Target-decoy false discovery rate estimation using Crema.

Autor: Lin A; Chemical and Biological Signatures, Pacific Northwest National Laboratory, Seattle, Washington, USA., See D; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA., Fondrie WE; Talus Bioscience, Seattle, Washington, USA., Keich U; School of Mathematics and Statistics, University of Sydney, Sydney, Australia., Noble WS; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.; Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
Jazyk: angličtina
Zdroj: Proteomics [Proteomics] 2024 Apr; Vol. 24 (8), pp. e2300084. Date of Electronic Publication: 2024 Feb 21.
DOI: 10.1002/pmic.202300084
Abstrakt: Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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