The galaxy platform for reproducible affinity proteomic mass spectrometry data analysis

Paul A. Stewart, Brent M. Kuenzi, Subina Mehta, Praveen Kumar, James E. Johnson, Pratik Jagtap, Timothy J. Griffin, Eric B. Haura

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages249-261
Number of pages13
Volume1977
DOIs
StatePublished - 2019

Publication series

NameMethods in molecular biology (Clifton, N.J.)
PublisherHumana Press
ISSN (Print)1064-3745

Bibliographical note

Funding Information:
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).

Funding Information:
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/K00P redoctoral 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).

Publisher Copyright:
© Springer Science+Business Media, LLC, part of Springer Nature 2019.

Keywords

  • AP-MS
  • APOSTL
  • Affinity proteomics
  • Affinity purification
  • Galaxy-P
  • Protein Interaction Mapping/methods
  • Computational Biology/methods
  • Databases, Protein
  • Mass Spectrometry/methods
  • Data Analysis
  • Proteomics/methods
  • Software
  • Chromatography, Affinity/methods
  • Web Browser

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Journal Article
  • Research Support, N.I.H., Extramural

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