Data reduction of isotope-resolved LC-MS spectra

Peicheng Du, Rajagopalan Sudha, Michael B. Prystowsky, Ruth Hogue Angeletti

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Motivation: Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data. Results: LCMS-2D can reliably reduce LC-MS spectra with multiple scans to a list of elution peaks, and subsequently to a list of peptide masses. It is capable of noise removal, and deconvoluting peaks that overlap in m/z, in retention time, or both, by using a novel iterative peak-picking step, a 'rescue' step, and a modified variable selection method. LCMS-2D performs well with three sets of annotated LC-MS spectra, yielding results that are better than those from PepList, msInspect and the vendor software BioAnalyst.

Original languageEnglish (US)
Pages (from-to)1394-1400
Number of pages7
JournalBioinformatics
Volume23
Issue number11
DOIs
StatePublished - Jun 1 2007

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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