ArticleBMC bioinformatics2020
Transfer posterior error probability estimation for peptide identification.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.Molecular & cellular proteomics : MCP · 2025Article
- An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics.Genome biology · 2022Article
- Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data.Journal of proteome research · 2022Article
- Discovery and Visualization of Uncharacterized Drug-Protein Adducts Using Mass Spectrometry.Analytical chemistry · 2022Article
- caAtlas: An immunopeptidome atlas of human cancer.iScience · 2021Article
- Accurately Assigning Peptides to Spectra When Only a Subset of Peptides Are Relevant.Journal of proteome research · 2021Article
- mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.Journal of proteome research · 2021Article
- Calculating Glycoprotein Similarities From Mass Spectrometric Data.Molecular & cellular proteomics : MCP · 2021Review
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3 authors.
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Abstract
backgroundIn shotgun proteomics, database searching of tandem mass spectra results in a great number of peptide-spectrum matches (PSMs), many of which are false positives. Quality control of PSMs is a multiple hypothesis testing problem, and the false discovery rate (FDR) or the posterior error probability (PEP) is the commonly used statistical confidence measure. PEP, also called local FDR, can evaluate the confidence of individual PSMs and thus is more desirable than FDR, which evaluates the global confidence of a collection of PSMs. Estimation of PEP can be achieved by decomposing the null and alternative distributions of PSM scores as long as the given data is sufficient. However, in many proteomic studies, only a group (subset) of PSMs, e.g. those with specific post-translational modifications, are of interest. The group can be very small, making the direct PEP estimation by the group data inaccurate, especially for the high-score area where the score threshold is taken. Using the whole set of PSMs to estimate the group PEP is inappropriate either, because the null and/or alternative distributions of the group can be very different from those of combined scores.
resultsThe transfer PEP algorithm is proposed to more accurately estimate the PEPs of peptide identifications in small groups. Transfer PEP derives the group null distribution through its empirical relationship with the combined null distribution, and estimates the group alternative distribution, as well as the null proportion, using an iterative semi-parametric method. Validated on both simulated data and real proteomic data, transfer PEP showed remarkably higher accuracy than the direct combined and separate PEP estimation methods.
conclusionsWe presented a novel approach to group PEP estimation for small groups and implemented it for the peptide identification problem in proteomics. The methodology of the approach is in principle applicable to the small-group PEP estimation problems in other fields.
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