apLCMS--adaptive processing of high-resolution LC/MS data.
BACKGROUND
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.


RESULTS
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.


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


BACKGROUND
Supplementary data are available at Bioinformatics online.
Yu
Tianwei
T
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@sph.emory.edu
Park
Youngja
Y
Johnson
Jennifer M
JM
Jones
Dean P
DP
D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods*; D019295:Computational Biology; D030562:Databases, Protein; D013058:Mass Spectrometry / Q000379:methods*; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods; D012984:Software
D020543:Proteome