Appistry’s Solution for NGS Data Storage and Analysis
Appistry’s high-performance Big Data platform combines self-organizing computational storage with optimized, distributed high-performance computing to provide secure, HIPAA-complaint storage and fast, accurate on-demand analysis. Accessed via a private cloud or through an on-site hardware appliance, Appistry’s Ayrris™ / BIO helps labs quickly move data from sequencers and into actionable information for researchers and clinicians.
- Cost effective, fail-safe Storage
- Easy, streamlined Data Management
- Fast, rigorous and accurate Analysis
- Comprehensive annotation for efficient Interpretation
- Flexibility for Custom Development
Eliminate sequencing analysis bottlenecks with Appistry
Reduce overhead associated with storage and management. Appistry’s approach to Big Data delivers 10-100x performance gains over traditional computing architectures. With Ayrris / BIO, labs can focus on the science rather than maintaining large, expensive, in-house infrastructures for genomic storage and analysis.
Bring the work to the data. Some companies estimate that up to 70% of big data computation is spent waiting on I/O. Appistry makes it easy for labs to transfer data off sequencers into Ayrris / BIO—and once the data is there, Ayrris / BIO pipelines can be run in a fraction of the time of most in-house analysis infrastructures.
Empower PIs. Raw data means nothing until it is interpreted by a PI. Ayrris / BIO’s infrastructure enables Appistry to deliver fully annotated results that includes valuable internal metadata and clinical information that would choke traditional computing infrastructures. Results are delivered in a variety of formats suitable for skimming, publishing, or evaluating and manipulating for follow-on studies.
Access best-in-class analysis pipelines. Developing and running top pipelines requires bioinformatics expertise and compute power in which most labs can’t afford to invest. By leveraging Appistry’s storage and analysis platform, labs can choose standard pipelines or develop custom pipelines—and run them on-demand on any dataset.