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Updated: Nov 22, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

ENJ algorithm can construct triple phylogenetic trees.

Yan Hong¹, Maozu Guo^2,3, Juan Wang^1,4

¹School of Computer Science, Inner Mongolia University, Hohhot 010021, P.R. China.

Molecular Therapy. Nucleic Acids

|January 11, 2021

Summary

This summary is machine-generated.

Extended Neighbor Joining (ENJ) improves phylogenetic tree construction by joining multiple nodes simultaneously, offering faster, more accurate evolutionary analyses. This method reveals closer ties between COVID-19 and SARS-CoV, suggesting reticulate evolution in coronaviruses.

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Last Updated: Nov 22, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

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Published on: February 5, 2014

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Area of Science:

Phylogenetics and Evolutionary Biology
Bioinformatics
Computational Biology

Background:

Phylogenetic trees visualize species evolution based on biological sequence data.
Neighbor Joining (NJ) is a common algorithm for phylogenetic tree construction, known for speed and accuracy.
NJ can produce different trees ('tied trees') from the same data due to input order variations.

Purpose of the Study:

To introduce an improved Neighbor Joining algorithm, Extended Neighbor Joining (ENJ).
To enable ENJ to construct triple phylogenetic trees by joining multiple nodes with minimum distance.
To enhance the accuracy and speed of phylogenetic tree construction.

Main Methods:

Developed the Extended Neighbor Joining (ENJ) algorithm, allowing simultaneous joining of up to three nodes.
Derived formulas for updating distance values and calculating branch lengths within the ENJ framework.
Tested ENJ using simulated and real biological datasets, including coronaviruses.

Main Results:

ENJ constructs phylogenetic trees with greater similarity to initial datasets compared to standard NJ.
ENJ demonstrates significantly faster computation times than the NJ algorithm.
Phylogenetic analysis of coronaviruses using ENJ indicates a closer relationship between COVID-19 and SARS-CoV.

Conclusions:

ENJ offers a faster and more accurate method for phylogenetic tree construction, addressing limitations of the NJ algorithm.
The ENJ analysis of coronaviruses suggests a reticulate evolutionary pattern, differing from previous tree-based interpretations.
ENJ provides a valuable tool for evolutionary studies, particularly for complex datasets like viral evolution.