Bioinformatics workflow management system
||This article may contain excessive, poor, irrelevant, or self-sourcing examples. (February 2012)|
A bioinformatics workflow management system is a specialized form of workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, that relate to bioinformatics.
There are currently many different workflow systems. Some have been developed more generally as scientific workflow systems for use by scientists from many different disciplines like astronomy and earth science. All such systems are based on an abstract representation of how a computation proceeds in the form of a directed graph, where each node represents a task to be executed and edges represent either data flow or execution dependencies between different tasks. Each system typically provides visual front-end allowing the user to build and modify complex applications with little or no programming expertise.
In alphabetical order, some examples of bioinformatics workflow management systems include:
- Anduril bioinformatics and image analysis
- BioExtract: a web-based system for querying biomolecular sequence data, executing analytic tools on the resulting extracts, and constructing workflows composed of such queries and tools.
- BioBIKE: a Web-based, programmable, integrated biological knowledge base
- Discovery Net: one of the earliest examples of a scientific workflow system, later commercialized as InforSense which was then acquired by IDBS.
- Galaxy: initially targeted at genomics
- GenePattern: A powerful scientific workflow system that provides access to more than 220 genomic analysis tools.
- KNIME the Konstanz Information Miner
- OnlineHPC Online workflow designer based on Taverna
- UGENE provides a workflow management system that is installed on a local computer
Comparisons between workflow systems
With a large number of bioinformatics workflow systems to choose from, it becomes difficult to understand and compare the features of the different workflow systems. There has been little work conducted in evaluating and comparing the systems from a bioinformatician's perspective, especially when it comes to comparing the data types they can deal with, the in-built functionalities that are provided to the user or even their performance or usability. Examples of existing comparisons include
- The paper "Scientific workflow systems-can one size fit all?", which provides a high-level framework for comparing workflow systems based on their control flow and data flow properties. The systems compared include Discovery Net, Taverna, Triana, Kepler as well as Yawl and BPEL.
- The paper "Meta-workflows: pattern-based interoperability between Galaxy and Taverna"  which provides a more user-oriented comparison between Taverna and Galaxy in the context of enabling interoperability between both systems.
- The infrastructure paper "Delivering ICT Infrastructure for Biomedical Research"  compares two workflow systems, Anduril and Chipster, in terms of infrastructure requirements in a cloud-delivery model.
- The paper "A review of bioinformatic pipeline frameworks"  attempts to classify workflow management systems based on three dimensions: "using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface".
- Oinn, T.; Greenwood, M.; Addis, M.; Alpdemir, M. N.; Ferris, J.; Glover, K.; Goble, C.; Goderis, A.; Hull, D.; Marvin, D.; Li, P.; Lord, P.; Pocock, M. R.; Senger, M.; Stevens, R.; Wipat, A.; Wroe, C. (2006). "Taverna: Lessons in creating a workflow environment for the life sciences". Concurrency and Computation: Practice and Experience. 18 (10): 1067–1100. doi:10.1002/cpe.993.
- Yu, J.; Buyya, R. (2005). "A taxonomy of scientific workflow systems for grid computing". ACM SIGMOD Record. 34 (3): 44. doi:10.1145/1084805.1084814.
- Curcin, V.; Ghanem, M. (2008). "Scientific workflow systems - can one size fit all?". 2008 Cairo International Biomedical Engineering Conference: 1–9. doi:10.1109/CIBEC.2008.4786077.
- Elhai, J.; Taton, A.; Massar, J.; Myers, J. K.; Travers, M.; Casey, J.; Slupesky, M.; Shrager, J. (2009). "BioBIKE: A Web-based, programmable, integrated biological knowledge base". Nucleic Acids Research. 37 (Web Server issue): W28–W32. doi:10.1093/nar/gkp354. PMC . PMID 19433511.
- Goecks, J.; Nekrutenko, A.; Taylor, J.; Galaxy Team, T. (2010). "Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences". Genome Biology. 11 (8): R86. doi:10.1186/gb-2010-11-8-r86. PMC . PMID 20738864.
- Reich, Michael; et al. (2006). "GenePattern 2.0". Nature Genetics. 38 (1): 500–5001. doi:10.1038/ng0506-500. PMID 16642009.
- "Workflow based framework for life science informatics". Computational Biology and Chemistry. 31: 305–319. 2007. doi:10.1016/j.compbiolchem.2007.08.009.
- Okonechnikov, K; Golosova, O; Fursov, M; Ugene, Team (2012). "Unipro UGENE: A unified bioinformatics toolkit". Bioinformatics (Oxford, England). 28 (8): 1166–7. doi:10.1093/bioinformatics/bts091. PMID 22368248.
- "VisTrails: enabling interactive multiple-view visualizations". VIS 05. IEEE Visualization, 2005. 2005. doi:10.1109/VISUAL.2005.1532788.
- Abouelhoda, M.; Alaa, S.; Ghanem, M. (2010). "Meta-workflows". Proceedings of the 1st International Workshop on Workflow Approaches to New Data-centric Science - Wands '10. p. 1. doi:10.1145/1833398.1833400. ISBN 9781450301886.
- Nyrönen, TH; Laitinen, J; et al. (2012), Delivering ICT infrastructure for biomedical research, Proceedings of the WICSA/ECSA 2012 Companion Volume (WICSA/ECSA '12), ACM, doi:10.1145/2361999.2362006
- Kallio, M. A.; Tuimala, J. T.; Hupponen, T; Klemelä, P; Gentile, M; Scheinin, I; Koski, M; Käki, J; Korpelainen, E. I. (2011). "Chipster: User-friendly analysis software for microarray and other high-throughput data". BMC Genomics. 12: 507. doi:10.1186/1471-2164-12-507. PMC . PMID 21999641.
- Leipzig J (2016). "A review of bioinformatic pipeline frameworks". Briefings in bioinformatics. doi:10.1093/bib/bbw020. PMID 27013646. Retrieved 23 March 2016.