Maria Colomé‐Tatché

XplainTDA identifies molecular determinants of topological structures in single-cell multi-omics data through perturbation-based analysis.

The paper identifies centromeres as hotspots for spontaneous DNA methylation epimutations in Neurospora crassa, showing these changes are mitotically stable and occur much more frequently than genetic mutations.

BATTLE-AMP benchmarks antimicrobial peptide predictors using MIC data, showing that models trained on MIC data outperform binary classifiers and highlighting limitations in distinguishing active from inactive peptides.

The paper identifies a CD87-dependent coagulation pathway that enhances Th17 potential in neonatal cDC2, with implications for early-life immunity.

SEAGALL uses geometry-aware graph attention networks to extract biologically relevant features from single-cell data, revealing cell identity drivers beyond marker genes.

The paper shows that periweaning diet activates an IFNγ-mediated circuit in cDC1 that promotes effector memory CD8 T cell homeostasis through immunogenic maturation.

This paper introduces a graph-based topic modelling method for single-cell multi-omic data, showing improved performance in clustering and biological interpretation.

This study benchmarks six scRNA-seq CNV callers using 15 datasets, finding that reference choice and allelic information impact performance, with some methods being more robust but computationally intensive.

This study uses high-resolution scRNA-seq to uncover how genomic rearrangements during antigen switching in Trypanosoma brucei depend on repair mechanisms and homologous templates.

The paper identifies RORγt-expressing dendritic cells as a distinct APC lineage conserved across tissues and species, with functions beyond promoting tolerance.

BioPathNet improves link prediction in biomedical knowledge graphs by using path-based reasoning, outperforming existing methods and identifying novel drug indications and gene interactions.

The paper introduces a tool called Community for analyzing differential cell communication in large case-control scRNAseq datasets, showing it outperforms existing methods in speed and robustness.

epiAneufinder identifies copy number variations from scATAC-seq data to explore single-cell CNV heterogeneity in cancer samples.

BBKNN, Scanorama, and scVI lead complex scRNA-seq integration in a 38-setup, nine-task, 14-metric benchmark; highly variable gene selection helps, whereas scaling favors batch removal over bio-conservation.

epiScanpy enables analysis of single-cell DNA methylation and ATAC-seq data using scanpy workflows, showing epigenetic markers enrich cell type labels.

The paper develops a single-nucleus RNA-seq2 method to study ploidy and spatial zonation in hepatocytes, finding gene expression in tetraploid cells depends on their hepatic lobe position.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

AlphaBeta estimates epimutation rates and spectra from DNA methylation data in plants, showing conserved epimutation landscapes and providing a molecular clock for tree age-dating.

The paper quantifies age-related somatic mutations and epimutations in a wild Populus trichocarpa tree over 330 years, showing lower mutation rates than annuals and distinct epimutation origins.

The paper shows that gene regulatory networks inferred from cancer data are heavily influenced by aneuploidies and sample impurities, with up to 80% of edges potentially due to these factors.

METHimpute constructs complete methylomes from low-coverage WGBS data using a Hidden Markov Model, achieving high accuracy at 6X coverage compared to 60X.