Experiences with workflows for automating data-intensive bioinformatics

Abstract

High-throughput technologies, such as next-generation sequencing, have turned molecular biology into a data-intensive discipline, requiring bioinformaticians to use high-performance computing resources and carry out data management and analysis tasks on large scale. Workflow systems can be useful to simplify construction of analysis pipelines that automate tasks, support reproducibility and provide measures for fault-tolerance. However, workflow systems can incur significant development and administration overhead so bioinformatics pipelines are often still built without them. We present the experiences with workflows and workflow systems within the bioinformatics community participating in a series of hackathons and workshops of the EU COST action SeqAhead. The organizations are working on similar problems, but we have addressed them with different strategies and solutions. This fragmentation of efforts is inefficient and leads to redundant and incompatible solutions. Based on our experiences we define a set of recommendations for future systems to enable efficient yet simple bioinformatics workflow construction and execution. Reviewers This article was reviewed by Dr Andrew Clark.

DOI: 10.1186/s13062-015-0071-8

Extracted Key Phrases

4 Figures and Tables

Cite this paper

@inproceedings{Spjuth2015ExperiencesWW, title={Experiences with workflows for automating data-intensive bioinformatics}, author={Ola Spjuth and Erik Bongcam-Rudloff and Guillermo Carrasco Hern{\'a}ndez and Lukas Forer and Mario Giovacchini and Roman Valls Guimera and Aleksi Kallio and Eija Korpelainen and Maciej M. Kańduła and Milko Krachunov and David P. Kreil and Ognyan Kulev and Paweł P Łabaj and Samuel Lampa and Luca Pireddu and Sebastian Sch{\"{o}nherr and Alexey Siretskiy and Dimitar Vassilev}, booktitle={Biology Direct}, year={2015} }