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A REsampling and Visual EvALuation Method to Detect and Map Local Model Violations During Biomolecular Sequence

Meijun Gao¹, Kevin J Liu^1,2,3

¹Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|February 20, 2026

Summary

This summary is machine-generated.

REVEAL is a new statistical framework that detects and pinpoints where evolutionary models mis-specify biomolecular sequence data. This tool improves phylogenetic and phylogenomic analyses by identifying localized model violations.

Keywords:

biomolecular sequence analysis model mis-specification phylogenetic estimation statistical resampling

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Area of Science:

Evolutionary biology
Bioinformatics
Computational biology

Background:

Phylogenetics and phylogenomics assume a single evolutionary model for all sequence sites.
This assumption is often violated by evolutionary process heterogeneity, causing local model mis-specification and inference bias.
Existing methods for addressing this issue have limitations in generalizability and model assumptions.

Purpose of the Study:

To introduce REVEAL (REsampling and Visual EvALuation), a general statistical framework.
To detect and localize model mis-specification in biomolecular sequence data without adding new assumptions.
To provide a flexible and effective tool for evaluating model adequacy in phylogenetic analyses.

Main Methods:

REVEAL employs sequence-aware statistical resampling.
It constructs a local support matrix along the sequence alignment.
This facilitates the identification of site-level model violations.

Main Results:

REVEAL demonstrates robust control of type I and type II errors in simulations.
Achieves precision >90% and recall >85% across diverse evolutionary scenarios.
Successfully identified localized model violations in mouse and mosquito genomic data.

Conclusions:

REVEAL is a general-purpose framework for detecting model mis-specification in sequence data.
It enhances the reliability of phylogenetic and phylogenomic inferences.
The tool is effective across various evolutionary contexts and dataset sizes.