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Phylogenetic Trees

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

Phylogeny

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Updated: Jan 10, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Efficient Bayesian Phylogenetics under the Infinite Sites Model.

Ivan Specht¹, Julia A Palacios^1,2,3

¹Institute for Computational and Mathematical Engineering, Stanford University, 475 Via Ortega, Stanford, CA 94305.

Biorxiv : the Preprint Server for Biology

|November 26, 2025

Summary

This summary is machine-generated.

We developed inPhynite, a fast Bayesian phylogenetics algorithm for large genomic datasets. It significantly improves computational efficiency for inferring evolutionary history and population sizes without sacrificing accuracy.

Keywords:

Bayesian animalia coalescent effective population size infinite sites phylogenetics

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Last Updated: Jan 10, 2026

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

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Published on: July 11, 2025

Area of Science:

Computational Biology
Evolutionary Genetics
Bioinformatics

Background:

Bayesian phylogenetic inference is crucial for understanding population evolutionary history.
Current phylogenetic tools struggle with scalability for large genomic datasets.

Purpose of the Study:

Introduce inPhynite, a novel, highly-efficient Bayesian phylogenetics algorithm.
Address the computational limitations of existing phylogenetic inference methods for large-scale data.

Main Methods:

Developed inPhynite, an algorithm compatible with the infinite sites mutation model.
Designed an efficient Markov chain for sampling mutations and coalescences.
Modeled effective population size trajectories as piecewise constant functions.

Main Results:

inPhynite achieves a speedup of over 225 times in statistical efficiency on large datasets.
The algorithm demonstrates comparable accuracy to existing methods.
Successfully applied inPhynite to infer human population evolutionary history using mitochondrial DNA.

Conclusions:

inPhynite offers a significant advancement in computational efficiency for Bayesian phylogenetics.
The infinite sites mutation model simplifies likelihood calculations, enabling faster inference.
inPhynite is a valuable tool for analyzing large genomic datasets and reconstructing population evolutionary dynamics.