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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Inferring phylogenies of evolving sequences without multiple sequence alignment.

Cheong Xin Chan¹, Guillaume Bernard¹, Olivier Poirion²

¹Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Australia.

Scientific Reports

|October 1, 2014

Summary

This summary is machine-generated.

Alignment-free phylogenetic methods offer faster and more robust evolutionary inference than traditional alignment approaches. These D2 statistics-based methods excel with large datasets, despite some sensitivity to recent divergence.

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Area of Science:

Bioinformatics
Computational Biology
Evolutionary Biology

Background:

Alignment-free methods are emerging for phylogenetic inference, extracting sub-sequence properties to build distance matrices.
Their scalability and robustness against evolutionary processes require systematic investigation.

Purpose of the Study:

To systematically assess the accuracy of alignment-free phylogenetic inference using D2 statistics.
To compare the performance of D2 methods against multiple sequence alignment approaches under various evolutionary scenarios.

Main Methods:

Utilized simulated nucleotide and amino acid sequence sets of varying sizes.
Employed D2 statistics for alignment-free phylogenetic analysis.
Evaluated performance across diverse empirical datasets.

Main Results:

D2 methods demonstrate greater robustness than multiple sequence alignment against among-site rate heterogeneity, compositional biases, rearrangements, and indels.
D2 methods are more sensitive to recent sequence divergence and truncation.
Alignment-free methods perform well on low-divergence sequences with increased computational speed.

Conclusions:

Alignment-free methods, particularly D2 statistics, show significant promise for large-scale phylogenomics due to their scalability and speed.
These methods offer a robust alternative to traditional alignment-based approaches for certain evolutionary scenarios.