Literature DB >> 18390580

Analysis of iTRAQ data using Mascot and Peaks quantification algorithms.

Carla M R Lacerda1, Lei Xin, Iain Rogers, Kenneth F Reardon.   

Abstract

The field of proteomics has been developing rapidly toward quantification of proteins. Despite the variety of experimental techniques available for peptide and protein labelling, there are few commercially available analytical tools with the ability to interpret data from any mass spectrometer. In this study, we compare two software packages, Mascot and Peaks, for the analysis of iTRAQ data from ESI-Q/TOF mass spectrometry. In the case of a six-protein mixture combined in a known proportion, the output of the Peaks algorithm deviated from the correct result by 14% on average, while the error of the Mascot quantification was nearly 200%. When the software were used to analyse iTRAQ data from a complex protein sample, the quantification results agreed within 20% for only 26% of the quantified proteins, showing significant differences in the two quantification algorithms. This comparison and analysis revealed major intricacies in peptide and protein quantification that must be taken into consideration for software development.

Mesh:

Substances:

Year:  2008        PMID: 18390580     DOI: 10.1093/bfgp/eln017

Source DB:  PubMed          Journal:  Brief Funct Genomic Proteomic        ISSN: 1473-9550


  5 in total

1.  Quantitative phosphoproteome profiling of iron-deficient Arabidopsis roots.

Authors:  Ping Lan; Wenfeng Li; Tuan-Nan Wen; Wolfgang Schmidt
Journal:  Plant Physiol       Date:  2012-03-21       Impact factor: 8.340

2.  Immunodepletion plasma proteomics by tripleTOF 5600 and Orbitrap elite/LTQ-Orbitrap Velos/Q exactive mass spectrometers.

Authors:  Kelly A Jones; Phillip D Kim; Bhavinkumar B Patel; Steven G Kelsen; Alan Braverman; Derrick J Swinton; Philip R Gafken; Lisa A Jones; William S Lane; John M Neveu; Hon-Chiu E Leung; Scott A Shaffer; John D Leszyk; Bruce A Stanley; Todd E Fox; Anne Stanley; Michael J Hall; Heather Hampel; Christopher D South; Albert de la Chapelle; Randall W Burt; David A Jones; Levy Kopelovich; Anthony T Yeung
Journal:  J Proteome Res       Date:  2013-09-19       Impact factor: 4.466

3.  A comparison of the accuracy of iTRAQ quantification by nLC-ESI MSMS and nLC-MALDI MSMS methods.

Authors:  Sally L Shirran; Catherine H Botting
Journal:  J Proteomics       Date:  2010-03-15       Impact factor: 4.044

4.  Comparisons of Two Proteomic Analyses of Non-Mucoid and Mucoid Pseudomonas aeruginosa Clinical Isolates from a Cystic Fibrosis Patient.

Authors:  Jayasimha Rao; F Heath Damron; Marek Basler; Antonio Digiandomenico; Nicholas E Sherman; Jay W Fox; John J Mekalanos; Joanna B Goldberg
Journal:  Front Microbiol       Date:  2011-08-01       Impact factor: 5.640

5.  PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios.

Authors:  Chris Bauer; Frank Kleinjung; Dorothea Rutishauser; Christian Panse; Alexandra Chadt; Tanja Dreja; Hadi Al-Hasani; Knut Reinert; Ralph Schlapbach; Johannes Schuchhardt
Journal:  BMC Bioinformatics       Date:  2012-02-16       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.