Mayu

Mayu estimates protein-level false discovery rates (FDRs) in proteomics datasets to provide accurate confidence measures for protein identifications derived from tandem mass spectrometry.

Key Features:

• Protein-level FDR estimation: Computes FDRs at the protein identification level using peptide-spectrum match data to address uncertainties not captured by peptide-level measures.
• Data set size consideration: Accounts for the influence of large and heterogeneous data set size on the reliability of protein identifications.
• Empirical validation of elevated protein FDRs: Demonstrates, using large proteomics datasets, that protein-level false discovery rates can be substantially higher than peptide-spectrum match FDRs.
• Proteome data quality control: Provides metrics to control and assess the quality of protein identifications in proteome repositories and integrated datasets.

Scientific Applications:

• Large-scale proteome identification: Supports projects aiming for saturation coverage of a proteome or specific subproteomes by supplying protein-level confidence measures.
• Repository and downstream study quality assessment: Enables quality control of proteome repositories and more reliable protein identification for basic biological and applied biomedical analyses.

Methodology:

Integrates with workflows such as the Trans-Proteomic Pipeline and estimates protein-level FDRs from peptide-spectrum matches derived from tandem mass spectrometry of tryptic digests.