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Issue link: https://clinicalomics.epubxp.com/i/853204
www.clinicalomics.com July/August 2017 Clinical OMICs 31 we can delete then recreate later, if needed, because of what we are able to record in our metadata." Effectively lessening the data bur- den without decreasing the value of the genomic data is only a part of the equation, said Wesselman, who previously worked in Hewlett Pack- ard's hyperscale computing division. OnRamp was initially working with ScaleMatrix using its on-premise com- puting offering before Cloudian joined to complete the picture. "We discovered ScaleMatrix was super close to us and had an amazing, state-of-the-art data center—some of the technologies we had attempted to work on in the lab at HP, they had developed and patented," said Wes- selman. "But the storage costs were killing us. We were always pound- ing them, telling them we needed to bring the costs down, and that is when Chris [Orlando, co-founder of ScaleMatrix] said we needed to bring Cloudian in." For it's part, Cloudian provides a significantly lower price of data stor- age compared to cloud services via its hybrid cloud object storage systems. "We are object storage that sits in the data center. What is important is the scalability—it can grow infinitely to accommodate the data you are gen- erating," said Toor. "But from a cost perspective, one of the really appeal- ing aspects of object storage is that we run on industry standard servers. "So you think of the commodity servers which are produced in the 10-million-per-year quantities. We capitalize on that economy of scale to provide a storage environment which is inherently 70% less expensive than the traditional storage devices that sit in data centers today." All of which enables a service offer- ing for providing valuable genomic data to a broader range of researchers. In Wesselman's view, it is imperative that genomic research solutions move beyond the realm of the 15,000 bioin- formaticians who currently ply their trade to the broader community. "We have designed a system that is first for the biologists and research- ers, so that they don't need to answer questions more complex than 'What is the build of the genome?'" he said. "The intuitive interface asks them the biology questions, they don't select the pipeline. The analysis happens for them once all the data is there, because we know what they need to do." In order to deliver on the promise of an interface that can intuitively pro- vide the data each scientist needs for their work, OnRamp brought on Jean Lozach as CTO, who previously held bioinformatics positions of increasing responsibility with Illumina. Lozach said he has witnessed the evolution of sequencing and the use of sequencing data from the front lines, and noted the significant changes that have occurred in how genomic data is handled from the early days when researchers "wanted to keep every- thing inside the sequencer." He said: "People need to see next generation sequencing and proteom- ics and all the other systems as simply tools to use—it is nothing more than a tool. Thanks to Illumina it has become a very valuable tool, in terms of gen- erating a lot of data. But [researchers] can't worry about everything. They need to have confidence that we are doing things the right way, that we can effectively capture the right data and reanalyze it because we know all the parameters." AT NYGC, IBM Watson Demonstrates Potential for AI and WGS Analysis Researchers at the New York Genome Center (NYGC), the Rockefeller University, and other NYGC member institutions, re- cently demonstrated the potential of IBM Watson for Genomics to analyze complex genomic data from state-of-the-art DNA sequencing of whole genomes and pro- vide a report of potentially clinically ac- tionable insights within 10 minutes. The proof-of-concept study used a beta version of Watson for Genomics to help in- terpret whole-genome sequencing (WGS) data for one patient. For the research, Watson's time to generate a report of 10 minutes was measured against human curation and analysis of the same patient data, which took 160 hours to reach the same conclusions. In the study, NYGC researchers and bio- informatics experts analyzed DNA and RNA from a glioblastoma tumor specimen and DNA from the patient's normal blood, and compared potentially actionable in- sights to those derived from a commercial targeted panel that had previously been performed. The whole genome and RNA sequencing data were analyzed by a team of bioinformaticians and oncologists at the NYGC as well as a beta version of IBM Watson for Genomics, an automated sys- tem for prioritizing somatic variants and wildpixel / Getty Images (continued on next page)