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Seeking a silver lining
November 2011
by David Hutton  |  Email the author
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BOSEMAN, Mont.Golden Helix Inc. and Expression Analysis are teaming up to develop an affordable and streamlined cloud-based analytic solution that reduces the barriers to adoption of RNA sequencing.
 
RNA sequencing is quickly becoming a tool of choice for gene expression studies, as it can facilitate the investigation of phenomena beyond the reach of traditional microarrays, such as novel transcripts and isoforms, alternative splice sites and allele-specific expression. Additionally, it provides greater coverage and higher quality genetic data than microarrays.  
 
Golden Helix will implement Expression Analysis' secondary analysis pipeline technology in a scalable, robust, user-friendly and cloud-based architecture, and then build tightly coupled analytic tools that not only facilitate researchers' access to that data, but also enable and inspire them to make otherwise impossible discoveries. When users are ready to "make sense" of this data (tertiary analysis), Golden Helix will provide differential expression workflows optimized for RNA-Seq data in its SNP & Variation Suite (SVS) alongside additional workflows for DNA variant analysis and genetic association testing.
 
 
Expression Analysis will provide customers with a complimentary, desktop-based Genome Browser designed to supplement the analytic workflows 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 of its "experience in creating outstanding genomic browsers and in tertiary analysis of DNA sequencing datasets."   Golden Helix CEO and President Dr. Christophe Lambert says his company needed a partner with a solid presence "upstream" of informatics, and one with similarly close ties into academic and commercial researchers, and Expression Analysis fit the bill.
 
"Given our focus on accuracy and reliability, though, we also needed to find an organization as obsessed with quality and continuous improvement as we," he says.   McPhail notes that there are several issues with RNA-Seq as it currently exists.   "Many of our clients' bioinformatics pipelines have not been optimized to deal with sequencing datasets," he explains. "These datasets tend to be extremely large and require a different scale of storage and compute infrastructure. By putting our bioinformatics pipeline on the cloud and creating web-based tools for data browsing and analysis, we believe we overcome many of the current structural limitations associated with large sequencing datasets."  
 
Lambert points out that while cost long has been the primary concern for RNA-Seq, prices have dropped enough where the sample processing itself 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-based informatics pipelines, and researchers are not too keen on walking away from that investment or the confidence they have in the results and familiar workflows. With sequencing, we introduce a paradigm where, though the workflows are similar and the results are familiar, well-vetted, mature pipelines just don't exist."  
 
Through this collaborative offering, Lambert says the partners can make a significant portion of this research easier and more productive.   
 
"In essence, this is about making it easier for researchers to make the switch from array-based technology to sequencing and to see a real benefit from that switch," he says. At the end of the day, Lambert says the collaboration is about increasing productivity and providing access to insights that would have been 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 may easily be 1,000 times larger than microarray data, yet some of the insights and advantages it has over microarrays is in things like large alignment files," he says. "Our solution utilizes both the cloud and the desktop for their respective strengths, leaving compute intensive tasks and large data storage in the cloud while providing the rich interactive Genome Browser experience on the desktop that dynamically pulls down what it needs to explore the analysis results." Financial terms of the collaboration were not disclosed.
 
Code: E111110

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