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首页> 外文期刊>BMC Genomics >Comprehensive genome-wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation
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Comprehensive genome-wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation

机译：全面的全基因组转录因子分析表明，人类基因调控需要结合高亲和力和低亲和力的DNA

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Background High-throughput in vivo protein-DNA interaction experiments are currently widely used in gene regulation studies. Hitherto, comprehensive data analysis remains a challenge and for that reason most computational methods only consider the top few hundred or thousand strongest protein binding sites whereas weak protein binding sites are completely ignored. Results A new biophysical model of protein-DNA interactions, BayesPI2+, was developed to address the above-mentioned challenges. BayesPI2+ can be run in either a serial computation model or a parallel ensemble learning framework. BayesPI2+ allowed us to analyze all binding sites of the transcription factors, including weak binding that cannot be analyzed by other models. It is evaluated in both synthetic and real in vivo protein-DNA binding experiments. Analysing ESR1 and SPIB in breast carcinoma and activated B cell-like diffuse large B-cell lymphoma cell lines, respectively, revealed that the concerted binding to high and low affinity sites correlates best with gene expression. Conclusions BayesPI2+ allows us to analyze transcription factor binding on a larger scale than hitherto achieved. By this analysis, we were able to demonstrate that genes are regulated by concerted binding to high and low affinity binding sites. The program and output results are publicly available at: http://folk.uio.no/junbaiw/BayesPI2Plus .
机译：背景技术高通量体内蛋白质-DNA相互作用实验目前广泛用于基因调控研究。迄今为止，全面的数据分析仍然是一个挑战，因此多数计算方法只考虑前几百或几千个最强的蛋白质结合位点，而弱的蛋白质结合位点则被完全忽略。结果开发了一种新的蛋白质-DNA相互作用的生物物理模型BayesPI2 +，以解决上述挑战。 BayesPI2 +可以在串行计算模型或并行集成学习框架中运行。 BayesPI2 +允许我们分析转录因子的所有结合位点，包括其他模型无法分析的弱结合。在合成和实际体内蛋白质-DNA结合实验中均对其进行了评估。分别分析乳腺癌和活化的B细胞样弥漫性大B细胞淋巴瘤细胞系中的ESR1和SPIB，发现与高和低亲和力位点的协同结合与基因表达最相关。结论BayesPI2 +使我们能够以比迄今更大的规模分析转录因子结合。通过该分析，我们能够证明基因是通过与高亲和力和低亲和力结合位点的协同结合来调控的。该程序和输出结果可从以下网址公开获得：http://folk.uio.no/junbaiw/BayesPI2Plus。

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