Gonzalo Benegas

The paper develops a high-throughput MPRA to map cis-regulatory elements in cereal promoters, identifying mutations that significantly increase gene expression.

This paper introduces GPN-Star, a phylogeny-informed genomic language model that improves variant effect prediction across species and evolutionary timescales.

This work benchmarks DNA sequence models for predicting causal regulatory variants in human genetics using a curated dataset of 113 Mendelian and 83 complex traits.

GPN-MSA is a DNA language model that predicts variant effects across the human genome using whole-genome alignments, achieving strong performance on deleteriousness prediction for coding and non-coding variants.

This paper introduces GPN, a DNA language model that predicts genome-wide variant effects and outperforms conservation-based predictors in Arabidopsis thaliana.

Choros corrects sequence-based biases in ribosome profiling data to provide more accurate quantification of translation dynamics.

The paper analyzes alternative splicing across diverse mouse cell types using two large-scale single-cell datasets, identifying novel isoforms and splicing regulation patterns.