Literature DB >> 17640002

Capture and analysis of quantitative proteomic data.

King Wai Lau1, Andrew R Jones, Neil Swainston, Jennifer A Siepen, Simon J Hubbard.   

Abstract

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.

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Mesh:

Year:  2007        PMID: 17640002      PMCID: PMC2260796          DOI: 10.1002/pmic.200700127

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


  51 in total

1.  Accurate quantitation of protein expression and site-specific phosphorylation.

Authors:  Y Oda; K Huang; F R Cross; D Cowburn; B T Chait
Journal:  Proc Natl Acad Sci U S A       Date:  1999-06-08       Impact factor: 11.205

2.  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

3.  A correlation algorithm for the automated quantitative analysis of shotgun proteomics data.

Authors:  Michael J MacCoss; Christine C Wu; Hongbin Liu; Rovshan Sadygov; John R Yates
Journal:  Anal Chem       Date:  2003-12-15       Impact factor: 6.986

4.  ZoomQuant: an application for the quantitation of stable isotope labeled peptides.

Authors:  Brian D Halligan; Ronit Y Slyper; Simon N Twigger; Wayne Hicks; Michael Olivier; Andrew S Greene
Journal:  J Am Soc Mass Spectrom       Date:  2005-01-13       Impact factor: 3.109

5.  Automated quantification tool for high-throughput proteomics using stable isotope labeling and LC-MSn.

Authors:  Guanghui Wang; Wells W Wu; Trairak Pisitkun; Jason D Hoffert; Mark A Knepper; Rong-Fong Shen
Journal:  Anal Chem       Date:  2006-08-15       Impact factor: 6.986

6.  ProteomeCommons.org IO Framework: reading and writing multiple proteomics data formats.

Authors:  J A Falkner; J W Falkner; P C Andrews
Journal:  Bioinformatics       Date:  2006-11-22       Impact factor: 6.937

7.  Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry.

Authors:  J E Eckel-Passow; A L Oberg; T M Therneau; C J Mason; D W Mahoney; K L Johnson; J E Olson; H R Bergen
Journal:  Bioinformatics       Date:  2006-09-05       Impact factor: 6.937

Review 8.  Mass spectrometry.

Authors:  A L Burlingame; R K Boyd; S J Gaskell
Journal:  Anal Chem       Date:  1998-08-15       Impact factor: 6.986

9.  Large-scale identification of Caenorhabditis elegans proteins by multidimensional liquid chromatography-tandem mass spectrometry.

Authors:  Kwasi G Mawuenyega; Hiroyuki Kaji; Yoshio Yamuchi; Takashi Shinkawa; Haruna Saito; Masato Taoka; Nobuhiro Takahashi; Toshiaki Isobe
Journal:  J Proteome Res       Date:  2003 Jan-Feb       Impact factor: 4.466

10.  A proteomic view of the Plasmodium falciparum life cycle.

Authors:  Laurence Florens; Michael P Washburn; J Dale Raine; Robert M Anthony; Munira Grainger; J David Haynes; J Kathleen Moch; Nemone Muster; John B Sacci; David L Tabb; Adam A Witney; Dirk Wolters; Yimin Wu; Malcolm J Gardner; Anthony A Holder; Robert E Sinden; John R Yates; Daniel J Carucci
Journal:  Nature       Date:  2002-10-03       Impact factor: 49.962

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  10 in total

1.  Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.

Authors:  Ann L Oberg; Douglas W Mahoney; Jeanette E Eckel-Passow; Christopher J Malone; Russell D Wolfinger; Elizabeth G Hill; Leslie T Cooper; Oyere K Onuma; Craig Spiro; Terry M Therneau; H Robert Bergen
Journal:  J Proteome Res       Date:  2008-01-04       Impact factor: 4.466

2.  Global relative quantification with liquid chromatography-matrix-assisted laser desorption ionization time-of-flight (LC-MALDI-TOF)--cross-validation with LTQ-Orbitrap proves reliability and reveals complementary ionization preferences.

Authors:  Bernd Hessling; Knut Büttner; Michael Hecker; Dörte Becher
Journal:  Mol Cell Proteomics       Date:  2013-06-20       Impact factor: 5.911

3.  Proteomics profiling of human embryonic stem cells in the early differentiation stage.

Authors:  Atara Novak; Michal Amit; Tamar Ziv; Hanna Segev; Bettina Fishman; Arie Admon; Joseph Itskovitz-Eldor
Journal:  Stem Cell Rev Rep       Date:  2012-03       Impact factor: 5.739

4.  Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ).

Authors:  Jenny Forshed; Henrik J Johansson; Maria Pernemalm; Rui M M Branca; Annsofi Sandberg; Janne Lehtiö
Journal:  Mol Cell Proteomics       Date:  2011-07-06       Impact factor: 5.911

5.  Statistical inference from multiple iTRAQ experiments without using common reference standards.

Authors:  Shelley M Herbrich; Robert N Cole; Keith P West; Kerry Schulze; James D Yager; John D Groopman; Parul Christian; Lee Wu; Robert N O'Meally; Damon H May; Martin W McIntosh; Ingo Ruczinski
Journal:  J Proteome Res       Date:  2013-01-16       Impact factor: 4.466

6.  Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

Authors:  Yuan Tian; Aik-Choon Tan; Xiaer Sun; Matthew T Olson; Zhi Xie; Natini Jinawath; Daniel W Chan; Ie-Ming Shih; Zhen Zhang; Hui Zhang
Journal:  Proteomics Clin Appl       Date:  2009-11       Impact factor: 3.494

7.  Motif-specific sampling of phosphoproteomes.

Authors:  Cristian I Ruse; Daniel B McClatchy; Bingwen Lu; Daniel Cociorva; Akira Motoyama; Sung Kyu Park; John R Yates
Journal:  J Proteome Res       Date:  2008-05       Impact factor: 4.466

8.  Statistical model to analyze quantitative proteomics data obtained by 18O/16O labeling and linear ion trap mass spectrometry: application to the study of vascular endothelial growth factor-induced angiogenesis in endothelial cells.

Authors:  Inmaculada Jorge; Pedro Navarro; Pablo Martínez-Acedo; Estefanía Núñez; Horacio Serrano; Arántzazu Alfranca; Juan Miguel Redondo; Jesús Vázquez
Journal:  Mol Cell Proteomics       Date:  2009-01-29       Impact factor: 5.911

9.  A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis.

Authors:  Faviel F Gonzalez-Galarza; Craig Lawless; Simon J Hubbard; Jun Fan; Conrad Bessant; Henning Hermjakob; Andrew R Jones
Journal:  OMICS       Date:  2012-07-17

Review 10.  Standardization and quality control in quantifying non-enzymatic oxidative protein modifications in relation to ageing and disease: Why is it important and why is it hard?

Authors:  Olgica Nedić; Adelina Rogowska-Wrzesinska; Suresh I S Rattan
Journal:  Redox Biol       Date:  2015-04-14       Impact factor: 11.799

  10 in total

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