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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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).
De Las Rivas J, Fontanillo C (2010) Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6:e1000807CrossRefGoogle Scholar
Scott DE, Bayly AR, Abell C et al (2016) Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat Rev Drug Discov 15:533–550CrossRefGoogle Scholar
LaCava J, Molloy KR, Taylor MS et al (2015) Affinity proteomics to study endogenous protein complexes: pointers, pitfalls, preferences and perspectives. BioTechniques 58:103–119CrossRefGoogle Scholar
Gregan J, Riedel CG, Petronczki M et al (2007) Tandem affinity purification of functional TAP-tagged proteins from human cells. Nat Protoc 2:1145–1151CrossRefGoogle Scholar
Jones AR, Eisenacher M, Mayer G et al (2012) The mzIdentML data standard for mass spectrometry-based proteomics results. Mol Cell Proteomics 11:M111.014381CrossRefGoogle Scholar
Kessner D, Chambers M, Burke R et al (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24:2534–2536CrossRefGoogle Scholar
Vaudel M, Barsnes H, Berven FS et al (2011) SearchGUI: an open-source graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics 11:996–999CrossRefGoogle Scholar
Vaudel M, Burkhart JM, Zahedi RP et al (2015) PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat Biotechnol 33:22–24CrossRefGoogle Scholar
Choi H, Larsen B, Lin ZY et al (2011) SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat Methods 8:70–73CrossRefGoogle Scholar
Teo G, Liu G, Zhang J et al (2014) SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. J Proteome 100:37–43CrossRefGoogle Scholar
Afgan E, Baker D, van den Beek M et al (2016) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44:W3–W10CrossRefGoogle Scholar
Boekel J, Chilton JM, Cooke IR et al (2015) Multi-omic data analysis using Galaxy. Nat Biotechnol 33:137–139CrossRefGoogle Scholar
Jagtap PD, Johnson JE, Onsongo G (2014) Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework. J Proteome Res 13:5898–5908CrossRefGoogle Scholar
Sheynkman GM, Johnson JE, Jagtap PD et al (2014) Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations. BMC Genomics 15:703CrossRefGoogle Scholar
Kuenzi BM, Borne AL, Li J et al (2016) APOSTL: an interactive Galaxy pipeline for reproducible analysis of affinity proteomics data. J Proteome Res 15:4747–4754CrossRefGoogle Scholar
Lowenstein EJ, Daly RJ, Batzer AG et al (1992) The SH2 and SH3 domain-containing protein GRB2 links receptor tyrosine kinases to ras signaling. Cell 70:431–442CrossRefGoogle Scholar
Choi H, Liu G, Mellacheruvu D et al (2012) Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT. Curr Protoc Bioinformatics Chapter 8:Unit8.15PubMedGoogle Scholar
Mellacheruvu D, Wright Z, Couzens AL et al (2013) The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods 10:730–736CrossRefGoogle Scholar
Knight JD, Liu G, Zhang JP et al (2015) A web-tool for visualizing quantitative protein-protein interaction data. Proteomics 15:1432–1436CrossRefGoogle Scholar
Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287CrossRefGoogle Scholar