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.
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).
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.
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.
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