Journal Article

apLCMS—adaptive processing of high-resolution LC/MS data

Tianwei Yu, Youngja Park, Jennifer M. Johnson and Dean P. Jones

in Bioinformatics

Volume 25, issue 15, pages 1930-1936
Published in print August 2009 | ISSN: 1367-4803
Published online May 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp291
apLCMS—adaptive processing of high-resolution LC/MS data

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Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency.

Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets.

Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS.

Contact: tyu8@sph.emory.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5242 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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