title: How to talk about protein-level false discovery rates in shotgun proteomics.
abstract: A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein-level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein-level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein-level FDRs for both competing null hypotheses.
authors: Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology - KTH, Solna, Sweden.
authors: Matthew
authors: M
authors: The
authors: Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology - KTH, Solna, Sweden.
authors: Ayesha
authors: A
authors: Tasnim
authors: Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology - KTH, Solna, Sweden. lukas.kall@scilifelab.se.
authors: Lukas
authors: L
authors: Käll
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D057166:High-Throughput Screening Assays; D011506:Proteins / Q000032:analysis* / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins
keywords: Bioinformatics; Data processing and analysis; Mass spectrometry-LC-MS/MS; Protein inference; Simulation; Statistical analysis