Literature DB >> 30221945

Combining High-Resolution and Exact Calibration To Boost Statistical Power: A Well-Calibrated Score Function for High-Resolution MS2 Data.

Andy Lin1, J Jeffry Howbert1, William Stafford Noble1,2.   

Abstract

To achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high-resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine's scores are well calibrated, that is, that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum, has proven to be challenging. Here we describe a database search score function, the "residue evidence" (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a "combined p value" score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p value to the score functions used by several existing search engines. Our results suggest that the combined p value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit ( http://crux.ms ).

Entities:  

Keywords:  database search; high-resolution MS2; statistical calibration

Mesh:

Substances:

Year:  2018        PMID: 30221945      PMCID: PMC6342018          DOI: 10.1021/acs.jproteome.8b00206

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  32 in total

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3.  Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies.

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5.  Rapid and accurate peptide identification from tandem mass spectra.

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6.  iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates.

Authors:  David Shteynberg; Eric W Deutsch; Henry Lam; Jimmy K Eng; Zhi Sun; Natalie Tasman; Luis Mendoza; Robert L Moritz; Ruedi Aebersold; Alexey I Nesvizhskii
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7.  PeptideShaker enables reanalysis of MS-derived proteomics data sets.

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8.  Computing exact p-values for a cross-correlation shotgun proteomics score function.

Authors:  J Jeffry Howbert; William Stafford Noble
Journal:  Mol Cell Proteomics       Date:  2014-06-02       Impact factor: 5.911

9.  Crux: rapid open source protein tandem mass spectrometry analysis.

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Journal:  J Proteome Res       Date:  2014-09-09       Impact factor: 4.466

10.  MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra.

Authors:  Viktoria Dorfer; Peter Pichler; Thomas Stranzl; Johannes Stadlmann; Thomas Taus; Stephan Winkler; Karl Mechtler
Journal:  J Proteome Res       Date:  2014-06-26       Impact factor: 4.466

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  3 in total

1.  Accurately Assigning Peptides to Spectra When Only a Subset of Peptides Are Relevant.

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Review 2.  Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis.

Authors:  Chen Chen; Jie Hou; John J Tanner; Jianlin Cheng
Journal:  Int J Mol Sci       Date:  2020-04-20       Impact factor: 5.923

3.  mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.

Authors:  William E Fondrie; William S Noble
Journal:  J Proteome Res       Date:  2021-02-17       Impact factor: 5.370

  3 in total

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