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.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics