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Verheggen K, Maddelein D, Hulstaert N, et al. Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics. J Proteome Res. 2015;15(3):707-12doi: 10.1021/acs.jproteome.5b00850.
Verheggen, K., Maddelein, D., Hulstaert, N., Martens, L., Barsnes, H., & Vaudel, M. (2016). Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics. Journal of proteome research, 15(3), 707-12. https://doi.org/10.1021/acs.jproteome.5b00850
Verheggen, Kenneth, et al. "Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics." Journal of proteome research vol. 15,3 (2016): 707-12. doi: https://doi.org/10.1021/acs.jproteome.5b00850
Verheggen K, Maddelein D, Hulstaert N, Martens L, Barsnes H, Vaudel M. Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics. J Proteome Res. 2016 Mar 04;15(3):707-12. doi: 10.1021/acs.jproteome.5b00850. Epub 2015 Nov 06. PMID: 26510693.
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J Proteome Res. 2016 Mar 04;15(3):707-12. doi: 10.1021/acs.jproteome.5b00850. Epub 2015 Nov 06.

Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics.

Journal of proteome research

Kenneth Verheggen, Davy Maddelein, Niels Hulstaert, Lennart Martens, Harald Barsnes, Marc Vaudel

Affiliations

  1. Medical Biotechnology Center, VIB , Albert Baertsoenkaai 3, Ghent B-9000, Belgium.
  2. Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, Ghent B-9000, Belgium.
  3. Bioinformatics Institute Ghent, Ghent University , Albert Baertsoenkaai 3, Ghent B-9000, Belgium.
  4. Proteomics Unit, Department of Biomedicine, University of Bergen , Postboks 7804, N-5020 Bergen, Norway.
  5. KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen , Postboks 7804, N-5020 Bergen, Norway.

PMID: 26510693 DOI: 10.1021/acs.jproteome.5b00850

Abstract

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open-source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.

Keywords: cluster computing; distributed computing; large scale data processing; parallelization

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