ArticleIntegrative and comparative biology2024
Practical Guidance and Workflows for Identifying Fast Evolving Non-Coding Genomic Elements Using PhyloAcc.
Article in Integrative and comparative biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- CNEwrap: a scalable toolkit with a novel algorithm for large-scale genome-wide accelerated conserved non-coding elements detection.Nucleic acids research · 2026Article
- The genetic foundations of convergent traits.Nature reviews. Genetics · 2026Review
- From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions.Genome biology and evolution · 2025Review
- Convergent evolution of noncoding elements associated with short tarsus length in birds.BMC biology · 2025Article
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7 authors.
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Abstract
Comparative genomics provides ample ways to study genome evolution and its relationship to phenotypic traits. By developing and testing alternate models of evolution throughout a phylogeny, one can estimate rates of molecular evolution along different lineages in a phylogeny and link these rates with observations in extant species, such as convergent phenotypes. Pipelines for such work can help identify when and where genomic changes may be associated with, or possibly influence, phenotypic traits. We recently developed a set of models called PhyloAcc, using a Bayesian framework to estimate rates of nucleotide substitution on different branches of a phylogenetic tree and evaluate their association with pre-defined or estimated phenotypic traits. PhyloAcc-ST and PhyloAcc-GT both allow users to define a priori a set of target lineages and then compare different models to identify loci accelerating in one or more target lineages. Whereas ST considers only one species tree across all input loci, GT considers alternate topologies for every locus. PhyloAcc-C simultaneously models molecular rates and rates of continuous trait evolution, allowing the user to ask whether the two are associated. Here, we describe these models and provide tips and workflows on how to prepare the input data and run PhyloAcc.
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