Comprehensive Protein Inference Analysis with PyProteinInference Elucidates Biological Understanding of Tandem Mass Spectrometry Data.

Abstract

Selection and application of protein inference algorithms can have a significant impact on the data output from tandem mass spectrometry (MS/MS) experiments. However, this critical step is often taken for granted, with many studies simply utilizing the inference method embedded within the end-to-end software pipeline employed for analysis without consideration of the particular algorithm's suitability for the experiment at hand or its effects on the resulting data. Although many individual inference algorithms have been demonstrated, few unified tools are available that allow the researcher to quickly apply a variety of different inference algorithms to meet the needs of their analysis, are agnostic of other tools in the analysis pipeline, and are easy to use for the bench biologist. PyProteinInference provides a comprehensive suite of tools that enable researchers to apply different inference algorithms and compute protein-level set-based false discovery rates (FDR) from MS/MS data through a unified interface. Here, we describe the software and its application to a traditional protein inference benchmarking data set and to a K562 whole-cell lysate to demonstrate its utility in facilitating conclusions about underlying biological mechanisms in proteomic data.