Dataset Information
A reference peptide database for proteome quantification based on experimental mass spectrum response curves.
Ontology highlight
ABSTRACT: Motivation
Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration.Results
We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics.Availability and implementation
SCRIPT-MAP database is available at http://www.firmiana.org/responders/.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Liu W
PROVIDER: S-EPMC6084618 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Publications
A reference peptide database for proteome quantification based on experimental mass spectrum response curves.
Bioinformatics (Oxford, England) 20180801 16
<h4>Motivation</h4>Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, l ...[more]
