Deciphering Principles of Regulatory Genomics

描述（由申请人提供）：我们建议将生物信息学和比较基因组学的前沿发展与分子生物学专业知识结合起来，研究一种新的模式生物——热带爪蟾，以确定组织特异性基因调控的基因组加密，重点是骨骼肌和肝脏基因调控元件。特别是，我们的目标是阐明序列特征，基因组定位和转录调控元件的功能活动，驱动组织特异性基因表达在胚胎脊椎动物发育过程中。我们建议利用利弗莫尔国家实验室独特的计算和生物资源，开发新一代的计算方法和工具，能够识别和解释匿名非编码基因组序列中的基因调控元件。首先，我们建议结合全基因组基因表达谱；脊椎动物基因组比较和转录因子结合位点分析，为人类基因组中骨骼肌和肝脏组织特异性调节元件的从头预测开发新的统计方法。其次，我们将建立一个高通量的管道，在青蛙体内实验测试预测元件的增强活性，青蛙是唯一的非鱼类，能够进行快速和大规模实验的脊椎动物生物。我们建议通过4年的时间测试550个预测元素。通过几轮计算预测实验验证，我们的目标是改进我们的方法，并获得对人类基因调控基本结构的见解。我们还将在青蛙胚胎上使用原位杂交技术测试20个关键骨骼肌和肝脏转录因子基因以及400个预测调控元件侧面基因的预测组织特异性。最后，我们将创建一个公开可用的资源，用于与广泛的研究界共享生成的计算方法和工具以及实验数据。

DESCRIPTION (provided by applicant): We propose to combine pioneering developments in bioinformatics and comparative genomics with molecular biology expertise in a new model organism, the frog Xenopus tropicalis, to determine the genomic encryption of tissue-specific gene regulation, with emphasis on skeletal muscle and liver gene regulatory elements. In particular, we aim to elucidate sequence signatures, genomic location and the functional activity of transcriptional regulatory elements that drive tissue specific gene expression during embryonic vertebrate development. We propose to exploit unique computational and biological resources available at Livermore National Laboratory to develop a new generation of computational methods and tools capable of identifying and interpreting gene regulatory elements in anonymous noncoding genomic sequences. First, we propose to combine genome-wide gene expression profiling; vertebrate genome comparisons, and transcription factor binding site analysis to develop novel statistical methods for de novo prediction of skeletal muscle and liver tissue-specific regulatory elements in the human genome. Second, we will establish a high-throughput pipeline of in vivo experimental testing of the ehancer activity of predicted elements in frog, which is the only non-fish, sequenced vertebrate organism amnable to rapid and large-scale experimentation. We propose to test 550 predicted elements through the cource of 4 years. Through several rounds of computational prediction-experimentation validation, we aim to refine our methodology and to derive insights into the basic architecture of gene regulation in humans. We will also test the predicted tissue-specificity of 20 key skeletal muscle and liver transcription factor genes and 400 genes flanking predicted regulatory elements using in situ hybridization on frog embryos. Finally, we will create a publicly available resource for sharing the generated computational methods and tools along with experimental data with the broad research community.