The Null-Test for peptide identification algorithm in Shotgun proteomics

Zhang, Shurong; Shan, Yichu; Jiang, Hao; Liu, Jianhui; Zhou, Yuan; Zhang, Lihua; Zhang, Yukui

doi:10.1016/j.jprot.2017.05.010

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…The increasing complexity of the peptide identification pipelines and the advancement in the mass spectrometric instrumentation yield new challenges for maintaining fair FDR estimation, and old approaches may fail to reveal bias in modern methods. For instance, the null test and the decoy–decoy search approaches are ineffective at pinpointing the bias we showed in this Letter because these methods do not utilize the target peptide set (see Supplementary Note S1.2), and thus they do not consider the differences between the distribution of or the correlation between the theoretical spectra of the target and decoy peptides. Granholm et al proposed a so-called semilabeled method to demonstrate biased features in Percolator using so-called entrapment sequences .…”

Section: Discussionmentioning

confidence: 99%

Bias in False Discovery Rate Estimation in Mass-Spectrometry-Based Peptide Identification

et al. 2019

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Accurate target-decoy-based false discovery rate (FDR) control of peptide identification from tandem mass-spectrometry data relies on an important but often neglected assumption that incorrect spectrum annotations are equally likely to receive either target or decoy peptides. Here we argue that this assumption is often violated in practice, even by popular methods. Preference can be given to target peptides by biased scoring functions, which result in liberal FDR estimations, or to decoy peptides by correlated spectra, which result in conservative estimations.

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Section: Discussionmentioning

confidence: 99%

Bias in False Discovery Rate Estimation in Mass-Spectrometry-Based Peptide Identification

et al. 2019

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“…Most importantly, some tools could overfit and report a list of results that, even though satisfying a given FDR, were still not trustworthy 6 . SEPro was benchmarked using a semi-labeled decoy approach to ensure the trustworthiness of its results and was later certified by other groups 19 . A new protocol was published 20 reflecting the changes that led to PatternLab 3.…”

Section: Development Of the Protocolmentioning

confidence: 99%

“…Typically, peptide identifications are assessed by searching datasets of scrambled sequences or including unlabeled decoy sequences and quantitation extraction can be evaluated on datasets generated with spiked proteins of known amounts. Zhang and collaborators compared the trustworthiness of search engine identifications and reported PatternLab as the only algorithm to identify no decoy proteins when searching datasets containing only decoys 19 .…”

Section: Plv Provides Confident Identificationsmentioning

confidence: 99%

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V

et al. 2022

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Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE.

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The Null-Test for peptide identification algorithm in Shotgun proteomics

Cited by 2 publications

References 36 publications

Bias in False Discovery Rate Estimation in Mass-Spectrometry-Based Peptide Identification

Bias in False Discovery Rate Estimation in Mass-Spectrometry-Based Peptide Identification

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V

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