Literature DB >> 21520941

ScanRanker: Quality assessment of tandem mass spectra via sequence tagging.

Ze-Qiang Ma1, Matthew C Chambers, Amy-Joan L Ham, Kristin L Cheek, Corbin W Whitwell, Hans-Rudolf Aerni, Birgit Schilling, Aaron W Miller, Richard M Caprioli, David L Tabb.   

Abstract

In shotgun proteomics, protein identification by tandem mass spectrometry relies on bioinformatics tools. Despite recent improvements in identification algorithms, a significant number of high quality spectra remain unidentified for various reasons. Here we present ScanRanker, an open-source tool that evaluates the quality of tandem mass spectra via sequence tagging with reliable performance in data from different instruments. The superior performance of ScanRanker enables it not only to find unassigned high quality spectra that evade identification through database search but also to select spectra for de novo sequencing and cross-linking analysis. In addition, we demonstrate that the distribution of ScanRanker scores predicts the richness of identifiable spectra among multiple LC-MS/MS runs in an experiment, and ScanRanker scores assist the process of peptide assignment validation to increase confident spectrum identifications. The source code and executable versions of ScanRanker are available from http://fenchurch.mc.vanderbilt.edu.

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Year:  2011        PMID: 21520941      PMCID: PMC3128668          DOI: 10.1021/pr200118r

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


  30 in total

1.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Authors:  Andrew Keller; Alexey I Nesvizhskii; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2002-10-15       Impact factor: 6.986

2.  Computational analysis of unassigned high-quality MS/MS spectra in proteomic data sets.

Authors:  Kang Ning; Damian Fermin; Alexey I Nesvizhskii
Journal:  Proteomics       Date:  2010-07       Impact factor: 3.984

3.  Automatic quality assessment of peptide tandem mass spectra.

Authors:  Marshall Bern; David Goldberg; W Hayes McDonald; John R Yates
Journal:  Bioinformatics       Date:  2004-08-04       Impact factor: 6.937

4.  TANDEM: matching proteins with tandem mass spectra.

Authors:  Robertson Craig; Ronald C Beavis
Journal:  Bioinformatics       Date:  2004-02-19       Impact factor: 6.937

5.  PepNovo: de novo peptide sequencing via probabilistic network modeling.

Authors:  Ari Frank; Pavel Pevzner
Journal:  Anal Chem       Date:  2005-02-15       Impact factor: 6.986

6.  Quality assessment of tandem mass spectra based on cumulative intensity normalization.

Authors:  Seungjin Na; Eunok Paek
Journal:  J Proteome Res       Date:  2006-12       Impact factor: 4.466

7.  MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis.

Authors:  David L Tabb; Christopher G Fernando; Matthew C Chambers
Journal:  J Proteome Res       Date:  2007-02       Impact factor: 4.466

8.  Error-tolerant identification of peptides in sequence databases by peptide sequence tags.

Authors:  M Mann; M Wilm
Journal:  Anal Chem       Date:  1994-12-15       Impact factor: 6.986

9.  A ranking-based scoring function for peptide-spectrum matches.

Authors:  Ari M Frank
Journal:  J Proteome Res       Date:  2009-05       Impact factor: 4.466

Review 10.  Probing native protein structures by chemical cross-linking, mass spectrometry, and bioinformatics.

Authors:  Alexander Leitner; Thomas Walzthoeni; Abdullah Kahraman; Franz Herzog; Oliver Rinner; Martin Beck; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2010-03-31       Impact factor: 5.911

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  12 in total

1.  Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment.

Authors:  Surendra Dasari; Matthew C Chambers; Misti A Martinez; Kristin L Carpenter; Amy-Joan L Ham; Lorenzo J Vega-Montoto; David L Tabb
Journal:  J Proteome Res       Date:  2012-01-27       Impact factor: 4.466

2.  JUMPg: An Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells.

Authors:  Yuxin Li; Xusheng Wang; Ji-Hoon Cho; Timothy I Shaw; Zhiping Wu; Bing Bai; Hong Wang; Suiping Zhou; Thomas G Beach; Gang Wu; Jinghui Zhang; Junmin Peng
Journal:  J Proteome Res       Date:  2016-06-13       Impact factor: 4.466

3.  QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation.

Authors:  Ze-Qiang Ma; Kenneth O Polzin; Surendra Dasari; Matthew C Chambers; Birgit Schilling; Bradford W Gibson; Bao Q Tran; Lorenzo Vega-Montoto; Daniel C Liebler; David L Tabb
Journal:  Anal Chem       Date:  2012-06-27       Impact factor: 6.986

Review 4.  Quality assessment for clinical proteomics.

Authors:  David L Tabb
Journal:  Clin Biochem       Date:  2012-12-12       Impact factor: 3.281

5.  Characterization of the Molecular Mechanisms Underlying Glucose Stimulated Insulin Secretion from Isolated Pancreatic β-cells Using Post-translational Modification Specific Proteomics (PTMomics).

Authors:  Taewook Kang; Pia Jensen; Honggang Huang; Gitte Lund Christensen; Nils Billestrup; Martin R Larsen
Journal:  Mol Cell Proteomics       Date:  2017-11-07       Impact factor: 5.911

6.  A unique covalent bond in basement membrane is a primordial innovation for tissue evolution.

Authors:  Aaron L Fidler; Roberto M Vanacore; Sergei V Chetyrkin; Vadim K Pedchenko; Gautam Bhave; Viravuth P Yin; Cody L Stothers; Kristie Lindsey Rose; W Hayes McDonald; Travis A Clark; Dorin-Bogdan Borza; Robert E Steele; Michael T Ivy; Julie K Hudson; Billy G Hudson
Journal:  Proc Natl Acad Sci U S A       Date:  2013-12-16       Impact factor: 11.205

7.  A tool to evaluate correspondence between extraction ion chromatographic peaks and peptide-spectrum matches in shotgun proteomics experiments.

Authors:  Cristian I Ruse; Samantha Peacock; Cornel Ghiban; Keith Rivera; Darryl J Pappin; Peter Leopold
Journal:  Proteomics       Date:  2013-07-11       Impact factor: 3.984

Review 8.  Metaproteomics of complex microbial communities in biogas plants.

Authors:  Robert Heyer; Fabian Kohrs; Udo Reichl; Dirk Benndorf
Journal:  Microb Biotechnol       Date:  2015-04-15       Impact factor: 5.813

9.  Development of an enhanced metaproteomic approach for deepening the microbiome characterization of the human infant gut.

Authors:  Weili Xiong; Richard J Giannone; Michael J Morowitz; Jillian F Banfield; Robert L Hettich
Journal:  J Proteome Res       Date:  2014-11-12       Impact factor: 4.466

Review 10.  Soil and leaf litter metaproteomics-a brief guideline from sampling to understanding.

Authors:  Katharina M Keiblinger; Stephan Fuchs; Sophie Zechmeister-Boltenstern; Katharina Riedel
Journal:  FEMS Microbiol Ecol       Date:  2016-08-21       Impact factor: 4.194

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