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Shotgun proteomics is a high-throughput technology used to identify unknownproteins in a complex mixture. At the heart of this process is a predictiontask, the spectrum identification problem, in which each fragmentation spectrumproduced by a shotgun proteomics experiment must be mapped to the peptide(protein subsequence) which generated the spectrum. We propose a new algorithmfor spectrum identification, based on dynamic Bayesian networks, whichsignificantly outperforms the de-facto standard tools for this task: SEQUESTand Mascot.

Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra