PepNovo: De novo peptide sequencing via probabilistic network modeling
PepNovo: De novo peptide sequencing via probabilistic network modeling
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DOI：
10.1021/ac048788h
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发表时间：
2005-02-15
影响因子：
7.4
通讯作者：
Pevzner, P
中科院分区：
文献类型：
--
作者：
Frank, A;Pevzner, P
我们提出了一种新的评分方法，用于从头开始，从串联质谱学数据中解释多肽。我们的评分方法使用了一个概率网络，其结构反映了支配多肽碎片的化学和物理规则。我们使用似然比假设检验来确定质谱图中观察到的峰是否更有可能在我们的碎裂模型下产生，而不是在将峰视为随机事件的模型下产生。我们在离子陷阱数据上测试了我们的从头算法PepNovo，并取得了优于流行的从头测序算法的结果。
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.