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 |
| DOIs | |
| State | Published - Jan 1 2019 |
Publication series
| Name | Methods in Molecular Biology |
|---|---|
| Volume | 1977 |
| ISSN (Print) | 1064-3745 |
Fingerprint
Keywords
- AP-MS
- APOSTL
- Affinity proteomics
- Affinity purification
- Galaxy-P
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.
Cite this
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 D; Griffin, Timothy J; Haura, Eric B.
Methods in Molecular Biology. Humana Press Inc., 2019. p. 249-261 (Methods in Molecular Biology; Vol. 1977).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - The galaxy platform for reproducible affinity proteomic mass spectrometry data analysis
AU - Stewart, Paul A.
AU - Kuenzi, Brent M.
AU - Mehta, Subina
AU - Kumar, Praveen
AU - Johnson, James E
AU - Jagtap, Pratik D
AU - Griffin, Timothy J
AU - Haura, Eric B.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - AP-MS
KW - APOSTL
KW - Affinity proteomics
KW - Affinity purification
KW - Galaxy-P
UR - http://www.scopus.com/inward/record.url?scp=85064744995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064744995&partnerID=8YFLogxK
U2 - 10.1007/978-1-4939-9232-4_16
DO - 10.1007/978-1-4939-9232-4_16
M3 - Chapter
C2 - 30980333
AN - SCOPUS:85064744995
T3 - Methods in Molecular Biology
SP - 249
EP - 261
BT - Methods in Molecular Biology
PB - Humana Press Inc.
ER -