Building the tools for accurate RNA-seq analysis of the coding and non-coding transcriptome

越来越清楚的是，细胞功能和输出依赖于调节关系层的微调，其中大部分我们都不太了解。任何破译细胞调控网络的尝试都需要彻底理解转录组和RNA相互作用组。不幸的是，由于文库制备和处理管道的限制，转录组的良好表征部分仍然很窄。与Lambowitz（UofTexas）和Abou Elela（UdeS）团队合作，我们制定了一个RNA-seq文库制备方案（TGIRT-seq），该方案显着提高了强结构RNA的检测，从而可以准确检测同一样品中的所有细胞RNA。然而，仍然缺乏分析RNA-seq的计算工具来提供准确的量化并能够对RNA-seq样本之间的差异进行有洞察力的解释。该研究计划在多个层面上解决了这些挑战，以提供转录组的系统分析：*-目标1：调整参考注释以改善转录本的读段分配。转录本定量的质量高度依赖于注释的质量。不完整的注释和多余的元素都可能导致量化不佳。为了解决这个问题，我们将在当前的注释中添加我们对不同细胞系和组织的TGIRT-seq数据集中的新型RNA的分析结果。我们还将研究使用数据驱动的注释来改善读取分配。*-目标2：改进RNA丰度和RNA-RNA相互作用的定量和可视化。目前RNA-seq分析管道的局限性包括其对嵌入基因和多重映射读取的管理不善。我们将解决这些问题，并建立在解决方案上，以量化RNA-RNA相互作用数据集。我们还将创建数据库，为中等大小的RNA特征提供有见地的可视化。上述方法以及提出的工具和方法将使RNA调控相互作用网络的研究成为可能。目标3：建立工具，以促进转录组数据的解释和它对蛋白质组的影响。我们最近描述了一个动态的和严格的调节，包括特定的蛋白质靶向基序的转录本。这一目标提出了扩大研究的蛋白质基序的类型，首先表征线粒体靶向序列的调控，然后将其与ELM数据库中定义的所有其他蛋白质基序进行比较。我们还将建立一个网络服务器，使生命科学研究人员能够自己进行此类分析。所提出的工具和方法应该能够增强RNA-seq的分析，并在我们对基因表达和转录组功能的理解方面取得重大进展。

It is becoming increasingly clear that cellular function and output is dependent on the fine tuning of layers of regulatory relationships, most of which we poorly understand. Any attempt to decipher cell regulatory networks will require a thorough understanding the transcriptome and the RNA interactome. Unfortunately, the well characterized portion of the transcriptome still remains narrow due to limitations in both library preparation and processing pipelines. In collaboration with the Lambowitz (UofTexas) and Abou Elela (UdeS) groups, we have elaborated an RNA-seq library preparation protocol (TGIRT-seq) which significantly improves the detection of strongly structured RNAs, allowing the accurate detection of all cellular RNAs within the same sample. However, the computational tools to analyze RNA-seq are still lacking to provide an accurate quantification and to enable an insightful interpretation of differences between RNA-seq samples. This research program addresses these challenges on multiple levels to provide systematic analysis of the transcriptome:***-Objective 1: To adjust reference annotations to improve read assignment to transcripts. The quality of transcript quantification is highly dependent on the quality of the annotation. Both incomplete annotations and superfluous elements can cause poor quantification. To address this issue, we will add to current annotations the results of our analysis of novel RNAs in TGIRT-seq datasets of diverse cell lines and tissues. We will also investigate the use of data-driven annotations to improve read assignment.***-Objective 2: to improve the quantification and visualization of RNA abundance and RNA-RNA interactions. Current limitations of RNA-seq analysis pipelines include their poor management of embedded genes and multimapped reads. We will address these problems and build upon the solution to quantify RNA-RNA interaction datasets. We will also create databases providing insightful visualization of mid-size RNA characteristics. The methodology described above and the proposed tools and approaches will enable the study of the RNA regulatory interaction network.***-Objective 3: to build tools to facilitate the interpretation of transcriptomic data and its effect on the proteome. We have recently described a dynamic and tight regulation of the inclusion of specific protein targeting motifs in transcripts. This aim proposes to widen the types of protein motifs investigated to first characterize the regulation of mitochondrial targeting sequences and then compare these to all other protein motifs defined in the ELM database. We will also create a webserver to enable researchers in the life sciences to carry out such analyses themselves.******The tools and approaches proposed should enhance the analysis of RNA-seq and result in significant advances in our understanding of gene expression and transcriptome functionality.