EFFICIENT MARGINALIZATION TO COMPUTE PROTEIN POSTERIOR PROBABILITIES FROM SHOTGU

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 为子项目列出的总成本可能 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。 从鸟枪蛋白质组学实验中识别蛋白质的问题尚未得到明确解决。识别样品中的蛋白质需要对它们进行排名，理想情况下是用可解释的分数。尤其是，？堕落？映射到多个蛋白质的肽使得这样的排序难以计算。计算蛋白质的后验概率的问题，这可以解释为蛋白质的信心？尤其令人生畏。以前的方法要么完全忽略了肽简并问题，解决它通过计算一组启发式的蛋白质或启发式后验概率，或估计后验概率与抽样方法。我们提出了一个概率模型，在串联质谱识别肽简并蛋白质鉴定。然后，我们引入图形转换算法，方便高效的蛋白质概率计算，即使是大的数据集。我们在五个不同的特征数据集上评估了我们的识别过程，并证明了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.