Complex Analytics for Pharma & Biotech

Biological and clinical data researchers collect, curate, analyze, and share both raw data and derived results. And increasingly there is a drive to archive data both for subsequent reanalysis with new analytical techniques and for reproducing results for experimental validation and audits. The sheer volume of data being produced from genetic sequencing data to compound libraries creates daunting challenges for both data management and analysis.

Biological data, like much scientific data, is ordered, highly dimensional data – far more complex that the flat, fact-oriented data that conventional databases were designed for. Experimental data needs to be preserved as collected or derived. Scientific data sets must be updated with corrections or additions, but never overwritten.

SciDB helps researchers store and analyze information about molecules, genes, proteins, pathways, biomarkers, compounds, and the like in a rich and ordered way. Complex math functions can be run directly against the full spectrum of data to identify patterns and direct further discovery.

For a compelling example of a genomics application built atop SciDB, see the 1000 genomes browser.