Palash Sashittal1, Mohammed El-Kebir2. 1. Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA. 2. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA.
Abstract
MOTIVATION: The combination of genomic and epidemiological data holds the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. RESULTS: We formulate the direct transmission inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce Transmission Tree Uniform Sampler (TiTUS), a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritize parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS's ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. AVAILABILITY AND IMPLEMENTATION: https://github.com/elkebir-group/TiTUS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The combination of genomic and epidemiological data holds the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. RESULTS: We formulate the direct transmission inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce Transmission Tree Uniform Sampler (TiTUS), a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritize parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS's ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. AVAILABILITY AND IMPLEMENTATION: https://github.com/elkebir-group/TiTUS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Simon Dellicour; Guy Baele; Gytis Dudas; Nuno R Faria; Oliver G Pybus; Marc A Suchard; Andrew Rambaut; Philippe Lemey Journal: Nat Commun Date: 2018-06-08 Impact factor: 14.919
Authors: Remco Bouckaert; Timothy G Vaughan; Joëlle Barido-Sottani; Sebastián Duchêne; Mathieu Fourment; Alexandra Gavryushkina; Joseph Heled; Graham Jones; Denise Kühnert; Nicola De Maio; Michael Matschiner; Fábio K Mendes; Nicola F Müller; Huw A Ogilvie; Louis du Plessis; Alex Popinga; Andrew Rambaut; David Rasmussen; Igor Siveroni; Marc A Suchard; Chieh-Hsi Wu; Dong Xie; Chi Zhang; Tanja Stadler; Alexei J Drummond Journal: PLoS Comput Biol Date: 2019-04-08 Impact factor: 4.475