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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

Do triplets have enough information to construct the multi-labeled phylogenetic tree?

Reza Hassanzadeh¹, Changiz Eslahchi², Wing-Kin Sung³

¹Department of Mathematics, Shahid Beheshti University, G.C., Tehran, Iran.

|August 1, 2014

Summary

Inferring evolutionary history using multi-labeled phylogenetic trees (MUL trees) is complex. This study finds that using only rooted triplets to construct the smallest MUL tree (SMRT problem) is biologically inappropriate, proposing new methods.

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Area of Science:

Evolutionary Biology
Phylogenetics
Computational Biology

Background:

Multi-labeled phylogenetic trees (MUL trees) model the evolutionary history of species, including polyploids.
Inferring MUL trees involves the Smallest MUL Tree Consistent with Rooted Triplets (SMRT) problem, which is NP-hard.
Existing exact algorithms for SMRT have high computational complexity (O(7n)).

Purpose of the Study:

To evaluate the biological appropriateness of the SMRT problem for inferring MUL trees.
To develop and test a heuristic algorithm for MUL tree inference.
To identify more biologically relevant problems for inferring evolutionary history.

Main Methods:

Developed a heuristic algorithm named MTRT (Minimum Triplets to Reconstruct Tree).
Applied MTRT to both real and simulated biological datasets.
Compared MTRT results with inferences based solely on rooted triplets.

Main Results:

The SMRT problem is deemed biologically inappropriate for inferring MUL trees.
The MTRT algorithm demonstrated that rooted triplets alone lack sufficient information to accurately infer MUL trees.
Inference based on minimum duplications using only triplet data is insufficient.

Conclusions:

Inferring MUL trees solely from rooted triplets is biologically unsuitable.
The MTRT heuristic provides insights into the limitations of triplet-based inference.
New, biologically relevant problems for MUL tree inference are proposed.