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

doi:10.1007/978-1-4939-9232-4_16

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 proceeding › Chapter

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 language	English (US)
Title of host publication	Methods in Molecular Biology
Publisher	Humana Press Inc.
Pages	249-261
Number of pages	13
Volume	1977
DOIs	https://doi.org/10.1007/978-1-4939-9232-4_16
State	Published - 2019

Publication series

Name	Methods in molecular biology (Clifton, N.J.)
Publisher	Humana 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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10.1007/978-1-4939-9232-4_16

Cite this

Stewart, P. A., Kuenzi, B. M., Mehta, S., Kumar, P., Johnson, J. E., Jagtap, P., Griffin, T. J., & Haura, E. B. (2019). The galaxy platform for reproducible affinity proteomic mass spectrometry data analysis. In Methods in Molecular Biology (Vol. 1977, pp. 249-261). (Methods in molecular biology (Clifton, N.J.)). Humana Press Inc.. https://doi.org/10.1007/978-1-4939-9232-4_16

The galaxy platform for reproducible affinity proteomic mass spectrometry data analysis. / Stewart, Paul A.; Kuenzi, Brent M.; Mehta, Subina; Kumar, Praveen; Johnson, James E.; Jagtap, Pratik ; Griffin, Timothy J.; Haura, Eric B.

Methods in Molecular Biology. Vol. 1977 Humana Press Inc., 2019. p. 249-261 (Methods in molecular biology (Clifton, N.J.)).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

@inbook{2db3127fdbb4407f8e96ed2781fab9b3,

title = "The galaxy platform for reproducible affinity proteomic mass spectrometry data analysis",

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.",

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author = "Stewart, {Paul A.} and Kuenzi, {Brent M.} and Subina Mehta and Praveen Kumar and Johnson, {James E.} and Pratik Jagtap and Griffin, {Timothy J.} and Haura, {Eric B.}",

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: {\textcopyright} Springer Science+Business Media, LLC, part of Springer Nature 2019.",

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AU - Jagtap, Pratik

AU - Griffin, Timothy J.

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N1 - 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.

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ER -

The galaxy platform for reproducible affinity proteomic mass spectrometry data analysis

Abstract

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Bibliographical note

Keywords

PubMed: MeSH publication types

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