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Biol Direct. 2015 Aug 19;10:43. doi: 10.1186/s13062-015-0071-8.

Experiences with workflows for automating data-intensive bioinformatics.

Author information

1
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, SE-75124, Uppsala, P.O. Box 591, Sweden. ola.spjuth@farmbio.uu.se.
2
SLU-Global Bioinformatics Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden. Erik.Bongcam@slu.se.
3
Science for Life Laboratory, Karolinska Institutet, SE-17121, Stockholm, P.O. Box 1031, Sweden. guillermo.carrasco@scilifelab.se.
4
Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, 6020, Austria. lukas.forer@i-med.ac.at.
5
Science for Life Laboratory, Karolinska Institutet, SE-17121, Stockholm, P.O. Box 1031, Sweden. mario.giovacchini@scilifelab.se.
6
Science for Life Laboratory, Karolinska Institutet, SE-17121, Stockholm, P.O. Box 1031, Sweden. brainstorm@nopcode.org.
7
CSC - IT Center for Science Ltd., FI-02101, Espoo, P.O. Box 405, Finland. aleksi.kallio@csc.fi.
8
CSC - IT Center for Science Ltd., FI-02101, Espoo, P.O. Box 405, Finland. eija.korpelainen@csc.fi.
9
Chair of Bioinformatics Research Group, Boku University, Vienna, Austria. maciej.kandula@boku.ac.at.
10
Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria. wfxp@milko.3mhz.net.
11
Chair of Bioinformatics Research Group, Boku University, Vienna, Austria. david.kreil@boku.ac.at.
12
Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria. okulev@fmi.uni-sofia.bg.
13
Chair of Bioinformatics Research Group, Boku University, Vienna, Austria. pawel.labaj@boku.ac.at.
14
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, SE-75124, Uppsala, P.O. Box 591, Sweden. samuel.lampa@it.uu.se.
15
CRS4 Polaris, Pula, Italy. luca.pireddu@crs4.it.
16
Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, 6020, Austria. sebastian.schoenherr@i-med.ac.at.
17
Department of Information Technology, Uppsala University, SE-75105, Uppsala, P.O. Box 337, Sweden. alexey.siretskiy@it.uu.se.
18
AgroBioInstitute and Joint Genomic Centre, Sofia, Bulgaria. jim6329@gmail.com.

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.

PMID:
26282399
PMCID:
PMC4539931
DOI:
10.1186/s13062-015-0071-8
[Indexed for MEDLINE]
Free PMC Article

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