Travis Wrightsman

This study identifies convergent genomic and gene-level constraints in grasses adapting to extreme environments using phylogenomic data and large language models.

The paper identifies a conserved cis-regulatory code across 589 grass species and finds that motif conservation declines rapidly but remains significant over evolutionary time, with adaptive changes linked to ecological transitions.

reelGene evaluates gene models using machine learning and evolutionary data, finding 28% of maize gene models are incorrectly annotated or nonfunctional.

The paper examines genome evolution in maize and related grasses, revealing variable outcomes of polyploidy across species.

The paper analyzes whole-genome polymorphism data from 27 Arabidopsis thaliana genomes to highlight challenges and insights in unbiased genetic variation characterization.

The paper benchmarks deep learning models on genomic data across 18 grass species, finding they generalize across species but not across alleles within species.

The paper trains deep learning models on ATAC-seq and DNA methylation data from 12 and 10 angiosperm species to predict chromatin accessibility and methylation across species, finding strong conservation of chromatin mechanisms.