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Computational tools for tandem repeat detection using long-read sequencing.

Qian Liu1,2, Jincheng Li1

  • 1Nevada Institute of Personalized Medicine, College of Sciences, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, United States.

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|February 5, 2026
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Summary
This summary is machine-generated.

This review surveys 39 computational tools for detecting tandem repeats (TRs) using long-read sequencing. It highlights their strengths, limitations, and future directions for advancing TR detection in genomics and disease research.

Keywords:
computational toolslong-read sequencingtandem repeats

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Tandem repeats (TRs) are crucial for biological functions, but their expansions cause over 60 human diseases.
  • Short-read sequencing struggles with accurate detection of long TR regions.
  • Long-read sequencing offers improved accuracy for genome-wide TR detection.

Purpose of the Study:

  • To systematically review and categorize computational tools for TR detection from long-read data.
  • To analyze the strengths and limitations of existing TR detection methodologies.
  • To identify current challenges and future research directions in the field.

Main Methods:

  • Systematic literature review and categorization of 39 computational tools.
  • Analysis of tool capabilities for TR detection, visualization, and functional interpretation.
  • Evaluation of tool performance specifically for long-read sequencing data.

Main Results:

  • A comprehensive catalog of 39 TR detection tools for long-read sequencing is presented.
  • Key strengths and limitations of each tool are discussed.
  • Current challenges in long-read TR detection are identified, including accuracy and scalability.

Conclusions:

  • Long-read sequencing is essential for accurate TR detection and understanding their role in health and disease.
  • Further development of computational tools is needed to overcome existing limitations.
  • Advancements in long-read TR detection will improve insights into genetic variation and disease mechanisms.