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

越来越清楚的是，细胞的功能和产出依赖于对调控关系层层的微调，而我们对其中的大部分了解很少。任何破译细胞调控网络的尝试都需要彻底了解转录组和RNA相互作用组。不幸的是，由于文库准备和处理管道的限制，转录组的特征良好的部分仍然很窄。在与兰博维茨(UofTexas)和Abou Elela(UdeS)小组的合作下，我们制定了一种RNA-seq文库准备协议(TGIRT-seq)，该协议显著改进了对强结构RNA的检测，允许准确检测同一样本中的所有细胞RNA。然而，分析RNA-seq的计算工具仍然缺乏，无法提供准确的量化，并能够有洞察力地解释RNA-seq样本之间的差异。这项研究计划在多个层面上解决这些挑战，以提供转录组的系统分析： -目标1：调整参考注释，以改进对成绩单的阅读作业。文本量化的质量在很大程度上取决于注释的质量。不完整的注释和多余的元素都会导致不好的量化。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数据集中新RNA的分析结果。我们还将研究使用数据驱动注释来改进读取分配。 -目标2：改进RNA丰度和RNA-RNA相互作用的量化和可视化。目前rna-seq分析管道的局限性包括它们对嵌入基因和多映射读取的管理不善。我们将解决这些问题，并在解决方案的基础上量化RNA-RNA相互作用数据集。我们还将创建数据库，提供中等大小RNA特征的有洞察力的可视化。上述方法以及拟议的工具和方法将使对RNA调控相互作用网络的研究成为可能。 -目标3：建立工具以促进对转录数据的解释及其对蛋白质组的影响。我们最近描述了一种动态而严格的调控，即在转录本中包括特定的蛋白质靶向基序。这一目标建议扩大所研究的蛋白质基序的类型，以首先表征线粒体靶向序列的调节，然后将这些蛋白质基序与ELM数据库中定义的所有其他蛋白质基序进行比较。我们还将创建一个网络服务器，使生命科学的研究人员能够自己进行这样的分析。 所提出的工具和方法应该加强对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.