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DTSTART;TZID=America/Chicago:20181111T111500
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UID:submissions.supercomputing.org_SC18_sess147_ws_cafcw112@linklings.com
SUMMARY:The Gen3 Approach to Portability and  Repeatability for Cancer Gen
 omics Projects
DESCRIPTION:Zac Flamig, Yajing Tang, and Robert L. Grossman (University of
  Chicago)\n\nThe Gen3 software stack is a open-source platform for managin
 g, analyzing, and sharing petabyte-scale research data. In this note, we d
 escribe the approach that we have used with Gen3 to support portability an
 d repeatibility for cancer genomics projects. Data in a Gen3 data commons 
 is divided into projects. Project data is of two types: large files, such 
 as BAM files and image files, that are managed as data objects and stored 
 in one or more private and public clouds, and all of the other data associ
 ated with a project, including all of the the clinical phenotype data and 
 biospecimen data. We call this other data “core data” and have developed d
 ata serialization format for it, which includes versioning and schema info
 rmation. Data objects are available across multiple data commons, while co
 re data can be exported and imported using the serialization format. In th
 is way, we support portability for data projects. We support repeatibility
  by representing workflows using the Common Workflow Language (CWL) and ma
 naging the CWL files as data objects. With this approach, we simply need t
 o manage and version the data objects, core data, and CWL files associated
  with a project.\n\nTag: Applications, Deep Learning, Exascale\n\nRegistra
 tion Category: Workshop Reg Pass\n\nSession Chairs: Thomas J. Barr (Nation
 wide Children's Hospital); Patricia Kovatch (Icahn School of Medicine at M
 ount Sinai); and Eric Stahlberg (MD Anderson Cancer Center, University of 
 Texas)\n\n
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