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...
A generalization of t-SNE and UMAP to single-cell multimodal omics.
Van Do H, Canzar S.
Genome Biology. 2021;22(1):130. doi: 10.1186/s13059-021-02356-5.
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;31(4):677-688 doi: 10.1101/gr.267906.120.
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.