Fabrizio Mafessoni

This paper uses multi-omics data and machine learning to identify deleterious mutations in plants, showing improved performance over methods relying only on phylogenetic conservation.

The paper quantifies CRISPR/Cas9-induced DSB dynamics and repair in tomato protoplasts, revealing that precise repair accounts for 40-70% of events, limiting editing efficiency.

This study identifies CRISPR-induced genomic events like crossover, deletions, and chromothripsis-like rearrangements in plants using a LOH assay.

The paper reports a high-coverage Neandertal genome from Chagyrskaya Cave, revealing genetic features unique to Neandertals and population structure insights.

The paper detects systematic errors in large genomic datasets by analyzing batch effects, finding coding regions have more errors than non-coding regions.