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DTSTAMP:20181221T160725Z
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DTSTART;TZID=America/Chicago:20181111T103000
DTEND;TZID=America/Chicago:20181111T110000
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:Workshop\nApplications, Deep Learning, Exascale, Workshop Reg 
 Pass\n\nDeveloping a Reproducible WDL-Based Workflow for RNASeq Data Using
  Modular, Software Engineering-Based Approaches\n\nCukras, Pettersson, Zha
 ng, Cen, Teer...\n\nComputational workflows have become standard in many d
 isciplines, including bioinformatics and genomics. Workflow languages, suc
 h as the Workflow Description Language (WDL) and Common Workflow Language 
 (CWL) have been developed to express workflow processing syntax. These lan
 guages can be highly expressive and customizable however this can result i
 n perpetuating the complex tangle of code that can be difficult to maintai
 n and comprehend. The Moffitt Cancer Center participates in the ORIEN Avat
 ar project, a multi-center project that has generated molecular profiles (
 DNASeq, RNASeq) on ~1,000 tissues to date. To support reproducibility in t
 he analysis of RNASeq data for this project, we have implemented an RNA Se
 quencing Genomics analysis pipeline using Cromwell, a WDL-based workflow e
 ngine, in our HPC environment. Constraining the language to specific struc
 tural conventions and emphasizing modularity, we have built a pipeline sui
 table for operational purposes and maintainability. We implemented individ
 ual tasks with built-in unit testing and nested levels of workflow integra
 tion for successively complex integration testing. This pipeline has been 
 successfully used by bioinformatics staff at Moffitt Cancer Center with mi
 nimal training.
URL:https://sc18.supercomputing.org/presentation/?id=ws_cafcw102&sess=sess
 147
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