Canzar Lab - Algorithmic Computational Biology
- Computational Genomics & Transcriptomics
- Algorithm Engineering
- Protein-Protein Interaction Networks
- Combinatorial Optimization
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...
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)