Data Priorities

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Bringing Tools to Data to Avoid Data migration and redundancy

Most current approaches for BigData analysis involve moving data to a server, HPC infrastructure or cloud where the software tools and reference databases are pre-configured. This is inefficient since this approach requires making redundant copies of data each time and additional costs/time associated with moving data back and forth. Since there is no single tool or workflow to analyze genomic data, multiple copies ...more »

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Analysis Priorities

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Integrative Analysis of molecular datatypes for a given sample

A sample could be analyzed for DNA sequence variations, structural variations, CNVs, Gene or transcript isoform expression, genome-wide methylation patterns, ChIP-seq for specific transcription factors, metabolomic or proteomic analysis, and other molecular profiles. A framework that allows a researcher to readily identify all molecular data types associated with a particular sample and integrate the results of such analyses ...more »

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Analysis Priorities

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Actively support crowd-sourcing challenges

To stimulate learning as much as possible, as quickly as possible, the data cloud could have a utility where interested parties could pose "crowd-sourcing" challenges, e,g, Kaggle. Indeed, Harold Varmus, NCI & leaders in cancer & genomics could pose the leading questions they would like bright people to take a run at answering, e.g. Hilbert's 23 problems

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