Literature DB >> 19714810

Comparison of novel decoy database designs for optimizing protein identification searches using ABRF sPRG2006 standard MS/MS data sets.

Luca Blanco1, Jennifer A Mead, Conrad Bessant.   

Abstract

Decoy database searches are used to filter out false positive protein identifications derived from search engines, but there is no consensus about which decoy is "the best". We evaluate nine different decoy designs using public data sets from samples of known composition. Statistically significant performance differences were found, but no single decoy stood out among the best performers. Ultimately, we recommend peptide level reverse decoys searched independently from the target.

Mesh:

Year:  2009        PMID: 19714810     DOI: 10.1021/pr800792z

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


  13 in total

1.  FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines.

Authors:  David C Wedge; Ritesh Krishna; Paul Blackhurst; Jennifer A Siepen; Andrew R Jones; Simon J Hubbard
Journal:  J Proteome Res       Date:  2011-02-21       Impact factor: 4.466

2.  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
Journal:  Mol Cell Proteomics       Date:  2011-08-29       Impact factor: 5.911

3.  Peptide identification based on fuzzy classification and clustering.

Authors:  Xijun Liang; Zhonghang Xia; Xinnan Niu; Andrew J Link; Liping Pang; Fang-Xiang Wu; Hongwei Zhang
Journal:  Proteome Sci       Date:  2013-11-07       Impact factor: 2.480

4.  A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets.

Authors:  Mikhail M Savitski; Mathias Wilhelm; Hannes Hahne; Bernhard Kuster; Marcus Bantscheff
Journal:  Mol Cell Proteomics       Date:  2015-05-17       Impact factor: 5.911

5.  DecoyPyrat: Fast Non-redundant Hybrid Decoy Sequence Generation for Large Scale Proteomics.

Authors:  James C Wright; Jyoti S Choudhary
Journal:  J Proteomics Bioinform       Date:  2016-06-27

Review 6.  A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.

Authors:  Alexey I Nesvizhskii
Journal:  J Proteomics       Date:  2010-09-08       Impact factor: 4.044

7.  COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA.

Authors:  Craig D Wenger; Douglas H Phanstiel; M Violet Lee; Derek J Bailey; Joshua J Coon
Journal:  Proteomics       Date:  2011-02-07       Impact factor: 3.984

8.  Stable isotope metabolic labeling-based quantitative phosphoproteomic analysis of Arabidopsis mutants reveals ethylene-regulated time-dependent phosphoproteins and putative substrates of constitutive triple response 1 kinase.

Authors:  Zhu Yang; Guangyu Guo; Manyu Zhang; Claire Y Liu; Qin Hu; Henry Lam; Han Cheng; Yu Xue; Jiayang Li; Ning Li
Journal:  Mol Cell Proteomics       Date:  2013-09-16       Impact factor: 5.911

9.  OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.

Authors:  Fabio R Cerqueira; Ana Tereza Ribeiro Vasconcelos
Journal:  Database (Oxford)       Date:  2020-01-01       Impact factor: 3.451

10.  MUMAL: multivariate analysis in shotgun proteomics using machine learning techniques.

Authors:  Fabio R Cerqueira; Ricardo S Ferreira; Alcione P Oliveira; Andreia P Gomes; Humberto J O Ramos; Armin Graber; Christian Baumgartner
Journal:  BMC Genomics       Date:  2012-10-19       Impact factor: 3.969

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