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：
7319126
负责人：
CHARLES DELISI
金额：
$ 62.42万
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 2010-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7319126
关键词：
Algorithms Aminobutyric Acid Aminobutyric Acids Benchmarking Binding Binding Sites Biological Class Communities Computers Computing Methodologies Condition Data Data Set Databases Development Disease Evolution Family GABA-A Receptor Gene Family Genes Genetic Transcription Genome Goals Heart Diseases Learning Malignant Neoplasms Maps Mass Spectrum Analysis Measures Methods Neuraxis Neurotransmitters Numbers Organism Performance Personal Satisfaction Play Positioning Attribute Post-Translational Protein Processing Process Protein Binding Purpose Range Receptor Gene Research Role Score Site Specific qualifier value Staging Standards of Weights and Measures 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的反应，包括癌症和心脏病，至关重要。该建议旨在在开发和测试计算方法上取得重大进展，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，旨在将调节器与目标相关联，并推断为当前身份不明的监管机构的目标。回顾性的测试和验证都是针对精心策划的数据库进行的，并使用各种实验方法对选定的一组预测进行。具体目的包括以下内容。 1。开发和测试创新方法，以发现针对经过良好研究的调节器以及目前身份不明的监管机构的现场。前一种方法需要整合众多且通常非常大的数据集，然后修剪这些功能，以识别生物学上最相关的功能。我们的初步结果表明，在现有方法2上这样做可以大大提高性能2。在IBM Bluegene/L上实现所有算法，这是可用的最快机器之一，尽管在其上实现算法需要相当多的技术成熟。我们目前的实施将对标准2 GHz处理器的计算功率提高了约20倍。蓝色基因/ L与（1）结合使用将使研究社区的发现数量要大得多，并且比目前可能更可靠。 3。应用并测试（i）完整的酿酒酵母基因组和（ii）哺乳动物GABA A受体家族的方法。前者提供了经过深入研究的优势，提供了用于测试数据的大型数据，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，并且在CNS发育和疾病中起关键作用。