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EvANI benchmarking workflow for evolutionary distance estimation.

Sina Majidian1, Stephen Hwang2, Mohsen Zakeri1

  • 1Department of Computer Science, Johns Hopkins University, 3400 North Charles St., Baltimore, MD 21218, United States.

Briefings in Bioinformatics
|June 12, 2025
PubMed
Summary
This summary is machine-generated.

We developed EvANI, a framework for evaluating genome comparison methods. K-mer based approaches offer efficient and accurate estimations of average nucleotide identity (ANI), crucial for understanding evolutionary relationships.

Keywords:
BLASTaverage nucleotide identityevolutiongenomek-mersketching

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Area of Science:

  • Genomics and Evolutionary Biology
  • Bioinformatics and Computational Biology

Background:

  • Advances in long-read sequencing yield high-quality genome assemblies, enabling comparative genomics across the Tree of Life.
  • Average nucleotide identity (ANI) quantifies genetic similarity between genomes, aiding species delineation and phylogenetic analysis.
  • Traditional ANI calculation via genome alignment is computationally intensive, prompting the development of faster, sketch-based methods.

Purpose of the Study:

  • To introduce EvANI, a novel evaluation framework for assessing the accuracy and efficiency of different average nucleotide identity (ANI) estimation algorithms.
  • To analyze the impact of assumptions and heuristics in sketch-based ANI methods on distance estimations.
  • To guide the selection of appropriate ANI estimation strategies for diverse genomic datasets and evolutionary studies.

Main Methods:

  • Development of a benchmark dataset comprising simulated and real genomic data for rigorous evaluation.
  • Implementation of a rank-correlation-based metric to quantify the accuracy of ANI estimates against evolutionary distances.
  • Comparative analysis of various ANI estimation algorithms, including alignment-based (ANIb) and k-mer-based approaches.

Main Results:

  • ANIb provides the most accurate tree distance estimation but is computationally inefficient.
  • K-mer-based methods demonstrate high efficiency and consistent accuracy across various datasets.
  • Optimal k-mer lengths can vary by clade, suggesting the utility of multiple k-mer values (e.g., k=10 and k=19 for Chlamydiales).
  • Maximal exact match approaches offer a balance between computational efficiency and accuracy.

Conclusions:

  • EvANI serves as a robust framework for evaluating genome comparison tools.
  • K-mer-based strategies are highly effective for rapid and accurate ANI estimation in large-scale genomic comparisons.
  • The choice of k-mer length and the consideration of alternative methods like maximal exact matches are important for optimizing phylogenetic analyses.