BalLeRMix +: mixture model approaches for robust joint identification of both positive selection and long-term balancing selection.
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ABSTRACT: Summary
The growing availability of genomewide polymorphism data has fueled interest in detecting diverse selective processes affecting population diversity. However, no model-based approaches exist to jointly detect and distinguish the two complementary processes of balancing and positive selection. We extend the BalLeRMix B-statistic framework described in Cheng and DeGiorgio (2020) for detecting balancing selection and present BalLeRMix+, which implements five B statistic extensions based on mixture models to robustly identify both types of selection. BalLeRMix+ is implemented in Python and computes the composite likelihood ratios and associated model parameters for each genomic test position.Availability and implementation
BalLeRMix+ is freely available at https://github.com/bioXiaoheng/BallerMixPlus.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Cheng X
PROVIDER: S-EPMC8756184 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
Publications
BalLeRMix +: mixture model approaches for robust joint identification of both positive selection and long-term balancing selection.
Cheng Xiaoheng X DeGiorgio Michael M
Bioinformatics (Oxford, England) 20220101 3
<h4>Summary</h4>The growing availability of genomewide polymorphism data has fueled interest in detecting diverse selective processes affecting population diversity. However, no model-based approaches exist to jointly detect and distinguish the two complementary processes of balancing and positive selection. We extend the BalLeRMix B-statistic framework described in Cheng and DeGiorgio (2020) for detecting balancing selection and present BalLeRMix+, which implements five B statistic extensions ...[more]
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