# Prediction of local convergent shifts in evolutionary rates with phyloConverge

## TL;DR

A new method called phyloConverge improves the detection of convergent genetic changes in noncoding regions, helping to understand how species adapt to similar environments.

## Contribution

phyloConverge introduces a scalable and flexible phylogenetic approach for local convergence analysis of genomic elements.

## Key findings

- phyloConverge identifies rate-accelerated conserved noncoding elements (CNEs) with high specificity and statistical robustness.
- Regulatory element regression in subterranean mammals is enriched for neuronal phenotypes and developmental processes.
- phyloConverge enables high-resolution genome-wide convergence analysis with modular adaptation detection.

## Abstract

Convergence analysis can characterize genetic elements underlying morphological adaptations. However, its performance on regulatory elements is limited due to their modular composition of transcription factor motifs, which have rapid turnover and experience different evolutionary pressures.

We introduce phyloConverge, a phylogenetic method that performs scalable, fine-grained local convergence analysis of genomic elements at flexible length scales. Using a benchmarking case of convergent subterranean mammal adaptation, phyloConverge identifies rate-accelerated conserved noncoding elements (CNEs) with high specificity and statistical robustness relative to competing methods. From CNE-level scoring, we detect the convergent regression of entire CNE units and highlight the contrast that subterranean-associated coding region regression is highly specific to ocular functions, whereas regulatory element regression is enriched for accompanying neuronal phenotypes and other developmental processes. From transcription factor motif-level scoring, we dissect elements into subregions with uneven convergence signals and demonstrate the modular adaptation of CNEs with high functional specificity. Finally, we demonstrate phyloConverge’s scalability to perform high-resolution convergence analysis genome-wide.

phyloConverge is available at https://github.com/ECSaputra/phyloConverge