Factor-general characterization of dynamic transcriptional stress responses

(原文不变) 自从十年前首次对人类基因组进行测序以来，研究人员在 确定多种功能元件的基因组位置，包括控制 基因调控。然而，到目前为止，主要的重点一直是对个别监管机构进行分类 元素，而不考虑它们的动态行为或交互。在这项提案中，我们概述了 识别对基因调控至关重要的序列并表征其特征的创新方法 动态交互。我们的建议包括结合一种强大的方法来直接测量 基因的表达，称为PRO-SEQ，适应于DNase-SEQ，一种识别位置的方法 在基因调控转录因子结合的基因组中。我们建议将这些应用于 方法在刺激可诱导系统后的一段时间内获得动态的、全基因组的 有关结合和表达的信息，特别关注由 小分子雷公藤红素在永生化K562白血病细胞系中的作用。因为无论是亲顺还是 DNase-seq依赖于特定转录因子的抗体或染色质技术 免疫沉淀，我们将这种方法描述为通用因子和无芯片。我们的建议有三个 主要目的：(1)利用PRO-SEQ鉴定和鉴定转录单位；(2)鉴定和鉴定转录单位 使用DNase-seq表征许多转录因子的结合位点；以及(3)整合这些 转录和结合的动态模式揭示调控之间的相互作用网络 序列和转录单位。这些目标中的每一个都涉及开发新的统计模型 和计算方法。我们新生成的数据、我们的预测和我们的软件都将 向公众开放。

(unchanged from original) Since the human genome was first sequenced a decade ago, researchers have made great strides in identifying the genomic locations of many kinds of functional elements, including the sequences that control gene regulation. Nevertheless, the primary focus to date has been to catalog individual regulatory elements, without regard for their dynamic behavior or interactions. In this proposal, we outline an innovative approach for both identifying sequences critical for gene regulation and characterizing their dynamic interactions. Our proposal involves combining a powerful method for directly measuring the expression of genes, called PRO-seq, with an adaptation of DNase-seq, a method for identifying positions in the genome at which gene-regulating transcription factors are bound. We propose to apply these methods in a time course after stimulation of an inducible system to obtain dynamic, genome-wide information about both binding and expression, focusing in particular on stress responses induced by the small molecular celastrol in the immortalized K562 leukemia cell line. Because neither PRO-seq nor DNase-seq depends on antibodies to particular transcription factors, or on the technique of chromatin immunuprecipitation, we describe this approach as factor-general and ChIP-free. Our proposal has three main aims: (1) to identify and characterize transcription units using PRO-seq; (2) to identify and characterize the binding sites for many transcription factors using DNase-seq; and (3) to integrate these dynamic patterns of transcription and binding to reveal networks of interaction between regulatory sequences and transcription units. Each of these aims involves the development of new statistical models and computational methods. Our newly generated data, our predictions, and our software will all be made publicly available.