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PMID: 19304874 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Automated diagnosis of LC-MS/MS performance.

Bioinformatics (Oxford, England) ·Vol. 25 ·No. 10 ·2009-05-15 ·Pages 1341-3

Xu H, Freitas MA

Abstract

We report a software scheme for automated diagnosis of liquid chromatography tandem mass spectrometry (LC-MS/MS) system performance. The proposed software scheme provides a robust framework for establishing automated diagnosis of LC-MS/MS system performance for a variety of instruments and experiments. This schematic consists of four main software components: (i) data conversion, (ii) peptide identification, (iii) LC retention time analysis and (iv) system performance evaluation. The implementation of a standard approach for assessing LC-MS/MS system performance enables researchers to apply reliable metrics to assess their workflows performance over different batch experiments. Furthermore, the results from system diagnosis can provide feedback to the workflow to stop batch analysis if system performance falls below prescribed thresholds. A basic implementation of the approach based on the MassMatrix database search and LC retention time analysis programs is presented. An open source implementation of the LC-MS/MS system diagnosis software based on the MassMatrix database search program is freely available to non-commercial users and can be downloaded at www.massmatrix.net.

MeSH Terms
Chromatography, Liquid/methods Databases, Protein Electronic Data Processing Proteome/analysis,chemistry Software Tandem Mass Spectrometry/methods
Chemicals
Proteome
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Xu Hua
Proteomics and Informatics Services Facility, Research Resources Center, University of Illinois at Chicago, Chicago, IL 60612, USA. huaxu@uic.edu
Freitas Michael A
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7 references, click to expand
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Article Info
Journal
Bioinformatics (Oxford, England)
Abbr.
Bioinformatics
ISSN
1367-4811
Published
2009-05-15
Epub
2009-00-20
Pages
1341-3
Language
English
Region
England
NLM ID
9808944
PMCID
PMC2677746
Subset
IM
Grants
NCI NIH HHS · CA101956 · United States
NCI NIH HHS · CA107106 · United States
NCRR NIH HHS · RR023647 · United States
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