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Computational Methods for Transcriptional Mapping of Eukaryotic Genomes

真核基因组转录作图的计算方法

基本信息

批准号：
7668034
负责人：
CHARLES DELISI
金额：
$ 60.5万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-15 至 2011-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7668034
关键词：
Algorithms Aminobutyric Acids Benchmarking Binding Binding Sites Biological Communities Computers Computing Methodologies Data Data Set Databases Development Disease Evolution Family GABA-A Receptor Gene Family Genes Genetic Transcription Genome Goals Heart Diseases IBM Blue-Gene Learning Malignant Neoplasms Maps Mass Spectrum Analysis Measures Methods Neuraxis Neurotransmitters Organism Performance Play Positioning Attribute Post-Translational Protein Processing Process Protein Binding Receptor Gene Research Role Site Specific qualifier value Staging Structure System Testing Transcription Initiation Site Transcriptional Regulation Validation Yeasts computerized tools environmental change follow-up gamma-Aminobutyric Acid improved innovation mammalian genome nervous system development programs receptor response transcription factor

项目摘要

DESCRIPTION (provided by applicant): Understanding the processes that regulate the transcription of genes is central to understanding evolution, the development of multicellular organisms, and the response to pathological changes, including cancer and heart disease. This proposal aims to make substantial progress in developing and testing computational methods, and then applying them to experimental systems. We develop and deploy a battery of computational methods aimed at associating regulators with their targets, and inferring sequences that are targets for currently unidentified regulators. Testing and validation is carried out both retrospectively, against well curated databases, and prospectively, using a variety of experimental methods on a selected set of predictions. The specific aims include the following. 1. Develop and test innovative approaches for discovering new binding sites for well studied regulators, as well as sites for currently unidentified regulators. The former method requires integrating numerous and often very large datasets and then pruning the features to identify those that are biologically most relevant. Our preliminary results suggest that doing so substantially improves performance over existing methods 2. Implement all algorithms on IBM BlueGene/L This is one of the fastest machines available, though implementing algorithms on it requires a fair amount of technical sophistication. Our current implementation increases compute power over standard 2 GHz processors by approximately 20-fold. The use of Blue Gene/ L in combination with (1) will put the research community in a position to make discoveries that are substantially greater in number and more reliable than is currently possible. 3. Apply and test the methods on (i) the full S Cerevisiae genome and (ii) the mammalian GABA A receptor family. The former offers the advantages of being well studied, of providing a large set for data for testing, and of being relatively simple compared to the mammalian genome. GABA is the major inhibitory neurotransmitter in the central nervous system (CNS), and plays a key role in CNS development and disease.

描述（由申请人提供）：了解调节基因转录的过程对于理解进化、多细胞生物的发展以及对病理变化（包括癌症和心脏病）的反应至关重要。本提案旨在在开发和测试计算方法方面取得实质性进展，然后将其应用于实验系统。我们开发并部署了一系列计算方法，旨在将调节器与其目标关联起来，并推断当前未知调节器的目标序列。测试和验证是回顾性的，针对精心策划的数据库，以及前瞻性的，使用各种实验方法对一组选定的预测进行。具体目标包括以下内容。1. 开发和测试创新的方法，以发现新的结合位点，为充分研究的监管机构，以及目前尚未确定的监管机构。前一种方法需要整合大量且通常非常大的数据集，然后修剪特征以识别那些在生物学上最相关的特征。我们的初步结果表明，这样做大大提高了现有方法的性能2。在IBM BlueGene/L上实现所有算法。这是可用的最快的机器之一，尽管在它上实现算法需要相当多的复杂技术。我们目前的实现将标准2 GHz处理器的计算能力提高了大约20倍。使用蓝色基因/ L与(1)相结合，将使研究界处于一个比目前可能的发现数量多得多、更可靠的位置。3. 在(i)酿酒酵母全基因组和（ii）哺乳动物GABA A受体家族上应用和测试方法。前者的优点是可以被很好地研究，可以为测试提供大量数据，并且与哺乳动物基因组相比相对简单。GABA是中枢神经系统（CNS）中主要的抑制性神经递质，在中枢神经系统的发育和疾病中起关键作用。