CiC (SEA): Large Scale Prediction of Transcription Factor Binding Sites for Gene Regulation Using Cloud Computing

CiC (SEA)：使用云计算大规模预测基因调控的转录因子结合位点

• 批准号: 1048261
• 负责人: Zhengchang Su
• 金额: $ 42.5万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048261&HistoricalAwards=false
• 关键词: CiC; SEA; Large; Scale; Prediction

With the development of sequencing technologies, over 1,400 bacterial genomes have been sequenced and this number is rising exponentially. To understand these organisms, we first need to identify all the functional sequences in the genomes. Although tremendous advances have been made in computational prediction of gene-coding sequences in genomes, our ability to accurately predict another type of important functional sequences--transcription factor binding sites (TFBSs) is still very limited due to the notorious difficulty of the problem. In this project, researchers will develop accurate algorithms and tools for predicting TFBSs in genomes at a large scale using a graph-theoretic approach. The algorithms will then be parallelized and adapted to the Microsoft Azure cloud platform to predict TFBSs in thousands of sequenced bacterial genomes.Since it remains an unsolved task to efficiently and accurately predict TFBSs in all sequenced bacterial genomes, the algorithms, tools and predictions resulted from this project will greatly aid in deciphering gene regulatory networks in sequenced bacterial genomes. This will directly impact our ability to understand bacteria and utilize them for various purposes, and thus provide the foundations for new strategies for renewable energy production, environment protection, and infectious disease prevention.

随着测序技术的发展，已有超过1400个细菌基因组被测序，而且这个数字还在呈指数级增长。为了了解这些生物，我们首先需要确定基因组中的所有功能序列。尽管在基因组中基因编码序列的计算预测方面已经取得了巨大的进步，但由于问题的困难，我们准确预测另一种重要功能序列-转录因子结合位点（TFBSs）的能力仍然非常有限。在这个项目中，研究人员将开发准确的算法和工具，用于使用图论方法大规模预测基因组中的TFBSs。然后，这些算法将被并行化并适应于微软Azure云平台，以预测数千个测序细菌基因组中的TFBSs。由于高效准确地预测所有已测序细菌基因组中的TFBSs仍然是一个未解决的任务，因此该项目所获得的算法、工具和预测将极大地有助于破译已测序细菌基因组中的基因调控网络。这将直接影响我们了解细菌并将其用于各种目的的能力，从而为可再生能源生产、环境保护和传染病预防的新策略提供基础。