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A Practical Guide to Phylogenetics for Nonexperts

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Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking.

Marcin Bogusz¹, Simon Whelan¹

¹Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, 752 36 Uppsala, Sweden.

Systematic Biology

|September 17, 2016

Summary

This summary is machine-generated.

Phylogenetic tree inference can be biased by sequence alignment. PaHMM-Tree estimates pairwise distances without alignment, improving accuracy, especially for deep divergences where alignment errors are high.

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Area of Science:

Evolutionary biology
Bioinformatics
Computational phylogenetics

Background:

Phylogenetic tree inference commonly uses a two-step alignment-then-tree process.
The alignment step can introduce biases, affecting tree accuracy.
Alignment-free methods offer an alternative but have limitations.

Purpose of the Study:

Introduce PaHMM-Tree, a novel method for phylogenetic tree inference.
Estimate pairwise distances without assuming a single sequence alignment.
Benchmark PaHMM-Tree against existing phylogenetic tree inference methods.

Main Methods:

Developed PaHMM-Tree, a neighbor joining-based method using statistical alignment for pairwise distances.
Conducted simulations to compare PaHMM-Tree with alignment-based and alignment-free methods.
Assessed performance across varying degrees of sequence divergence and alignment uncertainty.

Main Results:

PaHMM-Tree's distance estimates are as accurate as standard methods using true alignments.
PaHMM-Tree yields more accurate phylogenetic trees than other distance-based methods.
For deep divergences, PaHMM-Tree outperforms all assessed methods due to alignment error reduction.
Two-step statistical alignment methods perform better for shallow divergences.

Conclusions:

PaHMM-Tree offers improved phylogenetic accuracy, particularly for distantly related sequences.
The method mitigates biases introduced by the traditional alignment step.
PaHMM-Tree represents a significant advancement in distance-based phylogenetic inference.