Abstract

High-throughput mass spectrometers can produce thousands of peptide spectra from a single complex protein sample in a short amount of time. These data sets contain a substantial amount of redundancy (i.e. the same peptide is selected and identified multiple times in a single experiment) from peptides that may get selected multiple times in the liquid chromatography mass spectrometry (LC-MS/MS) experiment. The data from these mass spectrometers contain a substantial number of spectra that have low signal to noise (S/N) ratio and may not get interpreted due to poor quality. Recently, we presented a graph theoretic algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data. CAMS utilized a novel metric, called a F-set, that allows accurate identification of the spectra that are similar with much higher accuracy and sensitivity than if single peak comparisons were performed …