A DYNAMIC BAYESIAN NETWORK FOR IDENTIFYING PROTEIN BINDING FOOTPRINTS FROM SINGL

一种用于识别单个蛋白质结合足迹的动态贝叶斯网络

• 批准号: 8365880
• 负责人: William Noble
• 金额: $ 2.14万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8365880
• 关键词: Algorithms; Binding; Binding Sites; Biology; Chromatin; Code; Coupled; Data; Data Set; Digestion; Funding; Fungal Genome; Gene Expression Regulation; Genome; Genomics; Grant; Intercistronic Region; Location; Maps; Methods; Motivation; National Center for Research Resources; Network-based; Principal Investigator; Protein Binding; Research; Research Infrastructure; Resolution; Resources; Saccharomyces cerevisiae; Source; Transcript; Transcription Initiation Site; United States National Institutes of Health; Yeasts; computer based statistical methods; cost; digital; genome-wide; transcription factor

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Motivation: A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem. Results: We present a dynamic Bayesian network-based approach for the identification and assignment of statistical confidence estimates to protein-binding footprints from digital genomic footprinting data. The method, DBFP, allows footprints to be identified in a probabilistic framework and outperforms our previously described algorithm in terms of precision at a fixed recall. Applied to a digital footprinting data set from Saccharomyces cerevisiae, DBFP identifies 4679 statistically significant footprints within intergenic regions. These footprints are mainly located near transcription start sites and are strongly enriched for known TFBSs. Footprints containing no known motif are preferentially located proximal to other footprints, consistent with cooperative binding of these footprints. DBFP also identifies a set of statistically significant footprints in the yeast coding regions. Many of these footprints coincide with the boundaries of antisense transcripts, and the most significant footprints are enriched for binding sites of the chromatin-associated factors Abf1 and Rap1.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 动机：转录因子结合位点（TFBS）的全局图对于理解基因调控和基因组功能至关重要。DNA酶I消化染色质结合大规模并行测序（数字基因组足迹），使蛋白质结合足迹的鉴定具有高分辨率的基因组范围内。然而，准确推断这些足迹的位置仍然是一个具有挑战性的计算问题。 结果如下：我们提出了一种基于动态贝叶斯网络的方法，用于从数字基因组足迹数据中识别和分配蛋白质结合足迹的统计置信度估计。该方法，DBFP，允许足迹被确定在一个概率的框架，并优于我们以前描述的算法在一个固定的召回精度。应用到数字足迹数据集从酿酒酵母，DBFP确定4679个基因间区域内的统计学显著的足迹。这些足迹主要位于转录起始位点附近，并且强烈富集已知的TFBS。不含已知基序的足迹优先位于邻近其他足迹，与这些足迹的合作结合一致。DBFP还在酵母编码区中识别出一组统计学上显著的足迹。这些足迹中的许多与反义转录物的边界相吻合，并且最显著的足迹富集了染色质相关因子Abf 1和Rap 1的结合位点。