Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components. We present a new comprehensive R/Bioconductor-package, IntOMICS, which implements a Bayesian framework for multi-omics data integration. IntOMICS adopts a Markov Chain Monte Carlo sampling scheme to systematically analyze gene expression, copy number variation, DNA methylation, and biological prior knowledge to infer regulatory networks. The unique feature of IntOMICS is an empirical biological knowledge estimation from the available experimental data, which complements the missing biological prior knowledge. IntOMICS has the potential to be a powerful resource for exploratory systems biology.

中文翻译：

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IntOMICS：使用多组学数据重建监管网络的贝叶斯框架。

多组学数据的整合可以提供由不同相互关联的分子成分组成的生物系统的更复杂视图。我们提出了一个新的综合 R/Bioconductor 包 IntOMICS，它实现了多组学数据集成的贝叶斯框架。IntOMICS 采用 Markov Chain Monte Carlo 抽样方案系统地分析基因表达、拷贝数变异、DNA 甲基化和生物学先验知识，以推断调控网络。IntOMICS 的独特之处在于根据可用的实验数据进行经验生物学知识估计，补充了缺失的生物学先验知识。IntOMICS 有潜力成为探索性系统生物学的强大资源。