Comparative Genomics
Learning from the differences - what our closest relatives can teach us about molecular evolution.
Research
© Ines Hellmann
We are mainly interested in the evolution of gene regulation. We investigate this by integrating studies of cis-regulatory sequence evolution with analyses of chromatin state, gene expression, and expression network dynamics. Much of our work takes a comparative approach, focusing on humans, great apes, and Old World monkeys. Because evolutionary comparisons between closely related species often require precise quantitative methods, we also develop computational tools for bulk and single-cell transcriptomic analysis.
Research Topics
Single-cell RNA-seq methods are now coming of age, and methods for clustering, classification, and pseudotime analysis are becoming increasingly standardised. However, results are mostly qualitative, making quantitative comparisons across species difficult. We are developing and benchmarking methods that allow for quantitative, comparative analysis of single-cell omics data across closely related (primate) species. To this end, we consider the entire pipeline, including demultiplexing, background correction, mapping, normalisation, and the definition of orthologous features and cell types.
While the genetic code for translating mRNAs into proteins is well known, it is much less clear how TFBS and CRE activity control gene expression. What has become clear is that the regulatory code is much more dynamic, with many epistatic interactions that probably encode robustness but also allow for developmental system drift. Hence, the same molecular phenotype, e.g. an expression pattern, can be encoded in different ways in different species. Thus, we see cross-species comparisons of CRE and gene activity as a valuable tool for furthering our understanding of the regulatory code. To unravel these complex relationships, novel LLMs that focus on sequence-to-function translation, in combination with interpretable AI, hold great promise.
Finally, more complex phenotypes are encoded by the interactions of many genes. To take the next step towards understanding the evolution of complex phenotypes, we study the evolution of gene regulatory networks. Again, we take a comparative approach, where we 1) quantitatively compare co-expression network topologies across species and 2) compare causal regulatory relationships by perturbing transcription factors in different species. For these experiments, manipulatable cell systems are key. To this end, we rely on induced pluripotent stem cells and are thus particularly interested in pluripotency networks and early development.
Methods
R packages developed by the Hellmann Lab
© Ines Hellmann
- CroCoNet - a tool to quantitatively compare gene regulatory networks across species and identify conserved and diverged modules.
- Cross-species gRNA-design shiny app for transcription factor selection and gRNA design in perturb-seq experiments.
- powsimR - power analysis for bulk and single-cell RNA-seq experiments.
- Benchmarking background noise in single-cell RNA-seq data
- Cross-species analysis of single cell data.
- zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
Selected Publications
Térmeg, A., Storozhuk, V., Kliesmete, Z., Edenhofer, F. C., Geuder, J., Dietl, T., Vieth, B., Janssen, P., Richter, D., Bonev, B., & Hellmann, I. (2026). CroCoNet: A framework for the quantitative comparison of gene regulatory networks across species. Genome Biology, 27(1), 228. https://doi.org/10.1186/s13059-026-04152-5
Jocher, J., Janssen, P., Vieth, B., Edenhofer, F. C., Dietl, T., Térmeg, A., Spurk, P., Geuder, J., Enard, W., & Hellmann, I. (2026). Identification and comparison of orthologous cell types from primate embryoid bodies shows limits of marker gene transferability. eLife, 14, RP105398. https://doi.org/10.7554/eLife.105398.3
Kliesmete, Z., Orchard, P., Lee, V. Y. K., Geuder, J., Krauß, S. M., Ohnuki, M., Jocher, J., Vieth, B., Enard, W., & Hellmann, I. (2024). Evidence for compensatory evolution within pleiotropic regulatory elements. Genome Research, 34(10), 1528–1539. https://doi.org/10.1101/gr.279001.124
Janssen, P., Kliesmete, Z., Vieth, B., Adiconis, X., Simmons, S., Marshall, J., McCabe, C., Heyn, H., Levin, J. Z., Enard, W., & Hellmann, I. (2023). The effect of background noise and its removal on the analysis of single-cell expression data. Genome Biology, 24(1), 140. https://doi.org/10.1186/s13059-023-02978-x
Kliesmete, Z., Wange, L. E., Vieth, B., Esgleas, M., Radmer, J., Hülsmann, M., Geuder, J., Richter, D., Ohnuki, M., Götz, M., Hellmann, I., & Enard, W. (2023). Regulatory and coding sequences of TRNP1 co-evolve with brain size and cortical folding in mammals. eLife, 12, e83593. https://doi.org/10.7554/eLife.83593
Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019). A systematic evaluation of single-cell RNA-seq analysis pipelines. Nature Communications, 10(1), 4667. https://doi.org/10.1038/s41467-019-12266-7
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018). zUMIs: A fast and flexible pipeline to process RNA sequencing data with UMIs. GigaScience, 7(6), giy059. https://doi.org/10.1093/gigascience/giy059
Vieth, B., Ziegenhain, C., Parekh, S., Enard, W., & Hellmann, I. (2017). powsimR: Power analysis for bulk and single-cell RNA-seq experiments. Bioinformatics, 33(21), 3486–3488. https://doi.org/10.1093/bioinformatics/btx435
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2016). The impact of amplification on differential expression analyses by RNA-seq. Scientific Reports, 6, 25533. https://doi.org/10.1038/srep25533
People
Funding
