HMM_Score is based on machine learning principles using a hidden Markov model to significantly improve the accuracy of peptide identification for tandem mass spectrometry (MS/MS) data. The Bioinformatics article is available via Open Access. (DOI 10.1093/bioinformatics/btn011). The associated software can be downloaded from our Downloads page, and updates seen at the HMM_Score page.
The Peptide Markov Model (which, it turns out, doesn’t really need the “Markov” part to work well) also uses machine learning principles to significantly improve the accuracy of protein identification using peptide mass fingerprinting (PMF). While many have come to see the PMF approach as out of date due to the advent of ever-better MS/MS based methods, the paper shows that when peptide ion peak intensities are correlated with the peptide sequence, PMF can rival the accuracy of MS/MS based identification. The paper is available via “Author Choice” (open access) here, DOI: 10.1021/pr070088g. We are continuing to explore how this may be used to improve results from shotgun searches as well.