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

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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.