Literature DB >> 22489649

A complex standard for protein identification, designed by evolution.

Marc Vaudel1, Julia M Burkhart, Daniela Breiter, René P Zahedi, Albert Sickmann, Lennart Martens.   

Abstract

Shotgun proteomic investigations rely on the algorithmic assignment of mass spectra to peptides. The quality of these matches is therefore a cornerstone in the analysis and has been the subject of numerous recent developments. In order to establish the benefits of novel algorithms, they are applied to reference samples of known content. However, these were recently shown to be either too simple to resemble typical real-life samples or as leading to results of lower accuracy as the method itself. Here, we describe how to use the proteome of Pyrococcus furiosus , a hyperthermophile, as a standard to evaluate proteomics identification workflows. Indeed, we prove that the Pyrococcus furiosus proteome provides a valid method for detecting random hits, comparable to the decoy databases currently in popular use, but we also prove that the Pyrococcus furiosus proteome goes squarely beyond the decoy approach by also providing many hundreds of highly reliable true positive hits. Searching the Pyrococcus furiosus proteome can thus be used as a unique test that provides the ability to reliably detect both false positives as well as proteome-scale true positives, allowing the rigorous testing of identification algorithms at the peptide and protein level.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 22489649     DOI: 10.1021/pr300055q

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


  10 in total

1.  PeptideShaker enables reanalysis of MS-derived proteomics data sets.

Authors:  Marc Vaudel; Julia M Burkhart; René P Zahedi; Eystein Oveland; Frode S Berven; Albert Sickmann; Lennart Martens; Harald Barsnes
Journal:  Nat Biotechnol       Date:  2015-01       Impact factor: 54.908

Review 2.  Protein analysis by shotgun/bottom-up proteomics.

Authors:  Yaoyang Zhang; Bryan R Fonslow; Bing Shan; Moon-Chang Baek; John R Yates
Journal:  Chem Rev       Date:  2013-02-26       Impact factor: 60.622

3.  PepExplorer: a similarity-driven tool for analyzing de novo sequencing results.

Authors:  Felipe V Leprevost; Richard H Valente; Diogo B Lima; Jonas Perales; Rafael Melani; John R Yates; Valmir C Barbosa; Magno Junqueira; Paulo C Carvalho
Journal:  Mol Cell Proteomics       Date:  2014-05-30       Impact factor: 5.911

4.  A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry.

Authors:  Qiang Kou; Zhe Wang; Rachele A Lubeckyj; Si Wu; Liangliang Sun; Xiaowen Liu
Journal:  J Proteome Res       Date:  2019-01-28       Impact factor: 4.466

5.  Interlaboratory studies and initiatives developing standards for proteomics.

Authors:  Alexander R Ivanov; Christopher M Colangelo; Craig P Dufresne; David B Friedman; Kathryn S Lilley; Karl Mechtler; Brett S Phinney; Kristie L Rose; Paul A Rudnick; Brian C Searle; Scott A Shaffer; Susan T Weintraub
Journal:  Proteomics       Date:  2013-02-19       Impact factor: 3.984

6.  Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process.

Authors:  Xiao-Dong Feng; Li-Wei Li; Jian-Hong Zhang; Yun-Ping Zhu; Cheng Chang; Kun-Xian Shu; Jie Ma
Journal:  BMC Genomics       Date:  2017-03-14       Impact factor: 3.969

7.  Accurate and Automated High-Coverage Identification of Chemically Cross-Linked Peptides with MaxLynx.

Authors:  Şule Yılmaz; Florian Busch; Nagarjuna Nagaraj; Jürgen Cox
Journal:  Anal Chem       Date:  2022-01-11       Impact factor: 6.986

Review 8.  Current strategies and findings in clinically relevant post-translational modification-specific proteomics.

Authors:  Oliver Pagel; Stefan Loroch; Albert Sickmann; René P Zahedi
Journal:  Expert Rev Proteomics       Date:  2015-05-08       Impact factor: 3.940

9.  An extra dimension in protein tagging by quantifying universal proteotypic peptides using targeted proteomics.

Authors:  Giel Vandemoortele; An Staes; Giulia Gonnelli; Noortje Samyn; Delphine De Sutter; Elien Vandermarliere; Evy Timmerman; Kris Gevaert; Lennart Martens; Sven Eyckerman
Journal:  Sci Rep       Date:  2016-06-06       Impact factor: 4.379

10.  A cost-sensitive online learning method for peptide identification.

Authors:  Xijun Liang; Zhonghang Xia; Ling Jian; Yongxiang Wang; Xinnan Niu; Andrew J Link
Journal:  BMC Genomics       Date:  2020-04-25       Impact factor: 3.969

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.