Unit
UNIT 8.19 Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST
Published Online: 9 MAR 2015
DOI: 10.1002/0471250953.bi0819s49
Copyright © 2013 John Wiley & Sons, Inc. All rights reserved.
Lab Protocol Title

Current Protocols in Bioinformatics
Additional Information
How to Cite
, , , , , and 2015. Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST. Curr. Protoc. Bioinform. 49:8.19.1-8.19.16. doi: 10.1002/0471250953.bi0819s49
Publication History
- Published Online: 9 MAR 2015
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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.
Keywords:
- affinity purification mass spectrometry;
- protein interactions;
- scoring algorithms;
- interaction networks;
- proteomics
