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

• Published in: Bioinformatics Vol. 25; no. 15; pp. 1930 - 1936
• Main Authors: Yu, Tianwei, Park, Youngja, Johnson, Jennifer M., Jones, Dean P.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.08.2009; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bioinformatics; Biological and medical sciences; Biological samples; Chromatography, Liquid - methods; Computational Biology; Databases, Protein; Fundamental and applied biological sciences. Psychology; General aspects; Liquid chromatography; Mass spectrometry; Mass Spectrometry - methods; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Metabolites; Noise reduction; Original Papers; Proteome - analysis; Proteomics - methods; Software; High resolution; Data
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp291

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.