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Sampling and summarizing transmission trees with multi-strain infections.

Palash Sashittal1, Mohammed El-Kebir2

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Accurately inferring pathogen transmission histories is challenging due to within-host diversity and multi-strain infections. The new TiTUS method, using SATISFIABILITY, effectively reconstructs transmission trees, even with complex data.

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Area of Science:

  • Computational biology
  • Epidemiology
  • Genomics

Background:

  • Accurate inference of pathogen transmission histories is crucial for understanding and controlling outbreaks.
  • Existing computational methods often fail due to their inability to account for within-host pathogen diversity and multi-strain infections.

Purpose of the Study:

  • To develop an efficient computational method for inferring transmission trees that accommodates within-host diversity and multi-strain infections.
  • To address the limitations of current approaches in accurately reconstructing outbreak transmission histories.

Main Methods:

  • Formulation of the direct transmission inference (DTI) problem for multi-strain infections.
  • Introduction of Transmission Tree Uniform Sampler (TiTUS), a SATISFIABILITY-based method for sampling transmission trees.
  • Development of criteria for prioritizing parsimonious transmission trees and a novel consensus tree approach.

Main Results:

  • Established the hardness of the decision and counting versions of the DTI problem.
  • TiTUS demonstrated the ability to almost uniformly sample from the space of transmission trees.
  • Accurate reconstruction of transmission trees on simulated data and a documented HIV transmission chain.

Conclusions:

  • TiTUS provides an effective solution for inferring transmission trees with complex pathogen characteristics.
  • The method advances the field of phylogenetic inference by incorporating crucial biological complexities.
  • TiTUS offers a valuable tool for epidemiological studies and outbreak investigations.