Printer-friendly versionTranslational research requires a knowledge-driven ecosystem in which constituents generate, contribute, manage, and analyze data available from all parts of the landscape. The goal is a continuous feedback loop to accelerate the translation of data into knowledge. Collaboration, data sharing, data integration, and adherence to standards are integral. Only by seamlessly structuring and integrating these data types will the complex and underlying causes and outcomes of diseases revealed, and effective prevention, early detection, and personalized treatments realized.
Integrated Data Analysis Challenges in Life Sciences Research
One of the greatest challenges in life sciences research is aggregating and contextually integrating diverse biological data sets to perform analysis. Many research enterprises have functional clinical systems, a variety of instruments generating large diverse data sets across multiple laboratories, and many statistical analysis tools and algorithms. The real challenge is tracing clinical sources to biomolecular analyses, meaningfully integrating these diverse sets, and then readily accessing this data for iterative data analysis.
GenoLogics provides many informatics capabilities to accomplish effective translational data integration.
Translational Data Integration Capabilities
• Ability to manage and integrate clinical, genomics, proteomics, and other research data sets
• Database, query, and compare clinical annotations against SNP and expression data
• OLAP data cubes for proteomics search and quantitation results for advanced analytical comparisons and true data mining
• Pipeline OSS and in-house developed algorithms to get from raw instrument data, to quality scientific results
• Web reporting portal to integrate datasets into a number of diverse views
• Federate queries on translational research databases and marshal results into statistical packages
• Next generation sequencing data management marshaled to NGS data analysis partners like CLC Bio and JMP Genomics
• Tight software integrations with leading analysis tools in the proteomic and genomics data fields, to enable diverse analysis.
