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Bioinformatics Advance Access originally published online on May 4, 2009
Bioinformatics 2009 25(15):1930-1936; doi:10.1093/bioinformatics/btp291
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

Tianwei Yu 1,*, Youngja Park 2, Jennifer M. Johnson 2 and Dean P. Jones 2

1 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta and 2 Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA

* To whom correspondence should be addressed.


  Abstract

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{at}sph.emory.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on November 4, 2008; revised on April 17, 2009; accepted on April 27, 2009

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