Challenges of Large-Scale Biomedical Workflows on the Cloud -- A Case Study on the Need for Reproducibility of Results

Computational bioinformatics workflows are extensively used to analyse genomics data. With the unprecedented advancements in genomic sequence technology and opportunities for personalized medicines, it is essential that analysis results are repeatable by others, especially when moving into clinical...

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Bibliographic Details
Published in:Proceedings - IEEE Symposium on Computer-Based Medical Systems pp. 220 - 225
Main Authors: Kanwal, Sehrish, Lonie, Andrew, Sinnott, Richard O., Anderson, Charlotte
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2015
Subjects:
Australia
Bioinformatics
bioinformatics workflows
Computation
Data processing
Disorders
distributed compute resources
DNA
exome
Genomics
Laboratories
NeCTAR Research Cloud
Reproducibility
Sequential analysis
Software
Workbenches
Workflow
ISSN:1063-7125, 2372-9198
Online Access:Get full text
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Summary:Computational bioinformatics workflows are extensively used to analyse genomics data. With the unprecedented advancements in genomic sequence technology and opportunities for personalized medicines, it is essential that analysis results are repeatable by others, especially when moving into clinical environment. To cope with the complex computational demands of huge biological datasets, a shift to distributed compute resources is unavoidable. A case study was conducted in which three well established bioinformatics analysis groups across Australia were assigned to analyse exome sequence data from a range of patients with a rare condition: disorder of sex development. Initially these groups used their own in-house data processing pipelines, and subsequently used a common bioinformatics workbench based upon Galaxy and offered through the Australia-wide National eResearch Collaboration Tools and Resources (NeCTAR) Research Cloud. This paper describes the experiences in this work and the variability of results. We put forward principles that should be used to ensure reproducibility of scientific results moving forward.
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ISSN:1063-7125
2372-9198
DOI:10.1109/CBMS.2015.28