A modular framework for gene set analysis integrating multilevel omics data
A modular framework for gene set analysis integrating multilevel omics data
复制标题
DOI：
10.1093/nar/gkt752
复制
发表时间：
2013-11-01
影响因子：
14.9
通讯作者：
Theis, Fabian J.
中科院分区：
文献类型：
--
作者：
Sass, Steffen;Buettner, Florian;Theis, Fabian J.
现代的高通量方法可以在多个“组学”层面上研究生物功能。水平包括mRNA和蛋白质表达谱，以及其他知识，例如，DNA甲基化和microRNA调节。之所以对多组学感兴趣，是因为当考虑到所有组学水平时，实际细胞对不同条件的反应可以得到最好的机械解释。为了将基因产品映射到它们的生物学功能，通常使用像基因本体论这样的公共本体。已经开发了许多方法来识别本体中的术语，这些术语在一组基因中被过度表示。但是，这些方法不能适当地处理多种数据类型的任何组合。在这里，我们提出了一种新的方法来分析跨多个组学水平的综合数据，以同时评估它们的生物学意义。我们开发了一种基于模型的贝叶斯方法，用于在模块化框架中推断可解释术语概率。我们的多层次本体分析(MONA)算法的表现明显好于对单个水平的传统分析，即使对于复杂的模型，包括通过microRNAs进行mRNA微调，也能产生最好的结果。MONA框架足够灵活，可以考虑不同的基本监管主题或本体论。它可供应用研究人员随时使用，并可作为独立应用程序从http://icb.helmholtz-muenchen.de/mona.获得
Modern high-throughput methods allow the investigation of biological functions across multiple 'omics' levels. Levels include mRNA and protein expression profiling as well as additional knowledge on, for example, DNA methylation and microRNA regulation. The reason for this interest in multi-omics is that actual cellular responses to different conditions are best explained mechanistically when taking all omics levels into account. To map gene products to their biological functions, public ontologies like Gene Ontology are commonly used. Many methods have been developed to identify terms in an ontology, overrepresented within a set of genes. However, these methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyse integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable term probabilities in a modular framework. Our Multi-level ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. The MONA framework is flexible enough to allow for different underlying regulatory motifs or ontologies. It is ready-to-use for applied researchers and is available as a standalone application from http://icb.helmholtz-muenchen.de/mona.