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RERconverge Update: Runtime Reduction and Analysis Function Overhaul
Biorxiv : the Preprint Server for Biology
|June 22, 2026
Summary
RERconverge, an R package for analyzing convergent evolution, now offers faster, scalable algorithms for large genomic datasets. This update enhances the ability to link gene evolutionary rates to specific traits and selective pressures.
Area of Science:
- Evolutionary biology
- Genomics
- Bioinformatics
Background:
- Convergent evolution studies genomic changes underlying similar traits in different species.
- RERconverge identifies genomic regions associated with phenotypes by analyzing gene relative evolutionary rates (RERs).
Purpose of the Study:
- To update the RERconverge R package with improved algorithms and new analytical features.
- To enhance the package's capability to process larger genomic datasets and analyze evolutionary rates.
Main Methods:
- The RERconverge pipeline was optimized for speed and scalability.
- New customizable analysis options and statistical methods for outlier control were implemented.
Main Results:
- Core functions were significantly accelerated, achieving speedups of up to 28.6x.
- The package now supports larger genomic datasets and offers expanded analytical methods for phenotype-RER association testing.
Conclusions:
- The updated RERconverge package provides a more efficient and powerful tool for studying the genomic basis of convergent evolution.
- Enhanced scalability and analytical options facilitate research with increasingly large genomic datasets.
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