Computational Biology; Data Interpretation, Statistical; Data Mining; Epigenomics; Fibroblasts; Genomics; Rheumatic Diseases; Sequence Analysis; T Cells; Transcriptome
- Rheumatology Data Science Research Group
Research Area: Research area: We are applying statistical and 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. Other members: Claudia Hana - biostatistics
- 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
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.
- Hana, C.A. et al., 2021. Serum metabolomics analysis reveals increased lipid catabolism in mildly hyperbilirubinemic Gilbert’s syndrome individuals. Metabolism, 125, p.154913. Available at: http://dx.doi.org/10.1016/j.metabol.2021.154913.
- Del Vecchio, G. et al., 2020. Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes. Epigenetics, 16(6), pp.642–661. Available at: http://dx.doi.org/10.1080/15592294.2020.1816774.
- Farrell, C. et al., 2021. BiSulfite Bolt: A bisulfite sequencing analysis platform. GigaScience, 10(5). Available at: http://dx.doi.org/10.1093/gigascience/giab033.
- Tosevska, A. et al., 2016. Longer telomeres in chronic, moderate, unconjugated hyperbilirubinaemia: insights from a human study on Gilbert’s Syndrome. Scientific Reports, 6(1). Available at: http://dx.doi.org/10.1038/srep22300.