TNet is a phylogeny-based method for reconstructing transmission networks for infectious diseases. It takes as input a phylogeny of the strain (pathogen) sequences sampled from infected hosts and analyzes it to estimate the underlying transmission network. TNet relies on the availability of multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Each run of TNet on the same input tree can result in a different estimate of the transmission network, and so TNet should be executed multiple times (say 100) on the input phylogeny and an aggregated transmission network should be constructed from the resulting outputs. The method is parameter-free and highly scalable and can be easily applied within seconds to datasets with hundreds of strain sequences and hosts.
TNet was implemented by Saurav Dhar and is available open source under the GNU general public license. TNet is implemented in Python 3. It also uses the Biopython library that can be freely downloaded as described at https://biopython.org/.
- Manual: TNet-Manual.pdf
- Source code: https://github.com/sauravdhr/tnet_python (available open source under GNU GPL)
TNet can be cited as follows:
- TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity
Saurav Dhar, Chengchen Zhang, Ion Mandoiu, Mukul S. Bansal.
International Symposium on Bioinformatics Research and Applications (ISBRA) 2020, LNCS 12304: 203-216.
TNet-Geo is a customised and extended version of TNet that is specifically designed for geographical transmission network analysis when multiple strain sequences from different infected hosts are available from the different geographic regions (e.g., countries) under consideration. It can be used to estimate the extent of infection spread from one region to another in a given time period. TNet-Geo is freely available from the following URL: http://compbio.engr.uconn.edu/software/tnet-geo/
Funding: Development of the software resource(s) available from this webpage was funded in part by U.S. National Science Foundation award CCF 1618347.