Literature DB >> 19743429

When less can yield more - Computational preprocessing of MS/MS spectra for peptide identification.

Bernhard Y Renard1, Marc Kirchner, Flavio Monigatti, Alexander R Ivanov, Juri Rappsilber, Dominic Winter, Judith A J Steen, Fred A Hamprecht, Hanno Steen.   

Abstract

The effectiveness of database search algorithms, such as Mascot, Sequest and ProteinPilot is limited by the quality of the input spectra: spurious peaks in MS/MS spectra can jeopardize the correct identification of peptides or reduce their score significantly. Consequently, an efficient preprocessing of MS/MS spectra can increase the sensitivity of peptide identification at reduced file sizes and run time without compromising its specificity. We investigate the performance of 25 MS/MS preprocessing methods on various data sets and make software for improved preprocessing of mgf/dta-files freely available from http://hci.iwr.uni-heidelberg.de/mip/proteomics or http://www.childrenshospital.org/research/steenlab.

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Year:  2009        PMID: 19743429     DOI: 10.1002/pmic.200900326

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  26 in total

1.  One-step sample concentration, purification, and albumin depletion method for urinary proteomics.

Authors:  Ali R Vaezzadeh; Andrew C Briscoe; Hanno Steen; Richard S Lee
Journal:  J Proteome Res       Date:  2010-10-15       Impact factor: 4.466

2.  The relative charge ratio between C and N atoms in amide bond acts as a key factor to determine peptide fragment efficiency in different charge states.

Authors:  Feng Sun; Wansong Zong; Rutao Liu; Meijie Wang; Pengjun Zhang; Qifei Xu
Journal:  J Am Soc Mass Spectrom       Date:  2010-07-08       Impact factor: 3.109

3.  Quantitative proteomics analysis reveals molecular networks regulated by epidermal growth factor receptor level in head and neck cancer.

Authors:  Wei Yang; Quan Cai; Vivian W Y Lui; Patrick A Everley; Jayoung Kim; Neil Bhola; Kelly M Quesnelle; Bruce R Zetter; Hanno Steen; Michael R Freeman; Jennifer R Grandis
Journal:  J Proteome Res       Date:  2010-06-04       Impact factor: 4.466

4.  Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification.

Authors:  Chad R Weisbrod; Jimmy K Eng; Michael R Hoopmann; Tahmina Baker; James E Bruce
Journal:  J Proteome Res       Date:  2012-02-21       Impact factor: 4.466

Review 5.  Building and searching tandem mass spectral libraries for peptide identification.

Authors:  Henry Lam
Journal:  Mol Cell Proteomics       Date:  2011-09-06       Impact factor: 5.911

6.  Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry.

Authors:  Himanshu Grover; Garrick Wallstrom; Christine C Wu; Vanathi Gopalakrishnan
Journal:  OMICS       Date:  2013-01-05

7.  Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

Authors:  Pratik Jagtap; Sricharan Bandhakavi; LeeAnn Higgins; Thomas McGowan; Rongxiao Sa; Matthew D Stone; John Chilton; Edgar A Arriaga; Sean L Seymour; Timothy J Griffin
Journal:  Proteomics       Date:  2012-06       Impact factor: 3.984

8.  Co-regulation proteomics reveals substrates and mechanisms of APC/C-dependent degradation.

Authors:  Sasha A Singh; Dominic Winter; Marc Kirchner; Ruchi Chauhan; Saima Ahmed; Nurhan Ozlu; Amit Tzur; Judith A Steen; Hanno Steen
Journal:  EMBO J       Date:  2014-02-06       Impact factor: 11.598

9.  Sialic acid-focused quantitative mouse serum glycoproteomics by multiple reaction monitoring assay.

Authors:  Masaki Kurogochi; Takahiko Matsushista; Maho Amano; Jun-ichi Furukawa; Yasuro Shinohara; Masato Aoshima; Shin-Ichiro Nishimura
Journal:  Mol Cell Proteomics       Date:  2010-06-22       Impact factor: 5.911

10.  A proteomics search algorithm specifically designed for high-resolution tandem mass spectra.

Authors:  Craig D Wenger; Joshua J Coon
Journal:  J Proteome Res       Date:  2013-01-31       Impact factor: 4.466

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