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Updated: May 14, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Next-generation phylogenomics.

Cheong Xin Chan¹, Mark A Ragan

¹Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD, 4072, Australia.

|January 24, 2013

Summary

This summary is machine-generated.

Next-generation sequencing generates vast genomic data, challenging traditional phylogenomics. New alignment-free methods are needed for accurate evolutionary relationship analysis.

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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

Genomics
Evolutionary Biology
Bioinformatics

Background:

Next-generation sequencing technologies enable unprecedented breadth and depth of genome data generation.
Traditional phylogenomics relies on multiple sequence alignment and tree inference, which face computational limitations with large datasets.
Next-generation data present challenges including incompleteness, errors, and biological complexities like lateral gene transfer.

Purpose of the Study:

To address the computational challenges posed by large-scale genomic datasets in phylogenomics.
To advocate for the development and adoption of next-generation phylogenomic approaches.
To highlight the limitations of current phylogenomic methods with increasing data volume and complexity.

Main Methods:

Review of current phylogenomic methodologies.
Discussion of computational scalability issues in sequence alignment and tree inference.
Introduction of the concept of alignment-free approaches for phylogenomic analysis.

Main Results:

Traditional phylogenomic methods are becoming computationally infeasible for large-scale next-generation sequencing data.
Existing methods struggle with data quality issues and biological complexities inherent in massive genomic datasets.
Alignment-free approaches are proposed as a necessary advancement for next-generation phylogenomics.

Conclusions:

Next-generation sequencing data necessitates the evolution of phylogenomic methodologies.
Alignment-free methods offer a promising solution to overcome computational bottlenecks in analyzing large genomic datasets.
Future phylogenomic studies must incorporate advanced computational strategies to fully leverage the potential of next-generation sequencing.