Research focus

In the lab of computational biology we are interested in decoding the genomic regulatory code and understanding how genomic regulatory programs drive dynamic changes in cellular states, both in normal and disease processes. Transcriptional states emerge from complex gene regulatory networks. The nodes in these networks are cis-regulatory regions such as enhancers and promoters, where usually multiple transcription factors bind to regulate the expression of their target genes.

Wet lab

We apply high-throughput technologies to decipher enhancer logic and map gene regulatory networks, such as RNA-seq for transcriptomics and ATAC-seq and ChIP-seq for epigenomic profiling. To test the activities of promoters and enhancers we use massively parallel enhancer-reporter assays. Finally, to map high-resolution landscapes of possible cellular states we use single-cell transcriptomics and single-cell epigenomics. Our favorite model systems include Drosophila (the brain and the eye-antennal imaginal disc) as well as human cancer cells (short-term cultures, cell lines, primary cells, xenografts, and organ-on-chip).

Dry Lab

We use bioinformatics methods for network inference and computational modeling of enhancers, such as machine learning and advanced motif discovery. Some of the bioinformatics methods we have developed and made available to the community include TOUCAN, ENDEAVOUR, iRegulon, i-cisTarget, mu-cisTarget, and SCENIC.

Tech Lab

We develop microfluidics chips, including droplet microfluidics for single-cell assays. We also develop microfluidic devices to analyse 3D tumoroids (organ-on-chip) and single-cell migration, in combination with lens-free imaging.



SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Visit to test out SCope on several published datasets.


CisTopic is an R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.
BioRXiv Prepring – PAPER – GITHUB


PySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-CEll regulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
GITHUB – PyPi – Read The Docs


Arboreto is a computational framework that offers scalable implementations of Gene Regulatory Network inference algorithms. It currently supports GRNBoost2 and GENIE3 (Huynh-Thu et al., 2010).
PAPER – GITHUB – PyPi – Read The Docs


SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
Read more – PAPER - GITHUB


I-cisTarget is an integrative genomics method for the prediction of regulatory features and cis-regulatory modules.


IRegulon is a Cytoscape plugin that detects the TF, the targets and the motifs from a set of genes.