Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.

ABSTRACT:

Background

High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usual

SUBMITTER: Pfeifer N 

PROVIDER: S-EPMC2254445 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature