Jordi Abante

This study uses multi-ancestry admixture mapping in the UK Biobank to identify ancestry-associated disease loci, finding 13 genome-wide significant associations and four novel risk loci.

ARGformer uses transformers to learn embeddings from ancestral recombination graphs, enabling population structure analysis and ancestry inference without genotype data.

snputils provides a high-performance Python library for handling genetic variation and population structure data, improving efficiency and integration in genomic research.

This paper proposes a graph neural network approach for phenotype prediction that incorporates protein-protein interactions, outperforming existing methods in Alzheimer's disease prediction.

DIVE is a reference-free method for discovering mobile genetic elements and diversity-generating elements, showing improved detection power over existing reference-based approaches.

The paper analyzes DNA methylation entropy in human and mouse datasets, finding associations with sequence features, developmental patterns, and gene expression variability.

The paper introduces a method to model DNA methylation landscapes from nanopore sequencing data, accounting for sequencer noise and demonstrating its potential through simulations.

CpelTdm.jl is a Julia package that performs targeted differential DNA methylation analysis, detecting differences in methylation stochasticity between groups of samples.

The paper investigates promoter scanning by Pol II in yeast, showing that it occurs across all promoters and is influenced by Pol II activity and promoter architecture.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

The paper investigates factors affecting transcriptome assembly quality, finding that input RNA-Seq data quality and base-calling accuracy are more critical than coverage.