The Computational Epigenomics group lead by Maria Colomé-Tatché is interested in understanding how different epigenomes emerge, how stable epigenetic changes are, and how do they lead to different phenotypes. To address these questions, the group works on the development of new computational methods. In the recent years, epigenetic single cell measurements, where the epigenetic status of single cells is evaluated using next generation sequencing techniques, have become mainstream. The group focuses on generating new methods that leverage the full potential of this single-cell data.
Main Research lines:
1. Single-cell epigenomics:
The group works on the development of computational models for the analysis of single cell DNA methylation data (i.e. single cell bisulfite sequencing) and single cell open chromatin data (i.e. scATAC-seq), and integration with single cell transcriptomic data. The goal of these algorithms is for example to identify cell types and narrow down the genomic loci that determine cell identity, to quantify heterogeneity in cell populations, or to identify drivers of dynamical epigenetic processes like cell differentiation or disease progression.
2. Single-cell chromosomal instabilities:
Aneuploidy and copy number variations/alterations are genomic duplication and deletion events that range from a small number of base pairs to whole chromosomes. Using single-cell sequencing methods, it is possible to study copy number variations at the individual single cell level. The “Computational Epigenomics” group has pioneered the development of computational methods for this analysis, using both single-cell DNA sequencing as well as epigenomic sequencing. The group actively works on the development of new algorithms to determine copy number variations with better accuracy and to determine the relationship between these genomic alterations, the epigenomic state of the gained and lost regions, and the transcription of the genes.
3. Biological and biomedical applications:
In collaboration with experimental partners, the group works on different biological and biomedical applications, with the goal to apply the developed methods to the analysis and interpretation of different types of single-cell multi-omic data.
Highlighted publications:
-K.T. Schmid, A. Symeonidi, D. Hlushchenko, M.L. Richter, A.E. Tijhuis, F. Foijer, M. Colomé-Tatché*. Benchmarking scRNA-seq copy number variation callers. Nat. Commun. in press (2025) (bioRxiv https://doi.org/10.1101/2024.12.18.629083)
-K.R. McWilliam, Z. Keneskhanova, R.O. Cosentino, A. Dobrynin, J.E. Smith, I. Subota, M.R. Mugnier, M. Colomé-Tatché* and T.N. Siegel*. Genomic determinants of antigen expression hierarchy in African trypanosomes. Nature, 642, 182–190 (2025) https://doi.org/10.1038/s41586-025-08720-w.
-A. Ramakrishnan, A. Symeonidi, P. Hanel, K. T. Schmid, M. L. Richter, M. Schubert, M. Colomé-Tatché*. epiAneufinder identifies copy number alterations from single-cell ATAC-seq data. Nat. Commun. 14, 5846 (2023).
-M.D. Luecken, M. Büttner, K. Chaichoompu, A. Danese, M. Interlandi, M.F. Mueller, D.C. Strobl, L. Zappia, M. Dugas, M. Colomé-Tatché*, F.J. Theis*. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).