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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

PARSIMONY JACKKNIFING OUTPERFORMS NEIGHBOR-JOINING.

James S Farris¹, Victor A Albert², Mari Källersjö¹

¹Molekylärsystematiska laboratoriet, Naturhistoriska riksmuseet, Box 50007, Stockholm, S 104 05, Sweden.

Cladistics : the International Journal of the Willi Hennig Society

|December 18, 2021

Summary

This summary is machine-generated.

Neighbor-joining programs can hide data ambiguities. A new method, parsimony jackknifing, offers faster and more reliable tree building for phylogenetic analysis.

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Area of Science:

Phylogenetics
Computational Biology
Bioinformatics

Background:

Neighbor-joining algorithms are widely used for phylogenetic tree construction.
Existing methods like bootstrapping can yield misleading results due to issues with zero-length branches and data order sensitivity.
These limitations obscure true data ambiguities in evolutionary relationships.

Purpose of the Study:

To address the limitations of neighbor-joining bootstrapping in phylogenetic analysis.
To develop a more accurate and efficient method for assessing the reliability of phylogenetic trees.
To overcome the obscuring of data ambiguities inherent in single-tree-producing algorithms.

Main Methods:

Introduction of a novel procedure termed "parsimony jackknifing".
This method is designed to suppress zero-length branches and is insensitive to the order of terminals.
The procedure was benchmarked against existing neighbor-joining bootstrapping and branch-swapping methods.

Main Results:

Parsimony jackknifing runs hundreds of times faster than standard neighbor-joining bootstrapping.
For large datasets, it is hundreds of thousands of times faster than extensive branch-swapping.
The new method effectively screens out poorly-supported groups, providing more reliable tree topologies.

Conclusions:

Parsimony jackknifing offers a significant improvement in speed and accuracy for phylogenetic tree assessment.
It provides a more robust alternative to existing resampling techniques for uncovering data ambiguities.
This method enhances the reliability of phylogenetic inference, particularly for large datasets.