Skinnider Michael A, Cai Charley, Stacey R Greg, Foster Leonard J
Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
Bioinformatics. 2021 Sep 9;37(17):2775-2777. doi: 10.1093/bioinformatics/btab022.
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.
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 data are available at Bioinformatics online.
我们展示了PrInCE,一个R/Bioconductor软件包,它采用机器学习方法从共分离质谱(CF-MS)数据推断蛋白质-蛋白质相互作用网络。该软件包以前以Matlab脚本集合的形式发布,我们用开源语言对其进行了全新改写,显著提高了运行时性能和内存需求。我们描述了R实现中的几个新特性,包括检测共洗脱蛋白质复合物的测试和差异网络分析方法。PrInCE有详尽的文档记录,并且与Bioconductor类完全兼容,确保它能无缝融入现有的蛋白质组学工作流程。
PrInCE可从Bioconductor获取(https://www.bioconductor.org/packages/devel/bioc/html/PrInCE.html)。源代码可在GitHub上根据MIT许可免费获取(https://github.com/fosterlab/PrInCE)。通过GitHub问题跟踪器(https://github.com/fosterlab/PrInCE/issues)提供支持。
补充数据可在《生物信息学》在线获取。