Literature DB >> 18189369

Label-free comparative analysis of proteomics mixtures using chromatographic alignment of high-resolution muLC-MS data.

Gregory L Finney1, Adele R Blackler, Michael R Hoopmann, Jesse D Canterbury, Christine C Wu, Michael J MacCoss.   

Abstract

Label-free relative quantitative proteomics is a powerful tool for the survey of protein level changes between two biological samples. We have developed and applied an algorithm using chromatographic alignment of microLC-MS runs to improve the detection of differences between complex protein mixtures. We demonstrate the performance of our software by finding differences in E. coli protein abundance upon induction of the lac operon genes using isopropyl beta-D-thiogalactopyranoside. The use of our alignment gave a 4-fold decrease in mean relative retention time error and a 6-fold increase in the number of statistically significant differences between samples. Using a conservative threshold, we have identified 5290 total microLC-MS regions that have a different abundance between these samples. Of the detected difference regions, only 23% were mapped to MS/MS peptide identifications. We detected 74 proteins that had a greater relative abundance in the induced sample and 21 with a greater abundance in the uninduced sample. We have developed an effective tool for the label-free detection of differences between samples and demonstrate an increased sensitivity following chromatographic alignment.

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Year:  2008        PMID: 18189369     DOI: 10.1021/ac701649e

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  35 in total

1.  DeMix-Q: Quantification-Centered Data Processing Workflow.

Authors:  Bo Zhang; Lukas Käll; Roman A Zubarev
Journal:  Mol Cell Proteomics       Date:  2016-01-04       Impact factor: 5.911

2.  Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells.

Authors:  Nikhil Garge; Huaqin Pan; Megan D Rowland; Benjamin J Cargile; Xinxin Zhang; Phillip C Cooley; Grier P Page; Maureen K Bunger
Journal:  Mol Cell Proteomics       Date:  2010-02-23       Impact factor: 5.911

Review 3.  Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics.

Authors:  Kelli G Kline; Greg L Finney; Christine C Wu
Journal:  Brief Funct Genomic Proteomic       Date:  2009-03

Review 4.  Image analysis tools and emerging algorithms for expression proteomics.

Authors:  Andrew W Dowsey; Jane A English; Frederique Lisacek; Jeffrey S Morris; Guang-Zhong Yang; Michael J Dunn
Journal:  Proteomics       Date:  2010-12       Impact factor: 3.984

5.  Mitochondrial proteome remodelling in pressure overload-induced heart failure: the role of mitochondrial oxidative stress.

Authors:  Dao-Fu Dai; Edward J Hsieh; Yonggang Liu; Tony Chen; Richard P Beyer; Michael T Chin; Michael J MacCoss; Peter S Rabinovitch
Journal:  Cardiovasc Res       Date:  2011-10-19       Impact factor: 10.787

6.  Protein quantification across hundreds of experimental conditions.

Authors:  Zia Khan; Joshua S Bloom; Benjamin A Garcia; Mona Singh; Leonid Kruglyak
Journal:  Proc Natl Acad Sci U S A       Date:  2009-08-26       Impact factor: 11.205

7.  Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.

Authors:  Yuliya V Karpievitch; Thomas Taverner; Joshua N Adkins; Stephen J Callister; Gordon A Anderson; Richard D Smith; Alan R Dabney
Journal:  Bioinformatics       Date:  2009-07-14       Impact factor: 6.937

8.  Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

Authors:  Brendan MacLean; Daniela M Tomazela; Nicholas Shulman; Matthew Chambers; Gregory L Finney; Barbara Frewen; Randall Kern; David L Tabb; Daniel C Liebler; Michael J MacCoss
Journal:  Bioinformatics       Date:  2010-02-09       Impact factor: 6.937

9.  Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments.

Authors:  Aleksey V Tolmachev; Matthew E Monroe; Samuel O Purvine; Ronald J Moore; Navdeep Jaitly; Joshua N Adkins; Gordon A Anderson; Richard D Smith
Journal:  Anal Chem       Date:  2008-10-15       Impact factor: 6.986

10.  Proteomic-based detection of a protein cluster dysregulated during cardiovascular development identifies biomarkers of congenital heart defects.

Authors:  Anjali K Nath; Michael Krauthammer; Puyao Li; Eugene Davidov; Lucas C Butler; Joshua Copel; Mikko Katajamaa; Matej Oresic; Irina Buhimschi; Catalin Buhimschi; Michael Snyder; Joseph A Madri
Journal:  PLoS One       Date:  2009-01-19       Impact factor: 3.240

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