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<p>Structural variants (SVs) contribute significantly to genomic diversity and disease predisposition as well as development in diverse species. However, their accurate characterization has remained a challenge because of their complexity and size. With the rise of third-generation sequencing technology, analytical strategies to map SVs have been revisited, and software such as NanoVar, a free and open-source package designed for efficient and reliable SV detection in long-read sequencing data, has facilitated their studies. NanoVar has been shown to work effectively in various published genomic studies, including research on genetic disorders, population genomics and genome analysis of non-model organisms. In this article, we describe in detail all the steps of the NanoVar protocol and its interplay with other platforms for SV calling in whole-genome long-read sequencing data such that researchers with minimal experience with command-line interfaces can easily carry out the protocol. It also provides exhaustive instructions for diverse study designs, including single-sample analyses, cohort studies and genome instability analyses. Finally, the protocol covers SV visualization, filtering and annotation details. Overall, users can identify and analyze SVs in a typical human dataset with a conventional computational setup in ~2–5 h after read mapping.</p>

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NanoVar: a comprehensive workflow for structural variant detection to uncover the genome’s hidden patterns

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Asmaa Samy,
  • Cheng Yong Tham,
  • Matthew Dyer,
  • Touati Benoukraf

摘要

Structural variants (SVs) contribute significantly to genomic diversity and disease predisposition as well as development in diverse species. However, their accurate characterization has remained a challenge because of their complexity and size. With the rise of third-generation sequencing technology, analytical strategies to map SVs have been revisited, and software such as NanoVar, a free and open-source package designed for efficient and reliable SV detection in long-read sequencing data, has facilitated their studies. NanoVar has been shown to work effectively in various published genomic studies, including research on genetic disorders, population genomics and genome analysis of non-model organisms. In this article, we describe in detail all the steps of the NanoVar protocol and its interplay with other platforms for SV calling in whole-genome long-read sequencing data such that researchers with minimal experience with command-line interfaces can easily carry out the protocol. It also provides exhaustive instructions for diverse study designs, including single-sample analyses, cohort studies and genome instability analyses. Finally, the protocol covers SV visualization, filtering and annotation details. Overall, users can identify and analyze SVs in a typical human dataset with a conventional computational setup in ~2–5 h after read mapping.