Bioinformatics Workflows and Web Services in Systems Biology Made Easy for Experimentalists

  • Rafael C. Jimenez
  • Manuel Corpas
Part of the Methods in Molecular Biology book series (MIMB, volume 1021)


Workflows are useful to perform data analysis and integration in systems biology. Workflow management systems can help users create workflows without any previous knowledge in programming and web services. However the computational skills required to build such workflows are usually above the level most biological experimentalists are comfortable with. In this chapter we introduce workflow management systems that reuse existing workflows instead of creating them, making it easier for experimentalists to perform computational tasks.

Key words

Workflows Web services SBML BioModels Gene Ontology Taverna Biocatalogue myExperiment 



R.C.J. is supported by the NHLBI Proteomics Center Award HHSN268201000035C.


  1. 1.
    Goble CA, Bhagat J, Aleksejevs S, Cruickshank D, Michaelides D, Newman D, Borkum M, Bechhofer S, Roos M, Li P, De Roure D (2010) myExperiment: a repository and social network for the sharing of bioinformatics workflows. Nucleic Acids Res 38(Web Server Issue):W677–W682PubMedCrossRefGoogle Scholar
  2. 2.
    Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, Oinn T (2006) Taverna: a tool for building and running workflows of services. Nucleic Acids Res 34(Web Server Issue):W729–W732PubMedCrossRefGoogle Scholar
  3. 3.
    Li P, Castrillo JI, Velarde G, Wassink I, Soiland-Reyes S, Owen S, Withers D, Oinn T, Pocock MR, Goble CA, Oliver SG, Kell DB (2008) Performing statistical analyses on quantitative data in Taverna workflows: an example using R and maxdBrowse to identify differentially-expressed genes from microarray data. BMC Bioinformatics 9:334PubMedCrossRefGoogle Scholar
  4. 4.
    Li P, Oinn T, Soiland S, Kell DB (2008) Automated manipulation of systems biology models using libSBML within Taverna workflows. Bioinformatics 24(2):287–289PubMedCrossRefGoogle Scholar
  5. 5.
    Roos M, Marshall MS, Gibson AP, Schuemie M, Meij E, Katrenko S, van Hage WR, Krommydas K, Adriaans PW (2009) Structuring and extracting knowledge for the support of hypothesis generation in molecular biology. BMC Bioinformatics 10(Suppl 10):S9PubMedCrossRefGoogle Scholar
  6. 6.
    Fisher P, Hedeler C, Wolstencroft K, Hulme H, Noyes H, Kemp S, Stevens R, Brass A (2007) A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis. Nucleic Acids Res 35(16):5625–5633PubMedCrossRefGoogle Scholar
  7. 7.
    Fisher P, Noyes H, Kemp S, Stevens R, Brass A (2009) A systematic strategy for the discovery of candidate genes responsible for phenotypic variation. Methods Mol Biol 573:329–345PubMedCrossRefGoogle Scholar
  8. 8.
    Moustakas A, Souchelnytskyi S, Heldin CH (2001) Smad regulation in TGF-beta signal transduction. J Cell Sci 114(Pt 24):4359–4369PubMedGoogle Scholar
  9. 9.
    Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92PubMedCrossRefGoogle Scholar
  10. 10.
    Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R (2009) QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25(22):3045–3046PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Rafael C. Jimenez
    • 1
  • Manuel Corpas
    • 2
  1. 1.EMBL Outstation–European Bioinformatics InstituteCambridgeUK
  2. 2.The Genome Analysis CentreNorwichUK

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