PhyloAcc-C

PhyloAcc-C links nucleotide substitution rates in conserved non-coding genomic regions to changes in continuous phenotypic traits across a phylogeny.


Key Features:

  • Input Requirements: Accepts a multiple sequence alignment of conserved elements, continuous trait data for extant species, a background phylogeny, and a specified substitution process.
  • Rate-category inference: Uses Gibbs sampling to probabilistically assign lineages to background, conserved, or accelerated rate categories.
  • Association modeling: Evaluates correlations between lineage-specific substitution rate categories and changes in the rate of continuous trait evolution.
  • Targeted sequences: Focuses on conserved non-coding genomic regions rather than protein-coding genes.

Scientific Applications:

  • Regulatory element evolution: Investigates how conserved non-coding regions contribute to gene regulation and other biological functions by linking their substitution rates to trait variation.
  • Detection of selection or acceleration: Identifies genomic regions that are likely under selective constraint or experiencing accelerated evolution in association with trait changes.
  • Mammalian trait analysis: Applied to mammalian datasets for body size and lifespan, implicating processes such as tumor suppression, telomere maintenance, p53 regulation, skeletal gene function, and sprouting angiogenesis.

Methodology:

Associates nucleotide substitution rates with changes in a continuous trait across species and uses Gibbs sampling to assign lineages to background, conserved, or accelerated rate categories, then tests whether those categories correlate with increases or decreases in the rate of trait evolution.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
library
Programming Languages:
R, C++
Added:
6/18/2024
Last Updated:
11/24/2024

Operations

Publications

Gemmell P, Sackton TB, Edwards SV, Liu JS. A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution. PLOS Computational Biology. 2024;20(4):e1011995. doi:10.1371/journal.pcbi.1011995. PMID:38656999. PMCID:PMC11078400.

PMID: 38656999
Funding: - National Human Genome Research Institute: R01HG011485 - National Institute of General Medical Sciences: R01GM152814

Links