Literature DB >> 17269738

Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ).

Chee Sian Gan1, Poh Kuan Chong, Trong Khoa Pham, Phillip C Wright.   

Abstract

We assess the reliability of isobaric-tags for relative and absolute quantitation (iTRAQ), based on different types of replicate analyses taking into account technical, experimental, and biological variations. In total, 10 iTRAQ experiments were analyzed across three domains of life involving Saccharomyces cerevisiae KAY446, Sulfolobus solfataricus P2, and Synechocystis sp. PCC 6803. The coverage of protein expression of iTRAQ analysis increases as the variation tolerance increases. In brief, a cutoff point at +/-50% variation (+/-0.50) would yield 88% coverage in quantification based on an analysis of biological replicates. Technical replicate analysis produces a higher coverage level of 95% at a lower cutoff point of +/-30% variation. Experimental or iTRAQ variations exhibit similar behavior as biological variations, which suggest that most of the measurable deviations come from biological variations. These findings underline the importance of replicate analysis as a validation tool and benchmarking technique in protein expression analysis.

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Year:  2007        PMID: 17269738     DOI: 10.1021/pr060474i

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


  146 in total

1.  Addressing accuracy and precision issues in iTRAQ quantitation.

Authors:  Natasha A Karp; Wolfgang Huber; Pawel G Sadowski; Philip D Charles; Svenja V Hester; Kathryn S Lilley
Journal:  Mol Cell Proteomics       Date:  2010-04-10       Impact factor: 5.911

2.  Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Authors:  Douglas W Mahoney; Terry M Therneau; Carrie J Heppelmann; Leeann Higgins; Linda M Benson; Roman M Zenka; Pratik Jagtap; Gary L Nelsestuen; H Robert Bergen; Ann L Oberg
Journal:  J Proteome Res       Date:  2011-08-02       Impact factor: 4.466

Review 3.  Proteases for processing proneuropeptides into peptide neurotransmitters and hormones.

Authors:  Vivian Hook; Lydiane Funkelstein; Douglas Lu; Steven Bark; Jill Wegrzyn; Shin-Rong Hwang
Journal:  Annu Rev Pharmacol Toxicol       Date:  2008       Impact factor: 13.820

4.  MicroRNA-122 influences the development of sperm abnormalities from human induced pluripotent stem cells by regulating TNP2 expression.

Authors:  Te Liu; Yongyi Huang; Jianjun Liu; Yanhui Zhao; Lizhen Jiang; Qin Huang; Weiwei Cheng; Lihe Guo
Journal:  Stem Cells Dev       Date:  2013-03-06       Impact factor: 3.272

5.  Quantitative proteomics: measuring protein synthesis using 15N amino acid labeling in pancreatic cancer cells.

Authors:  Yingchun Zhao; Wai-Nang Paul Lee; Shu Lim; Vay Liang Go; Jing Xiao; Rui Cao; Hengwei Zhang; Robert Roy Recker; Gary Guishan Xiao
Journal:  Anal Chem       Date:  2009-01-15       Impact factor: 6.986

6.  A Tomato Vacuolar Invertase Inhibitor Mediates Sucrose Metabolism and Influences Fruit Ripening.

Authors:  Guozheng Qin; Zhu Zhu; Weihao Wang; Jianghua Cai; Yong Chen; Li Li; Shiping Tian
Journal:  Plant Physiol       Date:  2016-09-30       Impact factor: 8.340

7.  Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis.

Authors:  Paul D Piehowski; Vladislav A Petyuk; Daniel J Orton; Fang Xie; Ronald J Moore; Manuel Ramirez-Restrepo; Anzhelika Engel; Andrew P Lieberman; Roger L Albin; David G Camp; Richard D Smith; Amanda J Myers
Journal:  J Proteome Res       Date:  2013-04-10       Impact factor: 4.466

8.  Quantification of protein expression changes in the aging left ventricle of Rattus norvegicus.

Authors:  Jennifer E Grant; Amy D Bradshaw; John H Schwacke; Catalin F Baicu; Michael R Zile; Kevin L Schey
Journal:  J Proteome Res       Date:  2009-09       Impact factor: 4.466

9.  An Efficient Approach to Evaluate Reporter Ion Behavior from MALDI-MS/MS Data for Quantification Studies Using Isobaric Tags.

Authors:  Stephanie M Cologna; Christopher A Crutchfield; Brian C Searle; Paul S Blank; Cynthia L Toth; Alexa M Ely; Jaqueline A Picache; Peter S Backlund; Christopher A Wassif; Forbes D Porter; Alfred L Yergey
Journal:  J Proteome Res       Date:  2015-09-03       Impact factor: 4.466

10.  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

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