Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data.

ABSTRACT: Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a comp

SUBMITTER: Yu T 

PROVIDER: S-EPMC3624888 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature