EFFICIENT MARGINALIZATION TO COMPUTE PROTEIN POSTERIOR PROBABILITIES FROM SHOTGU

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 从鸟枪法蛋白质组学实验中鉴定蛋白质的问题尚未得到明确解决。识别样本中的蛋白质需要对它们进行排序，最好是具有可解释的分数。特别是，“堕落”？肽映射到多种蛋白质，使得这样的排名难以计算。计算蛋白质的后验概率（可以解释为对蛋白质存在的置信度）的问题尤其令人畏惧。以前的方法要么完全忽略肽简并问题，要么通过计算一组启发式蛋白质或启发式后验概率来解决该问题，或者通过采样方法估计后验概率。我们提出了一种用于识别肽简并性的串联质谱中蛋白质鉴定的概率模型。然后，我们引入图形转换算法，即使对于大型数据集，也能促进蛋白质概率的高效计算。我们在五个不同的特征良好的数据集上评估我们的识别程序，并证明我们有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.