A5038924040- Single-cell and spatial transcriptomics
- Cancer Genomics and Diagnostics
- vaccines and immunoinformatics approaches
- Genomics and Chromatin Dynamics
- RNA and protein synthesis mechanisms
- CRISPR and Genetic Engineering
- Epigenetics and DNA Methylation
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
VIB-KU Leuven Center for Brain & Disease Research
2019-2023
KU Leuven
2019-2023
Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state phenotype. However, sifting through millions of candidate variants in personal genome or cancer genome, to identify those that cis -regulatory function, remains major challenge. Interpretation noncoding benefits from explainable artificial intelligence predict interpret mutation gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone...
Abstract Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies remained absent. In this study, we benchmark the performance eight methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) reference sample develop PUMATAC, universal preprocessing pipeline, to handle various...
Abstract Single-cell RNA-seq and single-cell ATAC-seq technologies are being used extensively to create cell type atlases for a wide range of organisms, tissues, disease processes. To increase the scale these atlases, lower cost, allow more specialized multi-ome assays, custom droplet microfluidics may provide complementary solutions commercial setups. We developed HyDrop, flexible generic microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable...
Brief Abstract Prioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact a mutation on gene regulation. Here we apply specialized deep learning model phased melanoma genomes identify functional enhancer mutations with allelic imbalance chromatin accessibility expression.