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The Galaxy Platform for Reproducible Affinity Proteomic Mass Spectrometry Data Analysis

  • Paul A. StewartEmail author
  • Brent M. Kuenzi
  • Subina Mehta
  • Praveen Kumar
  • James E. Johnson
  • Pratik Jagtap
  • Timothy J. Griffin
  • Eric B. Haura
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1977)

Abstract

Affinity proteomics (AP-MS) is growing in importance for characterizing protein-protein interactions (PPIs) in the form of protein complexes and signaling networks. The AP-MS approach necessitates several different software tools, integrated into reproducible and accessible workflows. However, if the scientist (e.g., a bench biologist) lacks a computational background, then managing large AP-MS datasets can be challenging, manually formatting AP-MS data for input into analysis software can be error-prone, and data visualization involving dozens of variables can be laborious. One solution to address these issues is Galaxy, an open source and web-based platform for developing and deploying user-friendly computational pipelines or workflows. Here, we describe a Galaxy-based platform enabling AP-MS analysis. This platform enables researchers with no prior computational experience to begin with data from a mass spectrometer (e.g., peaklists in mzML format) and perform peak processing, database searching, assignment of interaction confidence scores, and data visualization with a few clicks of a mouse. We provide sample data and a sample workflow with step-by-step instructions to quickly acquaint users with the process.

Key words

Affinity purification Affinity proteomics APOSTL AP-MS Galaxy-P 

Notes

Acknowledgments

The authors acknowledge support from NIH grant U24CA199347 and NSF grant 1458524 to the Galaxy-P team members (P.K., S.M., J.J., P.J., T.G.), the Moffitt Lung Cancer Center of Excellence (P.S.), and the NIH/NCI F99/K00 Predoctoral to Postdoctoral Transition Award F99 CA212456 (B.K.). This work has been supported in part by the Biostatistics and Bioinformatics Shared Resource at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Paul A. Stewart
    • 1
    • 2
    Email author
  • Brent M. Kuenzi
    • 3
    • 4
  • Subina Mehta
    • 5
  • Praveen Kumar
    • 5
    • 6
  • James E. Johnson
    • 7
  • Pratik Jagtap
    • 5
  • Timothy J. Griffin
    • 5
  • Eric B. Haura
    • 1
  1. 1.Department of Thoracic OncologyMoffitt Cancer CenterTampaUSA
  2. 2.Biostatistics and Bioinformatics Shared ResourceMoffitt Cancer CenterTampaUSA
  3. 3.Department of Drug DiscoveryMoffitt Cancer CenterTampaUSA
  4. 4.Cancer Biology Ph.D. ProgramUniversity of South FloridaTampaUSA
  5. 5.Department of Biochemistry, Molecular Biology and BiophysicsUniversity of MinnesotaMinneapolisUSA
  6. 6.Bioinformatics and Computational BiologyUniversity of MinnesotaMinneapolisUSA
  7. 7.Minnesota Supercomputing InstituteUniversity of MinnesotaMinneapolisUSA

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