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 transmission network (constructed based on a single optimal sampled uniformly at random from among all optima), 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 and usage instructions 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.
Funding: Development of the software resource(s) available from this webpage was funded in part by U.S. National Science Foundation award CCF 1618347.