UNIT 8.19 Scoring Large‐Scale Affinity Purification Mass Spectrometry Datasets with MiST. Issue 1 (9th March 2015)

• Record Type: Journal Article
• Authors: Verschueren, Erik; Von Dollen, John; Cimermancic, Peter; Gulbahce, Natali; Sali, Andrej; Krogan, Nevan J.
• Is Part Of: Current protocols in bioinformatics. Volume 49:Issue 1(2015)
• Page Start: 8.19.1
• Page End: 8.19.16
• Publication Date: 2015-03-09
• Subjects: affinity purification mass spectrometry -- protein interactions -- scoring algorithms -- interaction networks -- proteomics; Bioinformatics -- Laboratory manuals; Nucleotide sequence -- Laboratory manuals; Amino acid sequence -- Laboratory manuals; Base Sequence; Amino Acid Sequence; Computational Biology -- methods; Databases, Genetic; Proteins -- analysis; Sequence Analysis -- methods; Sequence Homology; Amino acid sequence; Bioinformatics; Nucleotide sequence; Laboratory Manuals; Laboratory manuals; 570.285
• DOI: 10.1002/0471250953.bi0819s49
• Languages: English
• ISSNs: 1934-3396

Abstract

Abstract : High‐throughput Affinity Purification Mass Spectrometry (AP‐MS) experiments can identify a large number of protein interactions, but only a fraction of these interactions are biologically relevant. Here, we describe a comprehensive computational strategy to process raw AP‐MS data, perform quality controls, and prioritize biologically relevant bait‐prey pairs in a set of replicated AP‐MS experiments with Mass spectrometry interaction STatistics (MiST). The MiST score is a linear combination of prey quantity (abundance), abundance invariability across repeated experiments (reproducibility), and prey uniqueness relative to other baits (specificity). We describe how to run the full MiST analysis pipeline in an R environment and discuss a number of configurable options that allow the lay user to convert any large‐scale AP‐MS data into an interpretable, biologically relevant protein‐protein interaction network. © 2015 by John Wiley & Sons, Inc.