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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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.
In contrast, regions which code...
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Phylogenetic Trees03:21

Phylogenetic Trees

50.3K
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.
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Phylogeny01:23

Phylogeny

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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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Related Experiment Video

Updated: Feb 24, 2026

A Practical Guide to Phylogenetics for Nonexperts
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A Practical Guide to Phylogenetics for Nonexperts

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Efficient identification of phylogenetically informative alignment sites via sparse learning.

Carlos G Schrago1

  • 1Department of Genetics, Federal University of Rio de Janeiro, RJ, Brazil.

Molecular Phylogenetics and Evolution
|February 22, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new method using sparse learning to identify key sites in genetic data for accurate evolutionary tree reconstruction. This approach efficiently pinpoints phylogenetically informative sites, improving phylogenomic analyses.

Keywords:
Alignment trimmingIndelsLasso regressionMarker selectionPhylogenetic informativenessSparse learning

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Area of Science:

  • Phylogenetics and Evolutionary Biology
  • Computational Biology
  • Genomics

Background:

  • Accurate phylogenetic tree reconstruction relies on identifying phylogenetically informative sites in multiple sequence alignments.
  • Current methods often depend on predefined topologies or heuristics, limiting their applicability and interpretability.

Purpose of the Study:

  • To develop a topology-agnostic framework for quantifying site-wise phylogenetic information.
  • To identify the minimal subset of sites crucial for phylogenetic signal using sparse learning.

Main Methods:

  • Employed sparse learning via Lasso (Least Absolute Shrinkage and Selection Operator) regression.
  • Modeled site log-likelihoods as predictors of tree likelihood across random topologies.
  • Validated using simulated and empirical mammalian datasets.

Main Results:

  • Lasso-selected sites produced tree topologies nearly identical to those from full alignments.
  • An entropy-based proxy effectively approximated Lasso results for computational efficiency.
  • Demonstrated the identification of a minimal subset of phylogenetically informative sites.

Conclusions:

  • Sparse learning offers a principled, scalable, and practical method for assessing and optimizing phylogenetic data.
  • The developed framework provides an objective metric for phylogenetically informative sites.
  • This approach enhances efficiency and accuracy in phylogenomic analyses.