Literature DB >> 21761931

Faster SEQUEST searching for peptide identification from tandem mass spectra.

Benjamin J Diament1, William Stafford Noble.   

Abstract

Computational analysis of mass spectra remains the bottleneck in many proteomics experiments. SEQUEST was one of the earliest software packages to identify peptides from mass spectra by searching a database of known peptides. Though still popular, SEQUEST performs slowly. Crux and TurboSEQUEST have successfully sped up SEQUEST by adding a precomputed index to the search, but the demand for ever-faster peptide identification software continues to grow. Tide, introduced here, is a software program that implements the SEQUEST algorithm for peptide identification and that achieves a dramatic speedup over Crux and SEQUEST. The optimization strategies detailed here employ a combination of algorithmic and software engineering techniques to achieve speeds up to 170 times faster than a recent version of SEQUEST that uses indexing. For example, on a single Xeon CPU, Tide searches 10,000 spectra against a tryptic database of 27,499 Caenorhabditis elegans proteins at a rate of 1550 spectra per second, which compares favorably with a rate of 8.8 spectra per second for a recent version of SEQUEST with index running on the same hardware.

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Year:  2011        PMID: 21761931      PMCID: PMC3166376          DOI: 10.1021/pr101196n

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


  27 in total

1.  Role of accurate mass measurement (+/- 10 ppm) in protein identification strategies employing MS or MS/MS and database searching.

Authors:  K R Clauser; P Baker; A L Burlingame
Journal:  Anal Chem       Date:  1999-07-15       Impact factor: 6.986

2.  SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database.

Authors:  V Bafna; N Edwards
Journal:  Bioinformatics       Date:  2001       Impact factor: 6.937

3.  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

4.  OLAV: towards high-throughput tandem mass spectrometry data identification.

Authors:  Jacques Colinge; Alexandre Masselot; Marc Giron; Thierry Dessingy; Jérôme Magnin
Journal:  Proteomics       Date:  2003-08       Impact factor: 3.984

5.  Open mass spectrometry search algorithm.

Authors:  Lewis Y Geer; Sanford P Markey; Jeffrey A Kowalak; Lukas Wagner; Ming Xu; Dawn M Maynard; Xiaoyu Yang; Wenyao Shi; Stephen H Bryant
Journal:  J Proteome Res       Date:  2004 Sep-Oct       Impact factor: 4.466

6.  Parallel tandem: a program for parallel processing of tandem mass spectra using PVM or MPI and X!Tandem.

Authors:  Dexter T Duncan; Robertson Craig; Andrew J Link
Journal:  J Proteome Res       Date:  2005 Sep-Oct       Impact factor: 4.466

7.  Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry.

Authors:  Marshall Bern; Yuhan Cai; David Goldberg
Journal:  Anal Chem       Date:  2007-01-23       Impact factor: 6.986

8.  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

9.  A fast SEQUEST cross correlation algorithm.

Authors:  Jimmy K Eng; Bernd Fischer; Jonas Grossmann; Michael J Maccoss
Journal:  J Proteome Res       Date:  2008-09-06       Impact factor: 4.466

10.  Novel peptide identification from tandem mass spectra using ESTs and sequence database compression.

Authors:  Nathan J Edwards
Journal:  Mol Syst Biol       Date:  2007-04-17       Impact factor: 11.429

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  62 in total

Review 1.  Replacing immunoassays with tryptic digestion-peptide immunoaffinity enrichment and LC-MS/MS.

Authors:  Jessica O Becker; Andrew N Hoofnagle
Journal:  Bioanalysis       Date:  2012-02       Impact factor: 2.681

2.  Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

Authors:  John T Halloran; Jeff A Bilmes; William S Noble
Journal:  J Proteome Res       Date:  2016-07-22       Impact factor: 4.466

3.  A mass spectrometry proteomics data management platform.

Authors:  Vagisha Sharma; Jimmy K Eng; Michael J Maccoss; Michael Riffle
Journal:  Mol Cell Proteomics       Date:  2012-05-18       Impact factor: 5.911

4.  2018 YPIC Challenge: A Case Study in Characterizing an Unknown Protein Sample.

Authors:  Lindsay Pino; Andy Lin; Wout Bittremieux
Journal:  J Proteome Res       Date:  2019-10-07       Impact factor: 4.466

5.  Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

Authors:  Uri Keich; Kaipo Tamura; William Stafford Noble
Journal:  J Proteome Res       Date:  2019-01-03       Impact factor: 4.466

6.  Mass spectrometry-based protein identification with accurate statistical significance assignment.

Authors:  Gelio Alves; Yi-Kuo Yu
Journal:  Bioinformatics       Date:  2014-10-31       Impact factor: 6.937

7.  ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity.

Authors:  T Xu; S K Park; J D Venable; J A Wohlschlegel; J K Diedrich; D Cociorva; B Lu; L Liao; J Hewel; X Han; C C L Wong; B Fonslow; C Delahunty; Y Gao; H Shah; J R Yates
Journal:  J Proteomics       Date:  2015-07-11       Impact factor: 4.044

Review 8.  Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms.

Authors:  Sinjini Sikdar; Ryan Gill; Susmita Datta
Journal:  Brief Bioinform       Date:  2015-07-03       Impact factor: 11.622

9.  Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.

Authors:  Matthew The; Lukas Käll
Journal:  Mol Cell Proteomics       Date:  2018-11-27       Impact factor: 5.911

10.  MaSS-Simulator: A Highly Configurable Simulator for Generating MS/MS Datasets for Benchmarking of Proteomics Algorithms.

Authors:  Muaaz Gul Awan; Fahad Saeed
Journal:  Proteomics       Date:  2018-09-28       Impact factor: 3.984

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