ArticleBioinformatics (Oxford, England)2020
Sampling and summarizing transmission trees with multi-strain infections.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The tree labeling polytope: A unified approach to ancestral reconstruction problems.Cell systems · 2026Article
- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.PLoS computational biology · 2025Article
- Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data.Bioinformatics (Oxford, England) · 2024Article
- Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.Molecular biology and evolution · 2024Article
- SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.Cell systems · 2022Article
- Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review.Pathogens (Basel, Switzerland) · 2022Review
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2 authors.
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Abstract
motivationThe 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.
resultsWe 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.
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