A model of random sequences for de novo peptide sequencing

K. Jarman, W. Cannon, Kristin H. Jarman, A. Heredia-Langner
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引用次数: 8

Abstract

We present a model for the probability of random sequences appearing in product ion spectra obtained from tandem mass spectrometry experiments using collision-induced dissociation. We demonstrate the use of these probabilities for ranking candidate peptide sequences obtained using a de novo algorithm. Sequence candidates are obtained from a spectrum graph that is greatly reduced in size from those in previous graph-theoretical de novo approaches. Evidence of multiple instances of subsequences of each candidate, due to different fragment ion type series as well as isotopic peaks, is incorporated in a hierarchical scoring scheme. This approach is shown to be useful for confirming results from database search and as a first step towards a statistically rigorous de novo algorithm.
一种用于从头肽测序的随机序列模型
我们提出了一个随机序列出现在使用碰撞诱导解离的串联质谱实验获得的产物离子谱中的概率模型。我们演示了使用这些概率对使用从头算法获得的候选肽序列排序。候选序列是从谱图中获得的,该谱图的大小比以前的图理论从头开始的方法大大减少。由于不同的片段离子类型系列和同位素峰,每个候选子序列的多个实例的证据被纳入分层评分方案。这种方法对于确认数据库搜索的结果非常有用,并且是迈向统计上严格的从头算法的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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