Canzar Lab - Algorithmic Computational Biology
- Computational Genomics & Transcriptomics
- Algorithm Engineering
- Single Cell Genomics
- 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...
Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.
Ringeling FR, Chakraborty S, Vissers C, Reiman D, Patel AM, Lee KH, Hong A, Park CW, Reska T, Gagneur J, Chang H, Spletter ML, Yoon KJ, Ming GL, Song H, Canzar S
Nature Biotechnology. 2022. doi:10.1038/s41587-021-01136-7
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.
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.