PrInCE: an R/Bioconductor package for protein-protein interaction network inference from co-fractionation mass spectrometry data

Michael A Skinnider; Charley Cai; R Greg Stacey; Leonard J Foster

doi:10.1093/bioinformatics/btab022

PrInCE: an R/Bioconductor package for protein-protein interaction network inference from co-fractionation mass spectrometry data

Bioinformatics. 2021 Sep 9;37(17):2775-2777. doi: 10.1093/bioinformatics/btab022.

Authors

Michael A Skinnider¹, Charley Cai¹, R Greg Stacey¹, Leonard J Foster^{1

2}

Affiliations

¹ Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
² Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.

PMID: 33471077
DOI: 10.1093/bioinformatics/btab022

Abstract

Summary: We present PrInCE, an R/Bioconductor package that employs a machine-learning approach to infer protein-protein interaction networks from co-fractionation mass spectrometry (CF-MS) data. Previously distributed as a collection of Matlab scripts, our ground-up rewrite of this software package in an open-source language dramatically improves runtime and memory requirements. We describe several new features in the R implementation, including a test for the detection of co-eluting protein complexes and a method for differential network analysis. PrInCE is extensively documented and fully compatible with Bioconductor classes, ensuring it can fit seamlessly into existing proteomics workflows.

Availability and implementation: PrInCE is available from Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/PrInCE.html). Source code is freely available from GitHub under the MIT license (https://github.com/fosterlab/PrInCE). Support is provided via the GitHub issues tracker (https://github.com/fosterlab/PrInCE/issues).

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

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