title: PepNovo: de novo peptide sequencing via probabilistic network modeling.
abstract: We present a novel scoring method for de novo interpretation of peptides from tandem mass spectrometry data. Our scoring method uses a probabilistic network whose structure reflects the chemical and physical rules that govern the peptide fragmentation. We use a likelihood ratio hypothesis test to determine whether the peaks observed in the mass spectrum are more likely to have been produced under our fragmentation model than under a model that treats peaks as random events. We tested our de novo algorithm PepNovo on ion trap data and achieved results that are superior to popular de novo peptide sequencing algorithms. PepNovo can be accessed via the URL http://www-cse.ucsd.edu/groups/bioinformatics/software.html.
authors.affiliation: Department of Computer Science & Engineering, University of California, San Diego, La Jolla, California 92093-0114, USA. arf@cs.ucsd.edu
authors.forename: Ari
authors.initials: A
authors.lastname: Frank
authors.forename: Pavel
authors.initials: P
authors.lastname: Pevzner
mesh_terms: D000465:Algorithms; D016013:Likelihood Functions; D011336:Probability; D020539:Sequence Analysis, Protein / Q000379:methods*; D053719:Tandem Mass Spectrometry