ProInfer: an interpretable protein inference tool leveraging on biological networks

• Main Authors: Peng, Hui, Wong, Limsoon, Goh, Wilson Wen Bin
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2023
• Subjects: Science::Biological sciences; Databases, Protein; Proteome
• Online Access: https://hdl.handle.net/10356/169285
• Institution: Nanyang Technological University

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.