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Bayesian hierarchical model for protein identifications

机译：蛋白质标识的贝叶斯分层模型

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摘要

In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, mass spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a 'two-step' procedure of identifying the peptide first followed by the separate protein identification procedure next. In this setup, the interdependence of peptides and proteins is neglected resulting in relatively inaccurate protein identification. In this article, we propose a Markov chain Monte Carlo based Bayesian hierarchical model, a first of its kind in protein identification, which integrates the two steps and performs joint analysis of proteins and peptides using posterior probabilities. We remove the assumption of independence of proteins by using clustering group priors to the proteins based on the assumption that proteins sharing the same biological pathway are likely to be present or absent together and are correlated. The complete conditionals of the proposed joint model being tractable, we propose and implement a Gibbs sampling scheme for full posterior inference that provides the estimation and statistical uncertainties of all relevant parameters. The model has better operational characteristics compared to two existing 'one-step' procedures on a range of simulation settings as well as on two well-studied datasets.
机译：在蛋白质组学中，鉴定从生物样品中提取的复杂蛋白质混合物中的蛋白质是一个重要问题。在实验技术中，质谱（MS）是最受欢迎的。来自MS数据的蛋白质识别通常依赖于首先鉴定肽的“两步”过程，然后再次依赖于单独的蛋白质识别程序。在该设置中，肽和蛋白质的相互依存性被忽略，导致蛋白质鉴定相对不准确。在本文中，我们提出了一个基于马尔可夫链蒙特卡罗的贝叶斯等级模型，这是其蛋白质鉴定的第一类，这与两步相结合并使用后验概率进行蛋白质和肽的联合分析。通过使用聚合物组前沿基于蛋白质的假设，除去蛋白质的独立性，除去蛋白质的假设可能存在相同的生物途径并且缺席并相关。拟议的联合模型的完整条件是贸易的，我们提出并实施了GIBBS采样方案，用于全后续推理，提供所有相关参数的估计和统计不确定性。与两种仿真设置范围的两个现有的“一步”程序以及两个良好研究的数据集相比，该模型具有更好的操作特性。

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