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Minimizing the deep coalescence cost.

Dawid Dąbkowski¹, Paweł Tabaszewski¹, Paweł Górecki¹

¹1 Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Banacha 2, Warsaw 02-097, Poland.

Journal of Bioinformatics and Computational Biology

|November 14, 2018

Summary

This study addresses gene-species matching in metagenomics using phylogenetic trees. It introduces parsimony-based methods, including exact and heuristic algorithms, to accurately identify species from genetic data.

Keywords:

Deep coalescence gene tree metagenomics species taxonomy species tree

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Area of Science:

Computational Biology
Bioinformatics
Phylogenetics

Background:

Metagenomic studies identify species in environmental samples by matching molecular sequences (genes) to species taxonomy.
Accurate gene-species matching is crucial for understanding microbial communities and ecological relationships.

Purpose of the Study:

To formulate the gene-species matching problem within a parsimony framework using phylogenetic trees.
To develop exact and heuristic algorithms for solving gene-species matching, considering deep coalescence costs.

Main Methods:

Formulation of gene-species matching using binary phylogenetic trees (gene and species) under deep coalescence cost.
Development of an exact dynamic programming algorithm (exponential complexity).
Generalization to non-binary trees and development of time and space-efficient heuristic algorithms.

Main Results:

The problem is solved for binary trees, including cases with caterpillar trees.
An exact dynamic programming solution is proposed, though with high computational complexity.
Heuristic algorithms are developed for efficient gene-species matching on general phylogenetic trees.
Computational experiments on simulated and empirical datasets validate the proposed methods.

Conclusions:

The study provides a robust framework and efficient algorithms for gene-species matching in metagenomics.
The developed methods enhance the accuracy and efficiency of species identification from environmental DNA data.
This work contributes to advancing computational approaches in microbial ecology and evolutionary biology.