Header

UZH-Logo

Maintenance Infos

mProphet: automated data processing and statistical validation for large-scale SRM experiments


Reiter, L; Rinner, O; Picotti, P; Hüttenhain, R; Beck, M; Brusniak, M Y; Hengartner, M O; Aebersold, R (2011). mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nature Methods, 8(5):430-435.

Abstract

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

Abstract

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

Statistics

Citations

181 citations in Web of Science®
193 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

3 downloads since deposited on 05 Apr 2011
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2011
Deposited On:05 Apr 2011 15:06
Last Modified:21 Nov 2017 15:22
Publisher:Nature Publishing Group
ISSN:1548-7091
Publisher DOI:https://doi.org/10.1038/nmeth.1584
PubMed ID:21423193

Download