Abstract
The inference of disease transmission networks from genetic sequence data is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from (a subset of) infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference cannot take within-host strain diversity into account, which affects their accuracy, and, moreover, are highly computationally intensive and unscalable.
In this work, we introduce a new phylogenetic approach, TNet, for inferring transmission networks that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity using a similar computational framework. TNet is freely available open-source from https://compbio.engr.uconn.edu/software/TNet/.
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Acknowledgements
The authors wish to thank Dr. Pavel Skums (Georgia State University) and the Centers for Disease Control for sharing their HCV outbreak data. We also thank Samuel Sledzieski for creating and sharing the simulated transmission network datasets used in this work.
Funding
This work was supported in part by NSF award CCF 1618347 to IM and MSB.
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Dhar, S., Zhang, C., Mandoiu, I., Bansal, M.S. (2020). TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_18
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DOI: https://doi.org/10.1007/978-3-030-57821-3_18
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