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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction.

Elizabeth S Allman¹, John A Rhodes¹, Seth Sullivant²

¹1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks, Alaska.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|July 9, 2016

Summary

This summary is machine-generated.

Phylogenetic tree inference using k-mer frequencies can be inconsistent. This study introduces model-based distance corrections for accurate phylogenetic tree construction and improved multiple sequence alignment methods.

Keywords:

alignment-free methods k-mer phylogenetic trees

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Area of Science:

Computational Biology
Bioinformatics
Phylogenetics

Background:

K-mer frequencies offer a method for phylogenetic tree inference, bypassing multiple sequence alignment.
Current methods using Euclidean distance for k-mer vectors can lead to statistically inconsistent tree metrics.

Purpose of the Study:

To address the statistical inconsistency in k-mer based phylogenetic tree inference.
To develop a statistically sound method for estimating evolutionary distances from k-mer frequencies.
To improve the accuracy of phylogenetic tree construction and guide tree generation for multiple sequence alignment.

Main Methods:

Derivation of model-based distance corrections for orthologous sequences.
Analysis of model parameter identifiability from k-mer frequency data.
Simulation studies comparing corrected distances with existing k-mer methods.

Main Results:

The proposed model-based distance corrections yield statistically consistent phylogenetic tree inference.
Identifiability of model parameters from k-mer frequencies is demonstrated.
Corrected distances outperform standard k-mer methods in simulations, even with insertions and deletions.

Conclusions:

Model-based distance corrections provide a statistically robust approach to k-mer based phylogenetics.
This method enhances the reliability of phylogenetic trees inferred without multiple sequence alignment.
The findings have significant implications for improving multiple sequence alignment algorithms.