Torben Asp

The paper develops a semi-automated image analysis method for root phenotyping and estimates genomic breeding values for root traits in faba bean and white clover, showing genetic correlations with field yield.

This study predicts single cross performance in perennial ryegrass using genomic models, finding high predictive accuracy for dry matter yield and nutritive quality traits.

The paper validates variant effect predictions in a Brachypodium distachyon mutant population using burden and purging tests, showing ESM outperforms SIFT and PlantCAD for missense variants.

The paper develops deep learning models to predict gene expression across species and assesses their ability to capture regulatory variant effects in Brachypodium mutant lines, showing improved accuracy over existing methods.

The paper evaluates genomic prediction models for agronomic traits in perennial ryegrass under genotype x environment interactions, showing improved accuracy when accounting for these interactions.

The paper introduces an automated method for measuring seminal root angles from images and validates its accuracy against manual measurements.

This study uses multi-omics data from grass hybrids to identify gene and metabolite networks associated with agronomic traits, finding conserved hubs but limited predictive power of network features.

This study uses random regression models to analyze spatio-temporal genomic data in ryegrass, finding improved prediction accuracy for nutritive quality traits but not dry matter yield.

The paper presents a chromosome-scale genome assembly for L. japonicus Gifu, revealing differences in symbiotic gene organization compared to Medicago truncatula.

The paper assembles the Norway spruce mitogenome using machine learning and long reads, revealing heterogeneous recombination dynamics and active small repeats in plant mitogenomes.