Post-transcriptional Regulatory Networks

RNA结合蛋白(RBP)在RNA的剪接、编辑、核输出、翻译、周转等过程中发挥重要作用。 亚细胞定位。限制性商业惯例及其顺式调控要素(CRE)反映了它们的重要性 对人类健康的广泛影响：限制性商业惯例或CRE的突变在癌症中具有公认的作用， 发育缺陷，特别是在神经发育和神经退行性疾病方面。 使用高通量、基于体外选择的RNA结合试验、RNA竞争和 机器学习(ML)模型被训练为从RBP的蛋白质序列映射到其RNA结合偏好， 该项目将致力于将RNA序列和结构上下文结合偏好分配给所有人类 限制性商业惯例，所有脊椎动物限制性商业惯例，以及绝大多数后生动物限制性商业惯例。然后，这些特性将被用于 检测和分配人类基因组中限制性商业惯例和顺式调节元件的功能 其他模式生物的。具体细节、机器学习模型和预测的CRE将被分发 广泛通过出版物、开源软件和用户友好的网络工具，如cisBP-RNA。 该项目有可能转变癌症和人类遗传学研究，支持对 生殖系或体细胞突变对转录后调节(PTR)的功能影响。通过改进 PTR网络的重建，这一项目将加速这一新兴领域的研究走向完整 了解这一关键过程。该项目还将使研究PTR的演变成为可能 仅根据基因组和转录组数据在其他生物中重建PTR网络的工具。 RNA竞争将被用来评估511个仍未确定特征的RNA序列结合偏好 在人类和斑马鱼中的限制性商业惯例，从而建立了一个完整的结合偏好目录 在这两个物种中可能存在序列特异性限制性商业惯例。这些数据将与&gt；500的绑定数据结合使用 来自各种来源的其他限制性商业惯例，用于训练重建RNA结合的ML模型 给出RBP蛋白序列的偏好。这些模型还将利用从头开始的最新进展 从序列预测蛋白质结构。在PTR中将根据(I)位置和 在其预测的目标Cres的人类转录中的保守性，(Ii)它们的表达与 他们假定的目标转录本的PTR命运，以及(Iii)其他更强大的回归方法，如 干扰器。使用体内数据重新校准体外Motif模型将不断改进CRE预测 并改进对转录本RNA二级结构的电子预测。我们预测的Cres和 重建的PTR网络将通过与我们团队收集的体内数据和 其他。

RNA-binding proteins (RBPs) play key roles in RNA splicing, editing, nuclear export, translation, turnover, and subcellular localization. Reflecting their importance, RBPs and their cis-regulatory elements (CREs) have broad implications in human health: mutations in RBPs or CREs have well-established roles in cancer, developmental defects, particularly in neural development, and in neural degenerative diseases. Using a combination of a high-throughput, in-vitro-selection-based RNA binding assay, RNAcompete, and machine learning (ML) models trained to map from an RBP’s protein sequence to its RNA binding preferences, this project will endeavor to assign RNA sequence- and structural-context binding preferences to all human RBPs, all vertebrate RBPs, and the vast majority of metazoan RBPs. These specificities will then be used to detect and assign function to RBPs and cis-regulatory elements (CREs) in human genomes, as well as those of other model organisms. The specificities, machine learning models, and predicted CREs will be distributed widely via publication, open-source software, and user-friendly web tools like cisBP-RNA. This project has the potential to transform cancer and human genetics research supporting the estimation of the functional impact of germline or somatic mutations on post-transcriptional regulation (PTR). By improving the reconstruction of PTR networks, this project will speed research in this emerging field toward a complete understanding of this key process. This project will also permit the study of the evolution of PTR by developing tools to reconstruct PTR networks in other organisms based solely on genomic and transcriptomic data. RNAcompete will be used to assess the RNA sequence-binding preferences of the 511 still-uncharacterized RBPs in humans and D. rerio (zebrafish), thereby establishing a complete catalog of binding preferences for all likely sequence-specific RBPs in these two species. These data will be combined with binding data for >500 other RBPs from a variety of sources and used to train an ML model that reconstructs RNA-binding preferences given RBP protein sequences. These models will also leverage recent advances in de novo prediction of protein structure from sequence. RBPs will be assigned roles in PTR based on (i) the location and conservation, in human transcripts of their predicted target CREs, (ii) the correlation of their expression with the PTR fate of their putative target transcripts, and (iii) other, more powerful regression methods like the Inferelator. CRE predictions will be continuously improved using in vivo data to recalibrate in vitro motif models and to improve in silico predictions of transcript RNA secondary structure. Our predicted CREs and reconstructed PTR networks will be validated by comparisons with in vivo data collected by our team and others.