A Technique for Measuring Transcription Factor Activity

总结 几乎NIH的每个部门都投入了大量资金来了解转录因子（TF）。TF是 细胞的管理者，控制从细胞类型到细胞对压力的反应的一切。凭借他们的强大力量， 难怪许多人类疾病（癌症、家族性血小板疾病、Waardenburg综合征等）结果 转录因子的突变。此外，超过75%的人类致病变异 基因组位于调节区，其密集有TF结合位点。目前，我们可以测量 TF结合的位置，但结合并不等同于调节活性。此外，结合分析是 一次一个TF我们迫切需要的是一种能够测量 所有TF同时在一个单元格中。我们开发了一种新的方法，称为eRNA分析， 使用增强子RNA来使用所有eRNA的位置同时推断细胞中所有TF的活性。的 这种多样性补充扩展请求的主要目标是在体内评估我们的计算限制， 预测TF活性的建模。通过这种方式，我们寻求优化我们的技术， 仿形更加准确、快速、适用范围更广。

Summary Almost every division of NIH has invested heavily in understanding transcription factors (TFs). TFs are the managers of the cell, controlling everything from cell type to cellular response to stress. With their great power, it is no wonder, many human disorders (cancer, familial platelet disorder, Waardenburg syndrome, etc.) result from mutations in transcription factors. Moreover, over 75% of disease causing variants within the human genome reside in regulatory regions, which are dense with TF binding sites. Currently we can measure the location of TF binding, but binding does not equate with regulatory activity. Furthermore, binding analysis is conducted one TF at a time. What is desperately needed is a technology that is able to measure the activity of all TFs in a cell simultaneously. We have developed a novel approach, called eRNA profiling, that leverages enhancer RNAs to infer the activity of all TFs in a cell simultaneously using the location of all eRNAs. The primary goal of this diversity supplement extension request is to evaluate in vivo the limits of our computational modeling towards predicting the activity of TFs. In this way, we seek to optimize our technology, making eRNA profiling more accurate, fast and broadly applicable.