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.

翻译：生枪蛋白质组学是一种高通量技术,用于识别复杂混合物中的未知蛋白质。 这一过程的核心是预测任务,即光谱识别问题,在这一工作中,必须绘制出由猎枪蛋白质组学实验产生的每个碎裂谱到生成光谱的peptide(蛋白质子序列子序列)上。 我们基于动态的巴耶斯网络,提出了新的频谱识别算法,该算法大大超过这项任务的脱法标准工具:SEQUEST和Mascot。