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

Bioinformatics. 2009 Aug 1;25(15):1930-6. doi: 10.1093/bioinformatics/btp291. Epub 2009 May 4.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Chromatography, Liquid / methods*
  • Computational Biology
  • Databases, Protein
  • Mass Spectrometry / methods*
  • Proteome / analysis
  • Proteomics / methods
  • Software*


  • Proteome