Department of Molecular and Biomedical Sciences, Jozef Stefan Institute, Ljubljana, Slovenia.
OMICS. 2010 Aug;14(4):357-67. doi: 10.1089/omi.2009.0144.
Inference of new and useful hypotheses from heterogeneous sources of genome-scale experimental data requires new computational methods that can integrate different types of data. Gene expression and genetic interaction data are two most informative data types, each allowing the identification of genes at different levels of cellular regulatory network hierarchy. We present an integrative data analysis approach, which, rather than correlating the findings from the two data sets, uses each type of data independently to identify the components of molecular pathways and combines them into a single directed network. Our computational genomics approach is based on a set of inference rules traditionally used for reasoning on genetic experiments, which we have formalized and implemented in a software tool. The approach uses chemogenetic interaction and expression data to infer the type of relation between the chemical substance (perturber) and a transcription factor by using previous knowledge on the set of genes whose expression the transcription factor in question regulates. We have used the proposed approach to successfully infer the models for the action of the drug rapamycin and of a DNA damaging agent on their molecular targets and pathways in yeast cells. The developed method is available as a web-based tool at http://www.ailab.si/perturbagen.
从基因组规模的实验数据的异构源中推断新的和有用的假设需要新的计算方法，这些方法可以整合不同类型的数据。基因表达和遗传相互作用数据是两种最具信息量的数据类型，它们都可以识别细胞调控网络层次结构的不同层次的基因。我们提出了一种综合数据分析方法，该方法不是将两种数据集的发现进行关联，而是分别使用每种类型的数据来识别分子途径的组成部分，并将它们组合成一个单一的有向网络。我们的计算基因组学方法基于一组传统上用于遗传实验推理的推理规则，我们已经对其进行了形式化，并在一个软件工具中实现。该方法使用化学生物相互作用和表达数据，通过使用关于转录因子所调控的一组基因的先前知识，推断化学物质（扰动剂）和转录因子之间的关系类型。我们已经使用所提出的方法成功地推断了药物雷帕霉素和 DNA 损伤剂在酵母细胞中的分子靶标和途径上的作用模型。开发的方法可作为一个基于网络的工具，网址为 http://www.ailab.si/perturbagen。
