Edward S. Buckler

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

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

This study shows that maize evolved a higher recombination rate and re-patterning of crossover events during domestication, which enhanced genetic diversity and adaptation.

PlantCAD2 is a DNA foundation model pre-trained on 65 angiosperm genomes, showing improved performance in evolutionary conservation and chromatin accessible region prediction compared to existing models.

This study identifies 9 QTLs associated with freezing tolerance in Tripsacum dactyloides using bulk segregant analysis and regression-based allele frequency imputation.

The paper constructs a transcriptomic atlas of grass senescence to identify molecular mechanisms of nitrogen recycling, finding divergent underground sink networks in annual crops.

This study examines competitive dynamics in maize varieties and finds limited competition effects, no yield penalty in mixtures, and increased yield stability.

The paper analyzes higher-order repeat (HOR) structures in maize centromeres and knobs, finding they are widespread but not functionally specific to centromeres, while knobs show structured HOR patterns linked to meiotic drive.

DeltaBreed is a breeding data management system that integrates with BrAPI-compliant applications and provides interoperability test cases and future development plans.

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.

This study investigates the origins and adaptive consequences of polyploidy in the prairie grass Andropogon gerardi, finding that whole genome duplication is common and provides fitness advantages in xeric climates.

GeneCAD improves gene annotation in plant genomes using a DNA foundation model, achieving higher accuracy than existing tools across diverse species.

The paper generates high-quality genome assemblies for three Bouteloua species and identifies polyploidy and transposable elements, providing genomic resources for comparative studies.

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

The paper examines nitrogen dynamics in Andropogoneae grasses, finding annual species conserve nitrogen while perennials vary in their impact on nitrification and nitrate loss.

This study evaluates the use of environmental and genomic data to identify climate-adaptive maize varieties, finding that genomic data better predict yield traits than environmental data alone.

The paper compares cold-responsive proteomes across five PACMAD grasses to assess conservation of protein-level responses and identifies LEA3 as a key cold-tolerant protein.

The paper identifies selection signatures and predicts deleterious mutations in common bean using phylogenetic analysis and machine learning, showing correlations with agronomic traits.

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

The paper examines allelic variation in diel transcription patterns across maize inbreds and identifies cis-regulatory sequence variation influencing gene expression.

The paper identifies genes under relaxed selection in cassava due to clonal propagation, highlighting pollen-related genes and their implications for breeding.

The paper introduces PlantCaduceus, a DNA language model for plants, showing high transferability across species and improved performance in mutation prediction and variant identification.

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 improves genomic prediction of agronomic traits in maize by integrating spatio-temporal modeling of multi-spectral aerial images with genetic data.

This study evaluates regulatory network-based machine learning for gene expression prediction in maize using Arabidopsis thaliana networks, finding that simple models outperform complex ones within species but complex models are better for cross-species predictions.

The paper constructs a melon pan-genome and integrates it with a multi-parental framework to enable high-resolution trait dissection in melon.

The paper predicts evolutionary constraint in maize using genomic annotations to prioritize causal variants for fitness-related traits, improving genomic prediction accuracy.

rTASSEL provides an R interface to TASSEL for association mapping, offering faster performance than other R packages for genomic analyses.

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.

The paper introduces the Practical Haplotype Graph (PHG) platform for storing and using pangenomes, demonstrating its effectiveness in imputing genomes from low-coverage data in crops like maize.