Janko Tackmann

Janko Tackmann

Postdoctoral Fellow

Dr. Janko Tackmann

Office: Y55-L-56

Phone: +41 44 635 31 48

 

 

Curriculum Vitae

  • 2019-present Postdoc at the von Mering lab, Institute of Molecular Life Sciences, Universität Zürich (CH)
  • 2014-2019 Doctorate at the von Mering lab, Institute of Molecular Life Sciences, Universität Zürich (CH)
  • 2014 Research intern, Biological Research Center, Szeged (HU)
  • 2013 MSc Project, Biomedical Center, Uppsala (SE)
  • 2012-2013 Erasmus in Evolutionary Biology, Uppsala Universitet (SE)
  • 2011–2014 MSc in Bioinformatics, Freie Universität Berlin (DE)
  • 2011 Research intern, Max-Planck-Institute for Evolutionary Anthropology, Leipzig (DE)
  • 2008–2011 BSc in Bioinformatics, Freie Universität Berlin (DE)

Research Interest

  • Microbial ecosystem modeling
  • Probabilistic microbe-microbe interaction prediction
  • Environmental factors in microbial community assembly
  • Biases in large-scale aggregate microbial sequencing data
  • Microbiome-based forensics

Publications

Hanya, G., **Tackmann, J.**, Sawada, A., Lee, W., Pokharel, S. S., de Castro Maciel, V. G., ... & Liu, J. (2020). Fermentation Ability of Gut Microbiota of Wild Japanese Macaques in the Highland and Lowland Yakushima: In Vitro Fermentation Assay and Genetic Analyses. Microbial Ecology, 1-16. https://doi.org/10.1007/s00248-020-01515-8

**Tackmann, J.**, Rodrigues, J. F. M., & von Mering, C. (2019). Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data. Cell systems, 9(3), 286-296. https://doi.org/10.1016/j.cels.2019.08.002

**Tackmann, J.**, Arora, N., Schmidt, T. S. B., Rodrigues, J. F. M., & von Mering, C. (2018). Ecologically informed microbial biomarkers and accurate classification of mixed and unmixed samples in an extensive cross-study of human body sites. Microbiome, 6(1), 1-16. https://doi.org/10.1186/s40168-018-0565-6

Matias Rodrigues, J. F., Schmidt, T. S., **Tackmann, J.**, & von Mering, C. (2017). MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis. Bioinformatics, 33(23), 3808-3810. https://doi.org/10.1093/bioinformatics/btx517


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