Pedro Madrigal

The paper describes methods for automated viral ncRNA annotation and programmatic access to Rfam data via its API, enabling integration into bioinformatics workflows.

RNAcentral integrates literature and gene-level data to enhance non-coding RNA sequence analysis and functional understanding.

This study shows that DNMT3A R882H mutant HSCs resist inflammatory stress through delayed p53 activation, leading to clonal expansion and increased AML risk.

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

The paper uses a deep learning model to identify antimicrobial resistance genes in ISS microbiome data, revealing resistance patterns in specific bacterial species.

The paper uses single-cell RNA-seq of differentiating iPS cells from 125 donors to identify expression quantitative trait loci influencing gene expression during endoderm differentiation.

The paper develops a method to handle sparse data in single-cell epigenomics using functional data analysis, improving coverage estimation for chromatin accessibility.