Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture. At the heart of this process is a prediction task, the spectrum identification problem, in which each fragmentation spectrum produced by a shotgun proteomics experiment must be mapped to the peptide (protein subsequence) which generated the spectrum. We propose a new algorithm for spectrum identification, based on dynamic Bayesian networks, which significantly outperforms the de-facto standard tools for this task: SEQUEST and Mascot.
@article{arxiv.1210.4904,
title = {Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra},
author = {Ajit P. Singh and John Halloran and Jeff A. Bilmes and Katrin Kirchoff and William S. Noble},
journal= {arXiv preprint arXiv:1210.4904},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)