Laura E. Tibbs‐Cortes

The paper introduces a new pangenome viewer for maize that integrates graph and linear genome visualization to enhance structural variation analysis and locus characterization.

The paper introduces a web tool and R package for analyzing the evolutionary origins of maize proteins, providing interactive visualizations and gene data for 26 maize inbreds.

The paper benchmarks AlphaFold, ESMFold, and Boltz in maize to assess their performance on plant proteins, finding lower confidence scores and geometry issues in less conserved proteins.

The paper develops BRIDGEcereal, a web tool using unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes, demonstrating its use in wheat candidate genes.

This study evaluates prediction models for tocochromanol content in maize grain using genomic and transcriptomic data, finding that incorporating biological knowledge improves predictive ability.

The paper combines GWAS and TWAS to identify 13 candidate causal genes for tocochromanol levels in maize grain, including five previously unassociated genes.