Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.
Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.
复制标题
从多维基因组数据中识别多层基因调控模块
DOI：
10.1093/bioinformatics/bts476
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发表时间：
2012-10-01
期刊：
影响因子：
--
通讯作者：
Zhou XJ
中科院分区：
文献类型：
--
作者：
Li W;Zhang S;Liu CC;Zhou XJ
动机：真核基因表达（GE）受到精确协调的多层控制，跨越表观遗传、转录和转录后调控水平。近年来，新兴的多维基因组数据集为研究跨层调控相互作用提供了前所未有的机会。在这些数据集中，同一组样品在几个基因组活性层上进行分析，例如拷贝数变异（CNV），DNA甲基化（DM），GE和microRNA表达（ME）。然而，目前对这些数据的适当分析方法很少。结果如下：在这篇文章中，我们介绍了一种稀疏的多块偏最小二乘（sMBPLS）回归方法，以确定从这种新类型的数据的多维调节模块。一个多维调控模块包含来自不同层的调控因子集，这些调控因子可能共同促成局部“基因表达工厂”。我们证明了我们的方法在模拟数据以及癌症基因组图谱卵巢癌数据集上的性能，包括在230个样本上测量的CNV，DM，ME和GE数据。我们发现，大多数识别的模块具有显着的功能和转录富集，高于仅使用单一类型的基因组数据识别的模块中观察到的。我们对这些模块的网络分析表明，CNV、DM和microRNA可以对重要癌基因和抑癌基因的表达产生耦合影响。可用性和实现：MATLAB实现的源代码可以在http://zhoulab.usc.edu/sMBPLS/上免费获得。联系：xjzhou@usc.edu补充信息：补充材料可在生物信息学在线。
Motivation: Eukaryotic gene expression (GE) is subjected to precisely coordinated multi-layer controls, across the levels of epigenetic, transcriptional and post-transcriptional regulations. Recently, the emerging multi-dimensional genomic dataset has provided unprecedented opportunities to study the cross-layer regulatory interplay. In these datasets, the same set of samples is profiled on several layers of genomic activities, e.g. copy number variation (CNV), DNA methylation (DM), GE and microRNA expression (ME). However, suitable analysis methods for such data are currently sparse. Results: In this article, we introduced a sparse Multi-Block Partial Least Squares (sMBPLS) regression method to identify multi-dimensional regulatory modules from this new type of data. A multi-dimensional regulatory module contains sets of regulatory factors from different layers that are likely to jointly contribute to a local ‘gene expression factory’. We demonstrated the performance of our method on the simulated data as well as on The Cancer Genomic Atlas Ovarian Cancer datasets including the CNV, DM, ME and GE data measured on 230 samples. We showed that majority of identified modules have significant functional and transcriptional enrichment, higher than that observed in modules identified using only a single type of genomic data. Our network analysis of the modules revealed that the CNV, DM and microRNA can have coupled impact on expression of important oncogenes and tumor suppressor genes. Availability and implementation: The source code implemented by MATLAB is freely available at: http://zhoulab.usc.edu/sMBPLS/. Contact: xjzhou@usc.edu Supplementary information: Supplementary material are available at Bioinformatics online.
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DOI：
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通讯作者：
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