Title |
Experiences with workflows for automating data-intensive bioinformatics
|
---|---|
Published in |
Biology Direct, August 2015
|
DOI | 10.1186/s13062-015-0071-8 |
Pubmed ID | |
Authors |
Ola Spjuth, Erik Bongcam-Rudloff, Guillermo Carrasco Hernández, Lukas Forer, Mario Giovacchini, Roman Valls Guimera, Aleksi Kallio, Eija Korpelainen, Maciej M Kańduła, Milko Krachunov, David P Kreil, Ognyan Kulev, Paweł P. Łabaj, Samuel Lampa, Luca Pireddu, Sebastian Schönherr, Alexey Siretskiy, Dimitar Vassilev |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 17% |
Sweden | 7 | 13% |
United Kingdom | 5 | 9% |
Finland | 4 | 7% |
Austria | 3 | 6% |
Italy | 2 | 4% |
Spain | 2 | 4% |
Mexico | 1 | 2% |
Taiwan | 1 | 2% |
Other | 8 | 15% |
Unknown | 12 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 30 | 56% |
Members of the public | 24 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 2% |
Germany | 2 | 1% |
France | 2 | 1% |
Brazil | 2 | 1% |
Spain | 2 | 1% |
Norway | 1 | <1% |
Sweden | 1 | <1% |
Taiwan | 1 | <1% |
Netherlands | 1 | <1% |
Other | 2 | 1% |
Unknown | 178 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 68 | 35% |
Student > Ph. D. Student | 36 | 18% |
Student > Master | 18 | 9% |
Student > Bachelor | 13 | 7% |
Other | 11 | 6% |
Other | 30 | 15% |
Unknown | 19 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 54 | 28% |
Computer Science | 49 | 25% |
Biochemistry, Genetics and Molecular Biology | 41 | 21% |
Engineering | 6 | 3% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 2% |
Other | 18 | 9% |
Unknown | 24 | 12% |