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Single-cell sequencing reveals cellular evolution but is error-prone. New Bayesian models in BEAST2 accurately account for these errors, improving evolutionary inference and phylogenetic diversity estimates.

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Single-cell sequencing offers unprecedented resolution for studying cellular evolution.
  • However, limited starting material in single-cell DNA sequencing leads to higher error rates compared to bulk sequencing.
  • Accurate evolutionary inference from single-cell data requires robust methods to handle these errors.

Purpose of the Study:

  • To develop and implement error and mutation models for single-cell data within the BEAST2 Bayesian framework.
  • To improve the accuracy of evolutionary inference, including divergence time and substitution parameter estimation.
  • To assess the impact of uncorrected errors on phylogenetic diversity estimates.

Main Methods:

  • Development of novel error and mutation models tailored for single-cell sequencing data.
  • Integration of these models into the extensible Bayesian phylogenetic software BEAST2.
  • Simulation studies to evaluate model performance and accuracy.
  • Phylogenetic reconstruction using single-cell DNA sequencing data from colorectal cancer and healthy patients.

Main Results:

  • The developed models significantly increase the accuracy of relative divergence times and substitution parameter estimations.
  • Phylogenetic analyses incorporating error models shift estimated terminal splitting events forward in time.
  • Failure to account for errors can lead to overestimation of phylogenetic diversity, with 30-50% of apparent diversity potentially attributed to technical errors.

Conclusions:

  • The new Bayesian framework in BEAST2 provides a robust approach for evolutionary inference from error-prone single-cell sequencing data.
  • Accurate modeling of technical errors is crucial for reliable phylogenetic reconstruction and diversity estimation in single-cell genomics.
  • This work enhances the utility of single-cell sequencing for understanding cellular evolutionary processes.