ParallelEvolCCM: Quantifying Coevolutionary Patterns Among Genomic Features

Concerted gains and losses of genomic features such as genes and mobile genetic elements can provide key clues into related functional roles and shared evolutionary trajectories. By capturing phylogenetic signals, a coevolutionary model can outperform comparative methods based on shared presence and absence of features. We previously developed the Community Coevolution Model, which represents the gain/loss probability of each feature as a combination of its own intrinsic rate, combined with the joint probabilities of gain and loss with all other features. Originally implemented as an R library, we have now developed an R wrapper that adds parallelization and several options to pre-filter the features to increase the efficiency of comparisons. Here we describe the functionality of ParallelEvolCCM and apply it to a dataset of 1000 genomes of the genus Bifidobacterium. ParallelEvolCCM is released under the MIT license and available at https://github.com/beiko-lab/arete/blob/master/bin/ParallelEvolCCM.R.

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Publication Details

Journal
Genome Biology and Evolution
Published
2025-05-17
DOI
https://doi.org/10.1093/gbe/evaf092
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

ParallelEvolCCM: Quantifying Coevolutionary Patterns Among Genomic Features

Chaoyue Liu, Robert G. Beiko, Ryan C. Fink, João Vitor Cavalcante
Genome Biology and Evolution
Bioinformatics and Genomic Networks
article

ParallelEvolCCM: Quantifying Coevolutionary Patterns Among Genomic Features

Chaoyue Liu, Robert G. Beiko, Ryan C. Fink, João Vitor Cavalcante
article en

Abstract

Concerted gains and losses of genomic features such as genes and mobile genetic elements can provide key clues into related functional roles and shared evolutionary trajectories. By capturing phylogenetic signals, a coevolutionary model can outperform comparative methods based on shared presence and absence of features. We previously developed the Community Coevolution Model, which represents the gain/loss probability of each feature as a combination of its own intrinsic rate, combined with the joint probabilities of gain and loss with all other features. Originally implemented as an R library, we have now developed an R wrapper that adds parallelization and several options to pre-filter the features to increase the efficiency of comparisons. Here we describe the functionality of ParallelEvolCCM and apply it to a dataset of 1000 genomes of the genus Bifidobacterium. ParallelEvolCCM is released under the MIT license and available at https://github.com/beiko-lab/arete/blob/master/bin/ParallelEvolCCM.R.

Genome Biology and EvolutionVol. 17(6)
Dalhousie University (CA), Universidade Federal do Rio Grande do Norte (BR)
Genome Canada, Dalhousie University, Genome Atlantic, Research Nova Scotia, Alliance de recherche numérique du Canada, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 90%
Bioinformatics and Genomic Networks
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