Abstract
BackgroundThe target-decoy strategy effectively estimates the false-discovery rate (FDR) by creating a decoy database with a size identical to that of the target database. Decoy databases are created by various methods, such as, the reverse, pseudo-reverse, shuffle, pseudo-shuffle, and the de Bruijn methods. FDR is sometimes over- or under-estimated depending on which decoy database is used because the ratios of redundant peptides in the target databases are different, that is, the numbers of unique (non-redundancy) peptides in the target and decoy databases differ.ResultsWe used two protein databases (the UniProt Saccharomyces cerevisiae protein database and the UniProt human protein database) to compare the FDRs of various decoy databases. When the ratio of redundant peptides in the target database is low, the FDR is not overestimated by any decoy construction method. However, if the ratio of redundant peptides in the target database is high, the FDR is overestimated when the (pseudo) shuffle decoy database is used. Additionally, human and S. cerevisiae six frame translation databases, which are large databases, also showed outcomes similar to that from the UniProt human protein database.ConclusionThe FDR must be estimated using the correction factor proposed by Elias and Gygi or that by Kim et al. when (pseudo) shuffle decoy databases are used.
Highlights
One of the most important steps in peptide identification is to estimate the false discovery rate (FDR)
We denote the results with 1% false-discovery rate (FDR) from the reverse, shuffle, pseudo-reverse, pseudo-shuffle, and de Bruijn methods as FDRR, FDRS, FDRPR, FDRPS, and FDRD, respectively
Saccharomyces cerevisiae dataset We compared the results for the identified Peptidespectrum match (PSM) with the 1% FDR using the S. cerevisiae Elite and 2DLC dataset, the protein database, and various decoy databases
Summary
One of the most important steps in peptide identification is to estimate the false discovery rate (FDR). To estimate the FDR, the target-decoy strategy [1] and the mixture model-based method [2, 3] have been suggested. The target-decoy strategy is easy to implement and effective, so it is frequently used [1]. The target-decoy strategy effectively estimates the FDR by creating a decoy database which is identical in size to the target database. The most frequently used is the reverse method, which creates a decoy database by reversing the. The target-decoy strategy effectively estimates the false-discovery rate (FDR) by creating a decoy database with a size identical to that of the target database. Decoy databases are created by various methods, such as, the reverse, pseudo-reverse, shuffle, pseudo-shuffle, and the de Bruijn methods. FDR is sometimes over- or under-estimated depending on which decoy database is used because the ratios of redundant peptides in the target databases are different, that is, the numbers of unique (non-redundancy) peptides in the target and decoy databases differ
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