Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data.
Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data.
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DOI：
10.1128/msystems.01105-20
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发表时间：
2021-03-30
期刊：
影响因子：
6.4
通讯作者：
Narasimhan G
中科院分区：
文献类型：
--
作者：
Ruiz-Perez D;Lugo-Martinez J;Bourguignon N;Mathee K;Lerner B;Bar-Joseph Z;Narasimhan G
纵向微生物组数据分析的一个关键挑战是推断微生物分类群、它们的基因、它们消耗和产生的代谢物以及宿主基因之间的时间相互作用。为了解决这些挑战，我们开发了一个计算管道，一个用于纵向多组学数据（PALM）分析的管道，它首先对齐多组学数据，然后使用动态贝叶斯网络（DBN）重建统一模型。我们的方法克服了采样率和进展率的差异，利用生物启发的多组学框架，减少了DBN中的大量实体和参数，并验证了学习的网络。应用PALM从炎症性肠病患者收集的数据，我们表明，它准确地识别已知的和新的相互作用。有针对性的实验验证进一步支持了一些预测的新的代谢物分类群的相互作用。虽然许多大型联盟收集和分析了几种不同类型的微生物组和基因组时间序列数据，但很少有方法用于多组学数据集的联合建模。我们开发了一种新的计算管道PALM，它使用动态贝叶斯网络（DBN），旨在整合来自纵向微生物组研究的多组学数据。当用于整合来自微生物组样品的序列、表达和代谢组学数据沿着宿主表达数据时，所得到的模型识别分类群、其基因和它们产生和消耗的代谢物之间的相互作用，以及它们对宿主表达的影响。我们通过使用它们来预测微生物组水平的未来变化以及将学习到的相互作用与文献中已知的相互作用进行比较来测试这些模型。最后，我们对一些预测的相互作用进行了实验验证，以证明该方法识别新关系及其影响的能力。
A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.