Understanding Data Analysis Steps in Mass-Spectrometry-Based Proteomics Is Key to Transparent Reporting

Mass spectrometry (MS)-based proteomics data analysis
is composed
of many stages from quality control, data cleaning, and normalization
to statistical and functional analysis, without forgetting multiple
visualization steps. All of these need to be reported next to published
results to make them fully understandable and reusable for the community.
Although this seems straightforward, exhaustively reporting all aspects
of an analysis workflow can be tedious and error prone. This letter
reports good practices when describing data analysis of MS-based proteomics
data and discusses why and how the community should put efforts into
more transparently reporting data analysis workflows.