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Corporate Profiles : Apr 1, 2012 ( )
Managing a Deluge of Sequencing Data
Appistry Applies Cloud-Based Computing Technology in the Genomics Space
Computer scientists founded Appistry in 2001 to “solve big data analytical problems,” says Kevin Haar, CEO. The firm’s cloud-based computing technology helps customers like FedEx, financial institutions, and government agencies solve problems related to the analysis of data-intensive applications.
Appistry recently branched out to serve life scientists who analyze and manage the deluge of genomics data spewing from sequencers.
They hired biologic scientists like Richard Mazzarella, a member of the Human Genome Project, to guide the evolution into bioinformatics. “Now it’s hard to tell if we’re a life science or computer science company,” says Haar.
Appistry’s platform for genomics analysis is Ayrris/BIO™, an automated solution that transforms complex, unstructured sequencing data into understandable and clinically relevant results. Ayrris/BIO reflects the company’s new focus on the genomics field. “We fell in love with the genomics space and now it dominates our hearts and minds,” says Haar.
Ayrris/BIO applies cloud-like architectural principles to the challenge of extreme analytics involved in next-generation sequencing. Ayrris/BIO includes pipelines for human exome and whole genome human, bovine, maize, and other organisms.
Built specifically for the life science industry, Ayrris/BIO removes the technical burden from genomics discovery and analysis, and fully automates pipeline execution and data management, according to the company. The platform’s prebuilt pipelines for alignment, exome, whole-genome, and other analysis can be used out of the box or customized to meet the needs of individual researchers. Appistry’s life science team continues to enhance Ayrris/BIO and develop new approaches to help researchers to rapidly build, test, and use sequencing pipelines.
Research laboratories, contract research organizations, bioinformatic teams, and core facilities at national institutions may benefit from Ayrris/BIO, which can be acquired in two ways. Facilities with a large amount of sequencing data can obtain the software prebuilt and preconfigured running on an Appistry appliance. Or researchers can buy space and run an analysis on the cloud.
“We can run a whole-genome analysis for $250, which drives our common dream of a sub-$1,000 complete genome analysis,” says Sultan Meghji, vp of analytics applications.
In October 2011, Appistry released RNA-Seq solutions, which further leverage the Ayrris/BIO platform for personalized medicine applications. RNA-Seq detects both novel transcript isoforms and genetic polymorphisms. The platform covers all facets of sequencing analysis including alignment, transcript assembly, splice variant prediction, and expression-level determination.
RNA-Seq can profile multiple organisms including humans, mice, rats, zebrafish, Drosophila, E. coli, goats, chickens, dogs, corn, and soybeans. Users can visualize their results in multiple systems.
The firms believes RNA-Seq will help to solve personalized medicine problems. “You can tailor treatments not just to diagnostics, but also therapies,” says Meghji. A patient with a subtype of a disease can be treated with the best therapy, based on their genetic profile.
Advantages of Ayrris/BIO
In cloud architecture, work is distributed across many machines, and the data is combined and stored. Most other systems for analyzing genomics data are “spoke” technologies that take several months to build. Biologists, rather than computer scientists, generally design these rigid and fragile systems. In contrast, Appistry says its platform takes a few weeks to build, and no technical skills are needed to run Ayrris/BIO.
Prospective life science customers told Appistry that they wanted cost-effective, flexible solutions that could be scaled up. “We made that available in the cloud for individual scientists and researchers,” says Meghji. Ayrris/BIO easily scales up to handle very large systems with little effort, he reports.
Spoke-like systems take 24 to 72 hours to run an analysis and use hundreds of nodes. “When they move to our system with a small number of nodes, they do the same work in 30 minutes,” says Haar. Doctors, researchers, and life science professionals can use the cloud architecture for a scalable, reliable, and adaptable framework for high-performance applications, he notes.
Haar explains that pipelines for analysis of exome, whole-genome, and RNA-Seq data provide an inexpensive, secure, fault-tolerant, and efficient computing environment. “We’re pushing the frontier by creating the most efficient service and highest quality at the lowest price,” he says.
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