Raúl Castanera

Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants

This study evaluates deep learning for predicting rice traits using structural and single-nucleotide genomic variants, finding that deep learning models outperform Bayesian models in 75% of cases.

The replicative amplification of MITEs and their impact on rice trait variability

The paper analyzes MITE insertions in rice genomes to show their role in trait variability and proposes a replicative amplification mechanism for MITEs.

Transposons played a major role in the diversification between the closely related almond (Prunus dulcis) and peach (P. persica) genomes: Results from the almond genome sequence

The paper estimates the almond genome size and assembly, identifies transposable elements (TEs) as key drivers of divergence between almond and peach genomes, and highlights TE insertions affecting gene expression in traits like kernel taste and mesocarp formation.