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Anela Tosevska
Dr. Anela TosevskaBioinformatics Scientist

Department of Medicine III (Division of Rheumatology)
Position: Research Associate (Postdoc)

ORCID: 0000-0002-0892-7068


Computational Biology; Data Interpretation, Statistical; Data Mining; Epigenomics; Fibroblasts; Genomics; Rheumatic Diseases; Sequence Analysis; T Cells; Transcriptome

Research group(s)

  • Rheumatology Research Lab
    Research Area: The main interest of our laboratory is to understand the exact mechanisms that are important for cell stability and identity in T cell-mediated autoimmune diseases such as Systemic Lupus Erythematosus or Rheumatoid Arthritis
  • Bioinformatics in Rheumatology Research Group
    Head: Anela Tosevska
    Research Area: Research area: We are applying computational solutions to contribute to the understanding of inflammatory pathomechanisms and a personalized medical approach, bridging the gap between data, bench and bedside in the rheumatology field. We are continuously looking for highly motivated individuals to join our team.

Research interests

My research focuses on providing a better insight on the molecular basis of autoimmune rheumatic diseases using computational approaches. I am working closely with wet-lab scientists and clinicians to generate tailored analysis for experimental data and answer complex biological questions in rheumatology. Use of bioinformatic algorithms for integration of high-throughput and experimental data allows us to perform interdisciplinary research to understand drivers of inflammation and develop a more personalized treatment approach. Several projects are currently ongoing which address the specific role of cell-cell interactions and environmental factors on the transcriptome and epigenome of pathogenic cell types. A major focus lies on epigenetic regulation of cell stability under steady state and autoimmune conditions. Other areas of interests are liquid biopsies and cell-free nucleic acids for biomarker development.

Techniques, methods & infrastructure

  • Statistical Programming Languages and Environments: R/Bioconductor, Bash scripting, Python, High Performance Computing: SGE, Slurm;

  • Curation, analysis and interpretation of sequencing data: bulk RNAseq, scRNAseq, BSseq, ChIPseq, ATACseq, WGS;

  • Integration and visualization of multi-omics datasets, including NGS, mass cytometry and proteomics;

  • Statistical analysis of clinical data;

  • Machine Learning: Caret/Scikit-Learn.

Selected publications

  1. Kugler, M. et al. (2023) ‘Cytokine-directed cellular cross-talk imprints synovial pathotypes in rheumatoid arthritis’, Annals of the Rheumatic Diseases, p. ard-2022-223396. Available at:
  2. Suto, T. et al. (2022) ‘TNFR2 is critical for TNF-induced rheumatoid arthritis fibroblast-like synoviocyte inflammation’, Rheumatology, 61(11), pp. 4535–4546. Available at:
  3. Tosevska, A. et al. (2022) ‘Cell-Free RNA as a Novel Biomarker for Response to Therapy in Head & Neck Cancer’, Frontiers in Oncology, 12. Available at:
  4. Tosevska, A. et al. (2022) ‘Integrated analysis of an in vivo model of intra-nasal exposure to instilled air pollutants reveals cell-type specific responses in the placenta’, Scientific Reports, 12(1). Available at:
  5. Farrell, C. et al. (2021) ‘BiSulfite Bolt: A bisulfite sequencing analysis platform’, GigaScience, 10(5). Available at: