EFFICIENT MARGINALIZATION TO COMPUTE PROTEIN POSTERIOR PROBABILITIES FROM SHOTGU

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 从鸟枪式蛋白质组学实验中识别蛋白质的问题还没有得到最终解决。识别样本中的蛋白质需要对它们进行排序，理想的情况是使用可解释的分数。尤其是？堕落？多肽映射到多个蛋白质，使得这样的排名很难计算。计算蛋白质的后验概率，这可以解释为对蛋白质存在的置信度？S，这一问题尤其令人望而生畏。以往的方法要么完全忽略了肽的简并问题，要么通过计算一组启发式的蛋白质或启发式的后验概率来解决这个问题，要么用抽样的方法估计后验概率。我们提出了一个在串联质谱学中识别蛋白质的概率模型，该模型识别多肽的简并。然后，我们介绍了图形转换算法，这些算法有助于高效计算蛋白质概率，即使是对于大型数据集。我们在五个不同的特征良好的数据集上评估了我们的识别过程，并展示了我们高效计算高质量蛋白质后验的能力。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, ?degenerate? peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein?s presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or estimated the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors.