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A DYNAMIC BAYESIAN NETWORK FOR IDENTIFYING PROTEIN BINDING FOOTPRINTS FROM SINGL

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:
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-01 至 2012-06-30

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中文摘要
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英文摘要
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.
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ON USING SAMPLES OF KNOWN PROTEIN CONTENT TO ASSESS THE STATISTICAL CALIBRATION
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    8365887
  • 项目类别:
  • 资助金额:
    $2.14万
  • 财政年份:
    2011
  • 负责人:
    William Noble
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    2011
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A UNIFIED MULTITASK ARCHITECTURE FOR PREDICTING LOCAL PROTEIN PROPERTIES
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    2011
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