PetaGene Partners with NetApp Alliance for Increased User Performance
Genomic compression expert PetaGene has become a NetApp Alliance Partner, the company announced recently, a move set to improve performance and reduce costs for researchers using large datasets within the genomics field. According to the companies’ research, the collaboration will be particularly useful for those transferring backlog processing to the cloud, scaling sample processing and scaling out a global repository for genomic data.
Using PetaGene compression with NetApp solutions could result in:
• files being compressed up to 90% of their normal size, using less storage space and decreasing costs
• increased collaborative efficiency by being quicker to transfer over the NetApp Data Fabric
• greater ease to leverage the cloud’s flexibility using the NetApp Data Fabric
According to Dan Greenfield, CEO & Co-founder of PetaGene: “Partnering with a leading data services provider such as NetApp will allow holders of genomic data to access ready to deploy solutions for the storage and management of their data, with the benefits of our compression technology already built-in.”
Integrative Genomics Viewer – PetaGene Edition
PetaGene has also released its own version of the Integrative Genomics Viewer (IGV) for Mac and Windows, the IGV-PG, which was awarded “Best of Show” at the recent Bio-IT World Conference and Expo in Boston.
The IGV-PG software aids in speeding up genomic data analysis, and lets users transparently view PetaSuite compressed files on local, network and cloud storage, in an identical form to the uncompressed versions of the data.
The software saves time by decompressing on the fly only the data within the IGV window or region the user is displaying, to allow for quick data access. It works with Amazon Web Services, Google Cloud Platform and Microsoft Azure platforms, as well as generic S3-compatible object storage.
IGV-PG works both with PetaGene-compressed BAM files and regular IGV project files which refer to PetaGene compressed data.