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A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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Covariance decomposition for distance based species tree estimation.

Georgios Aliatimis1, Ruriko Yoshida2, Burak Boyacı3

  • 1STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, LA1 4YW, UK.

BMC Bioinformatics
|July 4, 2026
PubMed
Summary
This summary is machine-generated.

Species-tree methods face noise from gene-tree variation and sequence errors. Understanding these noise sources helps improve phylogenomic analyses by guiding data collection and method development.

Keywords:
CovarianceMETALMultispecies coalescent modelSplit supportSubstitution models

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Area of Science:

  • Phylogenomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenomic methods reconstruct species trees from multi-locus sequence data.
  • These methods are challenged by two primary noise sources: gene tree stochasticity under the multispecies coalescent model (MSC) and sequence-level substitutional noise.
  • Fast agglomerative methods like GLASS, STEAC, and METAL cluster multi-locus information using distance-based approaches.

Purpose of the Study:

  • To derive the exact covariance matrix for pairwise distance estimates under a joint MSC-plus-substitution model.
  • To algebraically decompose this covariance into components reflecting coalescent variation and sequence-level stochasticity.
  • To provide reliable confidence estimation for phylogenomic analyses.

Main Methods:

  • Derivation of the exact covariance matrix for pairwise distance estimates.
  • Algebraic decomposition of the covariance matrix into coalescent and substitutional noise components.
  • Development of a Gaussian-sampling procedure for generating split support values for METAL trees.

Main Results:

  • The study identifies parameter regimes where coalescent variance or substitutional noise dominates.
  • Substitutional noise is primary at very low and very high mutation rates; coalescent variance dominates at intermediate rates.
  • The interval where coalescent variance dominates narrows with increasing species-tree height.

Conclusions:

  • The relative importance of noise sources dictates optimal data collection strategies (more loci vs. longer sequences).
  • Ignoring the weaker noise source can be justified in specific parameter regimes.
  • The proposed Gaussian-sampling method provides more reliable confidence estimates than traditional bootstrapping for METAL trees.