ApLCMS--adaptive Processing of High-resolution LC/MS Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2009 May 6

PMID 19414529

Citations 227

Authors

Tianwei Yu

Youngja Park

Jennifer M Johnson

Dean P Jones

Affiliations

Soon will be listed here.

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.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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