期刊
BIOINFORMATICS
卷 25, 期 15, 页码 1930-1936出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btp291
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资金
- NIH [1P01ES016731-01, 2P30A1050409, 1UL1RR025008-01]
- University Research Committee of Emory University
Motivation: Liquid chromatography-mass spectrometry (LC/MS) pro. ling is a promising approach for the quanti. cation 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. ne-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quanti. cation. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/MS datasets.
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