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TE_Bench: a foundational benchmarking workflow for transposable element annotation pipelines.

Hannah P Kania1, Sierra A Seifert2, Anne D Yoder2

  • 1Department of Biology, Duke University, Durham, NC, USA. hpk4@duke.edu.

Mobile DNA
|July 16, 2026
PubMed
Summary

TE_Bench is a new workflow for evaluating transposable element (TE) annotation pipelines. It helps researchers choose the best tools for DNA sequence analysis and improve TE detection methods.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Computational methods for transposable element (TE) discovery are rapidly evolving.
  • Existing TE annotation pipelines often lack transparency, leading to inconsistent results.
  • There is a need for standardized, reproducible strategies to evaluate TE detection pipeline performance.

Purpose of the Study:

  • To develop TE_Bench, a user-friendly workflow for benchmarking TE annotation pipelines.
  • To provide a systematic approach for comparing pipeline performance using simulated or real DNA data.
  • To enable informed decisions on selecting appropriate TE annotation tools and guide future pipeline development.

Main Methods:

  • Developed TE_Bench, a Snakemake-based workflow for automating TE annotation benchmarking.
  • Incorporated options for simulating DNA sequences with artificial TEs or using user-provided real genomic data.
Keywords:
BenchmarkingGenome annotationTransposable elements

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Last Updated: Jul 17, 2026

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06:02

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

  • Quantified TE detection accuracy by comparing pipeline outputs against a reference annotation.

    • Main Results:

    • TE_Bench facilitates the comparison of TE annotation pipelines like EDTA, RepeatModeler2, and Earl Grey.
    • Benchmarking revealed that RepeatModeler2 and Earl Grey, in default configurations, outperformed EDTA.
    • Analysis using simulated data assessed the impact of TE class and nested structures on annotation quality.

    Conclusions:

    • TE_Bench offers an extensible, open-source solution for community-driven TE annotation benchmarking.
    • The workflow supports the use of simulated ground-truth data or real genomes for evaluation.
    • TE_Bench aids in selecting optimal TE annotation pipelines and identifying areas for pipeline improvement.