Richard James Morris

The paper identifies gene regulatory networks governing seed size in Brassica napus through transcriptomic analysis of developmental stages and seed organs.

The paper introduces mobileRNA, an R package that efficiently identifies mobile RNA molecules in grafted plants using merged genome alignment and simulated datasets.

The paper analyzes the gene regulatory network of flowering time in Brassica napus, finding similar network topology to Arabidopsis but with regulatory changes in SOC1 orthologues under cold conditions.

This paper trains a deep learning model to predict gene expression from regulatory sequences in plants and shows it captures positional regulatory effects and functional context.

This study examines subgenome expression bias in Brassica napus across cultivars, tissues, and developmental stages, finding that expression bias varies and depends on the metric used.

This paper introduces a Bayesian method to align transcript dynamics across different time scales and species, enabling comparative analysis of gene expression patterns.

This study identifies genetic loci that desynchronize bract and flower development in Arabidopsis, revealing complex transcriptomic changes during the floral transition.

This study uses simulations and experiments to identify residues in PARCL that drive phase separation and RNA binding, enabling targeted engineering of biomolecular condensates.

This study re-analyzes mobile mRNA datasets and finds that many annotated mobile transcripts lack support from RNA-Seq data due to technical and biological factors.

The paper compares binary and rank-based methods for identifying differentially expressed genes using Bayesian statistics on a yeast dataset, highlighting issues with traditional thresholds and advocating for rank-based approaches.

This paper introduces a Bayesian framework for differential gene expression analysis that ranks genes based on statistical evidence rather than binary classification, improving transparency and computational efficiency.