The Computational Biology Research Group develops new computational methods, efficient algorithms, and powerful software tools to help answer fundamental biological questions. We are especially interested in problems related to understanding the evolution of genes, genomes, and species. Some of our specific projects include:
- Inferring gene family and genome evolution through gene duplication, horizontal transfer, and loss.
- Understanding evolution at the sub-gene/domain level.
- Reconstructing highly accurate gene trees in both eukaryotes and prokaryotes for evolutionary and functional genomic studies.
- Inferring infectious disease transmission networks.
- Building whole-genome and multi-locus species phylogenies.
Videos describing some of our work are publicly available on YouTube at the following URLs:
ISMB 2012 talk: https://youtu.be/Z30z9xAh8_U
ISMB 2014 talk: https://youtu.be/GoMcy7jhp6s
ISCBacademy Webinar 2021: https://youtu.be/P8P_yDeInN4
The computer science and engineering department at UConn is one of the best places in the world for doing research in computational biology and bioinformatics (for example, see this metrics-based ranking). The following research positions are available:
PhD positions: Positions are available in the computational biology group for bright and motivated PhD students. Please click here for further details.
Research opportunities for UConn undergraduate students: Positions are also available for qualified UConn undergraduate students who wish to gain research experience by working on exciting research problems. Please click here for further details.
March 2022: Sumaira's paper on the impact of partial gene transfer on gene tree reconstruction accepted to RECOMB-CG 2022.
March 2022: Book chapter describing the use of phylogenetic reconciliation to understand microbial evolution to appear in Methods in Molecular Biology book series.
August 2021: Samson's paper on duplication-transfer-loss reconciliation with extinct and unsampled lineages to appear in Algorithms.
July 2021: Saurav's paper on disease transmission network inference at the level of individuals and geographical regions accepted to IEEE/ACM Transactions on Computational Biology and Bioinformatics.
July 2021: Keegan's paper on optimal completion and comparison of incomplete phylogenetic trees wins best student paper award at CPM 2021.
June 2021: An extended version of TNet designed for geographical transmission network inference is now available: TNet-Geo.
June 2021: Lab member Lina Kloub has successfully defended her PhD thesis. Congratulations!
May 2021: Lab member Saurav Dhar has graduated with his master's degree. We wish Saurav all the best!
May 2021: After over two years with the lab, undergraduate researcher Keegan Yao is graduating and will be starting his PhD in computer science at Duke. We wish Keegan all the best!
April 2021: Keegan's paper on optimal completion and comparison of incomplete phylogenetic trees accepted to CPM 2021.
February 2021: Lina's paper on detection of large-scale horizontal multigene transfer events accepted to Molecular Biology and Evolution.
July 2020: Abhijit's paper on using machine learning to distinguish between additive and replacing horizontal gene transfers accepted to ACM-BCB 2020.
Software Quick Links
Phylogenetic reconciliation; Gene family evolution
Protein domain and subgene level evolution
Phylogenetic simulation of gene and subgene evolution
Horizontal gene transfer inference
Phylogenomics; Whole-genome species tree construction
Gene tree reconstruction and error-correction
Viral transmission network inference
Tree comparison; Optimal tree completion