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UID:submissions.supercomputing.org_SC18_sess147_ws_cafcw102@linklings.com
SUMMARY:Developing a Reproducible WDL-Based Workflow for RNASeq Data Using
  Modular, Software Engineering-Based Approaches
DESCRIPTION:Scott Cukras, Fredrik Pettersson, Yonghong Zhang, Ling Cen, Ja
 mie Teer, and Steven Eschrich (Moffitt Cancer Center)\n\nComputational wor
 kflows have become standard in many disciplines, including bioinformatics 
 and genomics. Workflow languages, such as the Workflow Description Languag
 e (WDL) and Common Workflow Language (CWL) have been developed to express 
 workflow processing syntax. These languages can be highly expressive and c
 ustomizable however this can result in perpetuating the complex tangle of 
 code that can be difficult to maintain and comprehend. The Moffitt Cancer 
 Center participates in the ORIEN Avatar project, a multi-center project th
 at has generated molecular profiles (DNASeq, RNASeq) on ~1,000 tissues to 
 date. To support reproducibility in the analysis of RNASeq data for this p
 roject, we have implemented an RNA Sequencing Genomics analysis pipeline u
 sing Cromwell, a WDL-based workflow engine, in our HPC environment. Constr
 aining the language to specific structural conventions and emphasizing mod
 ularity, we have built a pipeline suitable for operational purposes and ma
 intainability. We implemented individual tasks with built-in unit testing 
 and nested levels of workflow integration for successively complex integra
 tion testing. This pipeline has been successfully used by bioinformatics s
 taff at Moffitt Cancer Center with minimal training.\n\nTag: Applications,
  Deep Learning, Exascale\n\nRegistration Category: Workshop Reg Pass\n\nSe
 ssion Chairs: Thomas J. Barr (Nationwide Children's Hospital); Patricia Ko
 vatch (Icahn School of Medicine at Mount Sinai); and Eric Stahlberg (MD An
 derson Cancer Center, University of Texas)\n\n
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