Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

被引：49

作者：

Deutsch, Eric W. ^{[1
]}

Mendoza, Luis ^{[1
]}

Shteynberg, David D. ^{[1
]}

Hoopmann, Michael R. ^{[1
]}

Sun, Zhi ^{[1
]}

Eng, Jimmy K. ^{[2
]}

Moritz, Robert L. ^{[1
]}

机构：

[1] Inst Syst Biol, Seattle, WA 98109 USA

[2] Univ Washington, Prote Resource, Seattle, WA 98195 USA

来源：

JOURNAL OF PROTEOME RESEARCH | 2023年

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

mass spectrometry; proteomics; Trans-Proteomic Pipeline; computational proteomics; PeptideProphet; ProteinProphet; 6; 1; 0; Parhelion; AFFINITY PURIFICATION; LABEL-FREE; PEPTIDE IDENTIFICATION; STATISTICAL-MODEL; TANDEM; QUANTIFICATION; PROTEINS; SOFTWARE; MS/MS; TAGS;

D O I：

10.1021/acs.jproteome.2c00624

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

引用

页码：615 / 624

页数：10

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