, though the workflowsare similar and the results are familiar, well-vetted, mature pipelines justdon't exist."
Through this collaborative offering, Lambert says thepartners can make a significant portion of this research easier and moreproductive. 
"In essence, this is about making it easier for researchersto make the switch from array-based technology to sequencing and to see a realbenefit from that switch," he says.
At the end of the day, Lambert says the collaboration isabout increasing productivity and providing access to insights that would havebeen hidden behind a high bioinformatics barrier. 
"Thus, the goal is to enable our customers to easily,comprehensively visualize and analyze their sequence data. RNA-Seq data mayeasily be 1,000 times larger than microarray data, yet some of the insights andadvantages it has over microarrays is in things like large alignment files," hesays. "Our solution utilizes both the cloud and the desktop for theirrespective strengths, leaving compute intensive tasks and large data storage inthe cloud while providing the rich interactive Genome Browser experience on thedesktop that dynamically pulls down what it needs to explore the analysisresults."
Financial terms of the collaboration were not disclosed.

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