利用De-novo Sequence Motif Discovery、Network Inference和Interactome数据理解转录调控
Understanding Transcriptional Regulation Using De-novo Sequence Motif Discovery, Network Inference and Interactome Data
摘要：
基因调控是一个复杂的过程，涉及几个基因组元件的作用，这些元件协同工作以驱动时空表达。基因调控元件的实验表征是一个非常复杂和资源密集型的过程。计算生物学的主要目标之一是使用来自已知的、先前注释的调控元件的子集的结果来对先前未表征的元件进行\textit{in-silico}注释。ENCODE项目（\emph{http://encode.nih.gov}）的最新结果提出了对1%人类基因组的这种功能（调节）非编码元件的深入分析。希望在这个子集上获得的结果可以扩展到基因组的其余部分。这是一项极其重要的工作，它将能够更快地解剖关键生物学过程中的其他功能元件，如疾病进展和器官发育（\cite{Kleinjan2005}，\cite{Lieb2006}。这些迄今未表征的区域的计算注释将需要识别具有良好预测价值的特征。在这项工作中，我们研究转录调控作为一个问题，在异构数据集成，跨序列，表达和相互作用组水平的属性。以\textit{Gata2}基因及其最近发现的泌尿生殖系统增强子\cite{Khandekar2004}为例，我们研究了各种高通量功能基因组检测（来自ENCODE和SymAtlas等项目）在表征这些增强子及其调控作用方面的预测价值。观察这些数据模式的现代统计学习方法的应用结果，我们提出了一组最具歧视性的特征来找到这些增强子。
英文摘要：
Gene regulation is a complex process involving the role of several genomic elements which work in concert to drive spatio-temporal expression. The experimental characterization of gene regulatory elements is a very complex and resource-intensive process. One of the major goals in computational biology is the \textit{in-silico} annotation of previously uncharacterized elements using results from the subset of known, previously annotated, regulatory elements. The recent results of the ENCODE project (\emph{http://encode.nih.gov}) presented in-depth analysis of such functional (regulatory) non-coding elements for 1% of the human genome. It is hoped that the results obtained on this subset can be scaled to the rest of the genome. This is an extremely important effort which will enable faster dissection of other functional elements in key biological processes such as disease progression and organ development (\cite{Kleinjan2005},\cite{Lieb2006}. The computational annotation of these hitherto uncharacterized regions would require an identification of features that have good predictive value. In this work, we study transcriptional regulation as a problem in heterogeneous data integration, across sequence, expression and interactome level attributes. Using the example of the \textit{Gata2} gene and its recently discovered urogenital enhancers \cite{Khandekar2004} as a case study, we examine the predictive value of various high throughput functional genomic assays (from projects like ENCODE and SymAtlas) in characterizing these enhancers and their regulatory role. Observing results from the application of modern statistical learning methodologies for each of these data modalities, we propose a set of features that are most discriminatory to find these enhancers.
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