ABI: Innovation: Computationally uncovering dynamic transcription factor interactions within and across organisms

该项目需要开发新的计算方法来揭示转录调控网络的动态变化。 虽然生物体中几乎所有的细胞都具有相同的DNA，但它们可以表现出非常不同的特征，因为不同的基因在其中被打开或表达。 转录因子相互作用，包括调控网络，控制哪些基因表达，因此这些相互作用在细胞，条件和生物体之间的动态是正常生物功能的关键特征。 然而，到目前为止，大多数现有的知识是静态的调节网络的性质：几乎所有的生物体，转录因子的相互作用是已知的，只有少数条件下的利益。为了帮助填补这一空白，并开始揭示转录调控网络的动态性质，将开发新的计算方法来预测和比较条件特异性的转录相互作用在一个有机体和不同的转录相互作用跨有机体。 将发布用于这些任务的软件，并提供给更广泛的科学界。此外，将开展新的重大外联工作，招募不同的学生参加这项研究。更具体地说，将开发新的计算方法，以揭示和差异分析转录因子与其基因组靶点之间的调控相互作用以及调控因子之间的相互作用中的条件特异性和跨生物体变异，因为这些相互作用是实现调节特异性的中心机制。 这些方法将利用大量具有已知结合特异性的转录因子，在许多条件下现有的染色质可及性数据，这些数据揭示了基因组的哪些部分在这些条件下可以被转录因子结合，以及对多个密切相关的完全测序的生物体进行比较分析。这项研究的结果将在http://compbio.cs.princeton.edu上公布。

This project entails the development of new computational methods to uncover the dynamic variation of transcriptional regulatory networks. While nearly all cells within an organism have the same DNA, they can exhibit very different characteristics as different genes are turned on, or expressed, within them. Transcription factor interactions, comprising regulatory networks, control which genes are expressed, and thus the dynamics of these interactions across cells, conditions and organisms are a critical feature of proper biological functioning. To date, however, most existing knowledge about regulatory networks is static in nature: for nearly all organisms, transcription factor interactions are known under only a small number of conditions of interest. To help fill this gap and begin to uncover the dynamic nature of transcriptional regulatory networks, novel computational approaches will be developed to predict and compare condition-specific transcriptional interactions within an organism and varying transcriptional interactions across organisms. Software for these tasks will be released and made available to the broader scientific community. Additionally, significant new outreach efforts will be undertaken to recruit a diverse group of students to take part in this research.More specifically, new computational approaches will be developed to uncover and differentially analyze condition-specific and cross-organism variation both in regulatory interactions between transcription factors and their genomic targets, as well as in interactions amongst regulators themselves, as these interactions are a central mechanism by which regulatory specificity is achieved. The approaches will leverage large numbers of transcription factors with known binding specificities, existing chromatin accessibility data across numerous conditions that reveal which portions of a genome are accessible to be bound by transcription factors in those conditions, and comparative analysis of multiple closely related fully sequenced organisms. The results of this research will be disseminated at http://compbio.cs.princeton.edu.