Computational Biology for Individualised Medicine
Infections are among the biggest threats to health and the most significant causes of death worldwide. Our aim is to reveal the host genetic risk factors and their downstream molecular pathways, which are crucial to make progress in understanding and treating infectious diseases in an individualised manner as well as to improve the identification of patients at risk. As a department of the HZI, we are assigned to CiiM and are currently based at TWINCORE in Hannover.
The research department “Computational Biology for Individualised Medicine” studies the interaction of genetic background and environment, and its contribution to infection and immune-related diseases. We focus on applying and developing the computational and statistical approaches to study the effect of genetic factors on a variety of molecular levels (such as genetics, genomics, metabolomics etc.), immunological parameters and functions, and complex diseases. We are currently working on the following areas:
Genetic regulation of molecular and immune phenotypes
We study how genetic variation affects molecular and immune phenotypes such as gene expression, metabolites and cytokine responses to stimulations. We develop computational methods and algorithms to fully exploit high-throughput datasets from the most recent profiling technologies, e.g., causal inference and deconvolution of the overall genetic regulation effects of gene expression into relevant cell types.
Integration of Multi-Omics
We integrate large multi-omics data sets and immune profiling of patient/control cohorts to unravel the genotype-phenotype map on a genome-wide scale and built computational models for predicting immune functions and disease risk.
Single cell Genomics
We apply cutting-edge techniques to study genetic regulation of gene expression in response to stimuli at single-cell resolution and we develop novel computational approaches for single-cell biology.
Since 2019, Yang Li has headed the Department of Bioinformatics in Individualised Infection Medicine at CiiM and HZI and has also been appointed Director of CiiM. The focus of her research is on understanding the molecular mechanisms of immunological/infectious diseases through the integration of multi-omics data.
Yang Li received her doctoral degree of bioinformatics in 2010 from the University of Groningen, the Netherlands. She continued her career as a postdoc at the Groningen Bioinformatics Centre and she received the Young Investigator Award of Bioinformatics from the Netherlands Bioinformatics Centre in 2011. She then became a principal investigator / assistant professor in the Department of Genetics at the University Medical Centre Groningen. Her successful research on systems genetics of immune-related diseases was awarded with two personal grants: the VENI (Dutch Organization for Scientific Research, 2013) and the Off-Road (the Netherlands Organisation for Health Research and Development, 2016). In 2018, Yang Li received the prestigious “Hypatia Grant” from the Radboud University Medical Center.
She is also a co-applicant for several large consortia. She has published ~70 scientific articles to date, including in Cell, NatureMedicine, Nature Immunology and Cell Reports and is a peer reviewer for many journals such as Nature Ecology & Evolution, Bioinformatics, Journal of the Royal Statistical Society (UK) and Genetics.
Bakker, O.B., Aguirre-Gamboa, R., Sanna, S., Oosting, M., Smeekens, S.P., Jaeger, M., Zorro, M., Võsa, U., Withoff, S., Netea-Maier, R.T., Koenen, H., Joosten, I., Xavier, R.J., Franke, L., Joosten, L.A.B., Kumar, V., Wijmenga, C.#, Netea, M.G#, Li, Y.# Integration of multi-omics datasets and deep phenotyping enables prediction of cytokine production in response to pathogens
Nature Immunology 2018(19):776-786. doi:10.1038/s41590-018-0121-3
Li, Y.#, Oosting, M., Smeekens, S.P., Jaeger, M., Aguirre-Gamboa, R., Le, K.T.T., Deelen, P., Ricaño-Ponce, I., Schoffelen, T., Jansen, A.F.M., Swertz, M.A., Withoff, S., van de Vosse, E., van Deuren, M., van de Veerdonk, F., Zhernakova, A., van der Meer, J.W.M., Xavier, R.J., Franke, L., Joosten, L.A.B.#, Wijmenga, C.#, Kumar, V.#, Netea, M.G# A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans
Cell 2016(167):1099–1110.e14. doi:10.1016/j.cell.2016.10.017
Aguirre-Gamboa, R., Joosten, I., Urbano, P.C.M., van der Molen, R.G., van Rijssen, E., van Cranenbroek, B., Oosting, M., Smeekens, S., Jaeger, M., Zorro, M., Withoff, S., van Herwaarden, A.E., Sweep, F.C.G.J., Netea, R.T., Swertz, M.A., Franke, L., Xavier, R.J., Joosten, L.A.B., Netea, M.G., Wijmenga, C., Kumar, V., Li, Y#, Koenen, H.J.P.M.# Differential Effects of Environmental and Genetic Factors on T and B Cell Immune Traits
Cell Reports 2016, doi:10.1016/j.celrep.2016.10.053
Li, Y.*, Oosting, M.*, Deelen, P., Ricaño-Ponce, I., Smeekens, S., Jaeger, M., Matzaraki, V., Swertz, M.A., Xavier, R.J., Franke, L., Wijmenga, C., Joosten, L.A.B., Kumar, V., Netea, M.G. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi
Nature Med. 2016(22)952–960. doi:10.1038/nm.4139
Van der Velde, K.J., de Haan, M., Zych, K., Arends, D., Snoek, L.B., Kammenga, J.E., Jansen, R.C., Swertz, M.A.#, Li, Y.# WormQTLHD−a web database for linking human disease to natural variation data in C. elegans
Nucleic Acids Res. 2014 (42)D794-801. doi:10.1093/nar/gkt1044
A complete list of Yang Li's publications can be found on GoogleScholar.
The Research Department “Computational Biology for Individualised Medicine” welcomes applications from candidates qualified for either a PhD or Postdoc position.
You can find all open job advertisements on the HZI's job page.
Unsolicited applications are always welcome. We are looking for motivated applicants with a strong background in Bioinformatics / Computational Biology, Computer Science, Statistics, Biology or Physics, good programing skills and interest in interdisciplinary research in biology and infection research.
We continuously offer Masters/Bachelor research projects. Please contact us for details by email.
Our research focuses on understanding inter-individual variation in susceptibility to immune related diseases and predicting immune functions through integration of large multi-omics and in-depth immune phenotypes datasets from a variety of existing and planned patient cohorts. Methods that we employ or develop in our research are related to the fields of Bioinformatics, Systems Genetics, Machine learning & deep learning and Systems immunology.