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Canzar Lab - Algorithmic Computational Biology

Research Topics

  • Computational Genomics & Transcriptomics
  • Algorithm Engineering
  • Protein-Protein Interaction Networks
  • Combinatorial Optimization

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Dr. Stefan Canzar

phone: +49 (0)89 - 2180 71050

Next-generation sequencing instruments produce a huge number of short DNA 'reads', each of which carries little information by itself. These reads therefore have to be pieced together by well-engineered algorithms to reconstruct biologically meaningful measurements. The lab's goal is the development of accurate mathematical models, efficient algorithms, and usable software to solve these complex, high-dimensional puzzles. Read more...

Selected Publications

Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data.
Van Do H, Rojas Ringeling F, Canzar S.
Genome Research. 2021. 

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data.
Alqassem I, Sonthalia Y, Klitzke-Feser E, Shim H, Canzar S.
Bioinformatics. 2021. doi:10.1093/bioinformatics/btab050

Sphetcher: Spherical thresholding improves sketching of single-cell transcriptomic heterogeneity.
Van Do H, Elbassioni K, Canzar S.
iScience. 2020;23(6):101126. doi: 10.1016/j.isci.2020.101126.

Dynamic pseudo-time warping of complex single-cell trajectories. 
Van Do H, Blažević M, Monteagudo P, Borozan L, Elbassioni K, Laue S, Rojas Ringeling F, Matijević D, Canzar S.
Research in Computational Molecular Biology (RECOMB). 2019;11467:294–296. (preprint bioRxiv)


More publications


19.02.2021 - New paper
Specter for (fast) multi-modal clustering of single cells accepted at Genome Research.

30.01.2021 - New paper
McSplicer infers a probabilistic model of alternative splicing

21.02.2020 - New paper

Single-cell sampling by Van Hoan Do accepted for presentation at RECOMB-seq and for publication at iScience

18.12.2019 - DAAD Project funding

Application of Generic Optimizer in Medical Imaging Classification Problems