Magnus Rattray

We are using probabilistic time-series models to gain insights into transcription dynamics in the early drosophila embryo. We consider the very earliest stage of development, where maternal transcripts are progressively replaced by zygotic gene expression. We have used a combination of whole embryo RNA-Seq, live cell imaging and fixed single molecule imaging experiments to gain insights into the mechanisms regulating RNA levels in the cell. Using a total RNA-Seq time course that captures intronic and exonic reads, we can model the production and degradation of RNA by combining a differential equation model of degradation with a Gaussian process model of transcription. We infer half-lives for a large set of zygotic genes and show how degradation rate regulates the difference in timing of peak levels of nascent and mature transcripts. Short half-life mRNAs are more likely to be associated with P-bodies and we find evidence of 5’ to 3’ degradation occurring in P-bodies for a subset of mRNAs. We then consider whether mRNA degradation is regulated spatially in the case of stripe patterns formed by the pair-rule gene eve. By combining data from live-cell imaging of transcription with fixed imaging of mature transcripts, modelling suggests that mRNA degradation is increased outside of stripe regions, leading to sharper stripes. We find increased co-localisation of P-bodies with eve mRNA in spatial regions with higher inferred degradation, suggesting spatial regulation of mRNA degradation is also associated with P-bodies. Most recently we are using genomic machine learning trained on 3’ UTRs of drosophila to try and identify features regulating degradation (work in progress) and we are assessing whether RNA LLMs work better on this task than competing approaches.