Objective Gene expression is the fundamental process that in all cells produces functional protein from a genomic DNA template using a messenger RNA (mRNA) intermediate. Eukaryotic gene expression involves transcription--the polymerization of mRNA--and splicing--the removal of non-coding regions from the mRNA. Recent evidence shows that nascent mRNAs are spliced while still being transcribed, not after completion of transcription, and that splicing machinery regulates transcription. This cross-talk complicates understanding of gene expression, as its mechanism and consequences are not understood. This project proposes using model-based data analysis, applied to multiple types of data, to study the kinetics of coupled transcription and splicing. Model-based data analysis is a statistical framework in which models are formulated as probability distributions encoding the stochastic interactions between components, including observed data. Knowledge of the underlying mechanism--here, biological--is used to quantify both the phenomenon, and the uncertainty resulting from partial knowledge and noisy observations. The need for such analysis is acute in modern biology: decades of molecular biology have yielded detailed information on specific molecules and pathways, and now next-generation sequencing (NGS) allows scientists to collect gigabytes of data on thousands of distinct molecules simultaneously. Yet, integrating these approaches is challenging: biologists struggle to analyze NGS data in ways that give insight into known--and previously unknown--biological mechanisms.Here, the model-based data analysis paradigm will be used to interrogate the interplay of transcription and splicing, using state-of the art data including time-resolved NGS measurements of RNA processing. Working with experimentalists, we will quantify the kinetics of splicing in constitutive genes by labeling nascent transcripts, and estimate the effect of splicing on polymerase elongation genome-wide. Fields of science natural sciencesbiological sciencesgeneticsDNAnatural sciencesbiological sciencesbiochemistrybiomoleculesproteinsnatural sciencesbiological sciencesgeneticsRNAnatural sciencescomputer and information sciencesdata sciencedata processingnatural sciencesbiological sciencesmolecular biology Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2014-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF) Call for proposal H2020-MSCA-IF-2014 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator THE UNIVERSITY OF EDINBURGH Net EU contribution € 195 454,80 Address Old college, south bridge EH8 9YL Edinburgh United Kingdom See on map Region Scotland Eastern Scotland Edinburgh Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
