We are interested in the development of computational algorithms and mathematical models for the study of whole-genome epigenomics data, with a particular interest in single-cell epigenomic data and its integration with single-cell transcriptomic data.

Our group uses whole-genome next generation sequencing data, mainly DNA methylation data (measured using whole-genome bisulfite sequencing), histone modification data (measured using chromatin immunoprecipitation followed by sequencing) or chromatin openness data (measured using ATAC-seq) to study different epigenetic processes, such as the inheritance of epigenetic marks upon cell divisions, epigenetic dynamics during differentiation processes like embryo development, and stochastic gains and losses of epigenetic marks during meiotic and mitotic cell divisions.

Since a few years ago, epigenetic single-cell measurements, where the epigenetic status of single-cells is evaluated using next generation sequencing techniques, are becoming mainstream. To leverage the information contained in these data, our group is working on developing single-cell epigenetic algorithms. These algorithms study for example how distinct cell populations can be distinguished using their epigenomic information, or study the link between the epigenetic conformation and the expression of genes in single cells.

Single-cell, DNA methylation, histone modifications, ATAC-seq, computational biology, mathematical modelling