arrow
返回

A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods

delete2011-02-07
delete4
delete
OA
AI
S
Sven Degroeve
N
Niklaas Colaert
J
Joël Vandekerckhove
K
Kris Gevaert
L
Lennart Martens *
DOI:10.1002/pmic.201000605delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
The identification of peptides and proteins from fragmentation mass spectra is a very common approach in the field of proteomics. Contemporary high-throughput peptide identification pipelines can quickly produce large quantities of MS/MS data that contain valuable knowledge about the actual physicochemical processes involved in the peptide fragmentation process, which can be extracted through extensive data mining studies. As these studies attempt to exploit the intensity information contained in the MS/MS spectra, a critical step required for a meaningful comparison of this information between MS/MS spectra is peak intensity normalization. We here describe a procedure for quantifying the efficiency of different published normalization methods in terms of the quartile coefficient of dispersion (qcod) statistic. The quartile coefficient of dispersion is applied to measure the dispersion of the peak intensities between redundant MS/MS spectra, allowing the quantification of the differences in computed peak intensity reproducibility between the different normalization methods. We demonstrate that our results are independent of the data set used in the evaluation procedure, allowing us to provide generic guidance on the choice of normalization method to apply in a certain MS/MS pipeline application.
Keyword:
Bioinformatics
Intensity normalization
Peptide fragmentation
Peptide identification

期刊

Proteomics 封面图
Proteomics
IF:
3.9
论文数:
7.6K
被引数:
1.1W

机构

V
VIB
学者数:
7.8K
论文数: 5.2K
被引数: 18
引用论文

引用论文

err分享
err收藏
Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses
err2010-02-01
err173
errOAAI
errRudnick, Paul A.; Clauser, Karl R.; Kilpatrick, Lisa E.; Tchekhovskoi, Dmitrii V.; Neta, Pedatsur; Blonder, Niksa; Billheimer, Dean D.; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Ham, Amy-Joan L.; Jaffe, Jacob D.; Kinsinger, Christopher R.; Mesri, Mehdi; Neubert, Thomas A.; Schilling, Birgit; Tabb, David L.; Tegeler, Tony J.; Vega-Montoto, Lorenzo; Variyath, Asokan Mulayath; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Paulovich, Amanda G.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Tempst, Paul; Liebler, Daniel C.; Stein, Stephen E.
err分享
err收藏
Intensity-based statistical scorer for tandem mass spectrometry
err2003-01-04
err131
PREAI
errHavilio, M; Haddad, Y; Smilansky, Z
err分享
err收藏
Exploring proteomes and analyzing protein processing by mass spectrometric identification of sorted N-terminal peptides
err2003-03-31
err733
PREAI
errGevaert, K; Goethals, M; Martens, L; Van Damme, J; Staes, A; Thomas, GR; Vandekerckhove, J
err分享
err收藏
X-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectrometry
err2009-08-24
err64
PREAI
errMylonas, Roman; Mauron, Yann; Masselot, Alexandre; Binz, Pierre-Alain; Budin, Nicolas; Fathi, Marc; Viette, Veronique; Hochstrasser, Denis F.; Lisacek, Frederique
err分享
err收藏
Clustering very large multi-dimensional datasets with MapReduce
err2011-08-21
err0
errOAAI
errRobson Leonardo Ferreira Cordeiro; Caetano Traina; Agma Juci Machado Traina; Julio López; U. Kang; Christos Faloutsos
err分享
err收藏
学者 查看更多内容