Melissa Tomkins

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

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.