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

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.