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Deconvolving Phylogenetic Distance Mixtures.

Shayesteh Arasti1, Ali Osman Berk Şapcı2, Eleonora Rachtman3

  • 1Department of Computer Science and Engineering, UC San Diego, CA 92093, USA.

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|February 6, 2026
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Summary
This summary is machine-generated.

This study introduces a new method for analyzing complex biological sequence mixtures. The phylogenetic distance deconvolution (PDD) approach accurately deconvolves mixtures, improving downstream microbiome analyses.

Keywords:
MetagenomicsMixture deconvolutionPhylogenetic distancesPhylogenetic mixture analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Metagenomics and metabarcoding analyze mixtures of organisms, facing challenges from evolutionary relationships and noisy sequence data.
  • Existing methods struggle to simultaneously address the evolutionary dependencies and data noise inherent in mixture analysis.

Purpose of the Study:

  • To develop a novel approach for characterizing sequence mixtures by deconvolving phylogenetic distances.
  • To improve the accuracy of microbiome analyses and other applications by providing a less noisy profile of the mixture.

Main Methods:

  • Formalized the phylogenetic distance deconvolution (PDD) problem.
  • Proposed an exact algorithm and an efficient heuristic greedy algorithm with local refinements.
  • Implemented the PDD approach in a tool called DecoDiPhy.

Main Results:

  • DecoDiPhy accurately deconvolves phylogenetic mixture distances with quadratic scaling.
  • The heuristic algorithm is effective in practice.
  • Consolidated reads mapped to numerous branches into fewer placements, enhancing downstream task accuracy.

Conclusions:

  • The PDD approach, implemented as DecoDiPhy, offers a robust solution for analyzing complex biological sequence mixtures.
  • This method improves accuracy in tasks like sample differentiation and detecting differentially abundant taxa in metagenomics.