Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics

被引:18
|
作者
Vaudel, Marc [1 ,2 ]
Sickmann, Albert [1 ,3 ]
Martens, Lennart [4 ,5 ]
机构
[1] Leibniz Inst Analyt Wissensch ISAS eV, Dortmund, Germany
[2] Univ Bergen, Dept Biomed, Prote Unit PROBE, N-5009 Bergen, Norway
[3] Ruhr Univ Bochum, MPC, Bochum, Germany
[4] VIB, Dept Med Prot Res, Ghent, Belgium
[5] Univ Ghent, Dept Biochem, B-9000 Ghent, Belgium
来源
关键词
Proteomics; Data interpretation; Online resource; Pathway; Protein function; Quantification; QUANTITATIVE PROTEOMICS; PROTEIN QUANTIFICATION; STATISTICAL-MODEL; PRIDE CONVERTER; DATABASE; IDENTIFICATION; TOOL; PEPTIDE; MS/MS; FRAMEWORK;
D O I
10.1016/j.bbapap.2013.06.019
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
With the advent of mass spectrometry based proteomics, the identification of thousands of proteins has become commonplace in biology nowadays. Increasingly, efforts have also been invested toward the detection and localization of posttranslational modifications. It is furthermore common practice to quantify the identified entities, a task supported by a panel of different methods. Finally, the results can also be enriched with functional knowledge gained on the proteins, detecting for instance differentially expressed gene ontology terms or biological pathways. In this study, we review the resources, methods and tools available for the researcher to achieve such a quantitative functional analysis. These include statistics for the post-processing of identification and quantification results, online resources and public repositories. With a focus on free but user-friendly software, preferably also open-source, we provide a list of tools designed to help the researcher manage the vast amount of data generated. We also indicate where such applications currently remain lacking. Moreover, we stress the eventual pitfalls of every step of such studies. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. (C) 2013 Elsevier B.V. All rights reserved.
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页码:12 / 20
页数:9
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