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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.
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Related Experiment Video

Updated: Jun 25, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Practical Guidance and Workflows for Identifying Fast Evolving Non-Coding Genomic Elements Using PhyloAcc.

Gregg W C Thomas¹, Patrick Gemmell^2,3, Subir B Shakya^1,2,4

¹Informatics Group, Harvard University, Cambridge, MA 02138, USA.

Integrative and Comparative Biology

|May 30, 2024

Summary

This summary is machine-generated.

This study introduces PhyloAcc, a Bayesian framework for analyzing genome evolution. PhyloAcc links molecular evolution rates with phenotypic traits across phylogenetic trees, aiding in the discovery of evolutionary associations.

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Area of Science:

Evolutionary Biology
Genomics
Computational Biology

Background:

Comparative genomics enables the study of genome evolution and its link to phenotypic traits.
Phylogenetic analyses can estimate molecular evolution rates and associate them with extant species' phenotypes, like convergent traits.
Identifying genomic changes associated with phenotypic traits requires robust analytical pipelines.

Purpose of the Study:

To introduce PhyloAcc, a Bayesian framework for estimating nucleotide substitution rates on phylogenetic tree branches.
To evaluate the association between molecular evolution rates and phenotypic traits.
To provide practical guidance on data preparation and running PhyloAcc models.

Main Methods:

Development of PhyloAcc, a Bayesian framework incorporating models like PhyloAcc-ST, PhyloAcc-GT, and PhyloAcc-C.
PhyloAcc-ST and PhyloAcc-GT identify loci with accelerated evolution in targeted lineages, considering single or multiple species tree topologies.
PhyloAcc-C models molecular and continuous trait evolution rates simultaneously to assess their association.

Main Results:

PhyloAcc provides a flexible framework for comparative genomics analyses.
The models allow for the identification of genomic regions associated with specific evolutionary events or phenotypic adaptations.
The study details workflows for input data preparation and model execution.

Conclusions:

PhyloAcc offers powerful tools for investigating the relationship between molecular evolution and phenotypic traits.
These models can help uncover the genomic basis of evolutionary adaptations.
The described methods and workflows facilitate the application of PhyloAcc in evolutionary studies.