Abstract
BackgroundIn mass spectrometry-based proteomics, protein identification is an essential task. Evaluating the statistical significance of the protein identification result is critical to the success of proteomics studies. Controlling the false discovery rate (FDR) is the most common method for assuring the overall quality of the set of identifications. Existing FDR estimation methods either rely on specific assumptions or rely on the two-stage calculation process of first estimating the error rates at the peptide-level, and then combining them somehow at the protein-level. We propose to estimate the FDR in a non-parametric way with less assumptions and to avoid the two-stage calculation process.ResultsWe propose a new protein-level FDR estimation framework. The framework contains two major components: the Permutation+BH (Benjamini–Hochberg) FDR estimation method and the logistic regression-based null inference method. In Permutation+BH, the null distribution of a sample is generated by searching data against a large number of permuted random protein database and therefore does not rely on specific assumptions. Then, p-values of proteins are calculated from the null distribution and the BH procedure is applied to the p-values to achieve the relationship of the FDR and the number of protein identifications. The Permutation+BH method generates the null distribution by the permutation method, which is inefficient for online identification. The logistic regression model is proposed to infer the null distribution of a new sample based on existing null distributions obtained from the Permutation+BH method.ConclusionsIn our experiment based on three public available datasets, our Permutation+BH method achieves consistently better performance than MAYU, which is chosen as the benchmark FDR calculation method for this study. The null distribution inference result shows that the logistic regression model achieves a reasonable result both in the shape of the null distribution and the corresponding FDR estimation result.
Highlights
IntroductionEvaluating the statistical significance of the protein identification result is critical to the success of proteomics studies
In mass spectrometry-based proteomics, protein identification is an essential task
Experimental MS/MS spectra are searched against a sequence database to obtain a set of peptide-spectrum matches (PSMs) [2,3,4]
Summary
Evaluating the statistical significance of the protein identification result is critical to the success of proteomics studies. Existing FDR estimation methods either rely on specific assumptions or rely on the two-stage calculation process of first estimating the error rates at the peptide-level, and combining them somehow at the protein-level. The identification of proteins is a two-stage process: peptide identification and protein inference [1]. The ability of accurately inferring proteins and directly assessing such inference results is critical to the success of proteomics studies. Many effective protein inference algorithms have been developed such as ProteinProphet, ComByne and MSBayesPro. the problem of accurate assessment of statistical significance of protein identifications remains an open question [8, 9]. Past research efforts towards this direction can be classified into p-value based approaches and false discovery rate (FDR) approaches: Wu et al BMC Genomics 2018, 19(Suppl 6):567
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