Valerie Christiaens

CREsted is a software package for modeling and designing cell type-specific enhancers using deep learning, validated across tissues and species.

The paper introduces HyDrop v2, a microfluidic system for generating large scATAC-seq atlases, and shows its data is comparable to commercial platforms for training sequence-to-function models.

The paper extracts cis-regulatory rules from sequence-to-function models in human neural development using new single-cell multiome data and a computational tool called TF-MINDI.

The paper compares enhancer codes of cell types between mammalian neocortex and avian pallium using deep learning, finding higher divergence in excitatory neurons than other cell types.

This study uses deep learning to design synthetic enhancers that target specific cell types in fruit flies and humans, revealing the enhancer code as optimal TF activator motifs and absence of repressor motifs.

SCENIC+ infers enhancer-driven gene regulatory networks from single-cell multi-omics data, improving TF identification and enabling analysis of regulatory dynamics and perturbations.

The paper maps enhancer gene regulatory networks (GRNs) in liver cell types and identifies TFs governing hepatocyte zonation states using multiomics and deep learning.

HyDrop is a droplet microfluidic platform for scATAC-seq and scRNA-seq that improves throughput and sensitivity while reducing cost.

The paper identifies regulatory regions and gene regulatory networks in Drosophila brain cell types using chromatin accessibility and transcriptome data.

The paper identifies and characterizes enhancer activity in melanoma cell states using MPRA and DeepMEL2, showing AP-1 drives MES enhancers and SOX/MITF influence MEL enhancers.

The paper uses a deep learning model on melanoma genomes to prioritize enhancer mutations by analyzing allelic imbalance in chromatin accessibility and gene expression.

The paper uses deep learning to analyze enhancer logic across species in melanoma, identifying conserved regulatory complexes and their roles.

The paper spatially integrates single-cell transcriptomics and epigenomics data to identify enhancers and gene regulatory relationships in Drosophila eye-antennal disc.

The paper identifies an intermediate cell state in melanoma through single-cell RNA-seq and ATAC-seq, revealing a gene regulatory network that governs phenotype switching and potential therapeutic targets.

The paper introduces cisTopic, a probabilistic framework for discovering cis-regulatory topics and cell states from single-cell epigenomics data, and validates its findings using RNA-seq co-expression networks and perturbation experiments.

This study creates a single-cell transcriptome catalogue of the Drosophila brain across its lifespan, revealing regulatory erosion and identifying novel neuronal states linked to ageing.

The paper introduces a computational pipeline, cisTarget, to identify functional non-coding mutations in cancer genomes by analyzing their impact on personalized gene regulatory networks.