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Blank C, Easterly C, Gruening B, et al. Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework. Proteomes. 2018;6(1)doi: 10.3390/proteomes6010007.
Blank, C., Easterly, C., Gruening, B., Johnson, J., Kolmeder, C. A., Kumar, P., May, D., Mehta, S., Mesuere, B., Brown, Z., Elias, J. E., Hervey, W. J., McGowan, T., Muth, T., Nunn, B., Rudney, J., Tanca, A., Griffin, T. J., & Jagtap, P. D. (2018). Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework. Proteomes, 6(1), . https://doi.org/10.3390/proteomes6010007
Blank, Clemens, et al. "Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework." Proteomes vol. 6,1 (2018). doi: https://doi.org/10.3390/proteomes6010007
Blank C, Easterly C, Gruening B, Johnson J, Kolmeder CA, Kumar P, May D, Mehta S, Mesuere B, Brown Z, Elias JE, Hervey WJ, McGowan T, Muth T, Nunn B, Rudney J, Tanca A, Griffin TJ, Jagtap PD. Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework. Proteomes. 2018 Jan 31;6(1). doi: 10.3390/proteomes6010007. PMID: 29385081; PMCID: PMC5874766.
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Proteomes. 2018 Jan 31;6(1). doi: 10.3390/proteomes6010007.

Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework.

Proteomes

Clemens Blank, Caleb Easterly, Bjoern Gruening, James Johnson, Carolin A Kolmeder, Praveen Kumar, Damon May, Subina Mehta, Bart Mesuere, Zachary Brown, Joshua E Elias, W Judson Hervey, Thomas McGowan, Thilo Muth, Brook Nunn, Joel Rudney, Alessandro Tanca, Timothy J Griffin, Pratik D Jagtap

Affiliations

  1. Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany. [email protected].
  2. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  3. Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany. [email protected].
  4. Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  5. Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland. [email protected].
  6. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  7. Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. [email protected].
  8. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  9. Computational Biology Group, Ghent University, Krijgslaan 281, B-9000 Ghent, Belgium. [email protected].
  10. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  11. Department of Chemical & Systems Biology, Stanford University, Stanford, CA 94305, USA. [email protected].
  12. Center for Bio/Molecular Science & Engineering, Naval Research Laboratory, Washington, DC 20375, USA. [email protected].
  13. Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  14. Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany. [email protected].
  15. Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. [email protected].
  16. Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  17. Porto Conte Ricerche Science and Technology Park of Sardinia, 07041 Alghero, Italy. [email protected].
  18. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].
  19. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA. [email protected].

PMID: 29385081 PMCID: PMC5874766 DOI: 10.3390/proteomes6010007

Abstract

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics "Contribution Fest" undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.

Keywords: Galaxy platform; bioinformatics; community development; functional microbiome; mass spectrometry; metaproteomics; software workflow development

Conflict of interest statement

The authors declare no conflict of interest.

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