Ignacio L. Ibarra

UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility

UniversalEPI predicts cell type-specific and differential chromatin interactions using DNA sequence and ATAC-seq data, showing robust performance across cell types and conditions.

Cancer-myeloid cell invasive program in pediatric-type diffuse high-grade glioma

This paper presents an integrative multimodal atlas of pediatric-type diffuse high-grade gliomas, identifying cancer-myeloid cell programs and distinct cancer cell states across multiple patients.

Active repression of cell fate plasticity by PROX1 safeguards hepatocyte identity and prevents liver tumourigenesis

The paper identifies PROX1 as a safeguard repressor that maintains hepatocyte identity and prevents liver tumourigenesis by repressing alternate fate regulators.

Learning sequence-based regulatory dynamics in single-cell genomics

The paper introduces muBind, a deep learning model that predicts genomic counts from DNA sequences and cell relationships, enhancing understanding of regulatory dynamics during cell transitions.

Integrated multi-omics single cell atlas of the human retina

The paper creates a comprehensive human retina cell atlas using single-cell multi-omics data, revealing over 110 cell types and their gene regulatory networks, and enables fine-mapping of GWAS and eQTL variants.

Squidpy: a scalable framework for spatial omics analysis

Squidpy extends Scanpy/AnnData with spatial neighbor graphs and a lazy-loading image container, enabling spatial statistics, ligand-receptor tests, image features, and ~10-fold faster neighborhood enrichment than Giotto.

Comparative chromatin accessibility upon BDNF-induced neuronal activity delineates neuronal regulatory elements

The paper identifies BDNF-specific chromatin accessibility changes in neurons and links them to gene regulation and neurodevelopmental traits.

Learning interpretable cellular responses tocomplex perturbations in high-throughput screens

The compositional perturbation autoencoder separates drug, dose and cell-type effects in single-cell transcriptomes, predicts unseen combinations, and generates 5,329 unmeasured genetic combinations in silico.

APOBEC2 is a Transcriptional Repressor 1 required for proper Myoblast Differentiation

This study identifies APOBEC2 as a transcriptional repressor that represses non-myogenesis pathway genes to facilitate proper myoblast differentiation.