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

Nature Protocols (2025)Cite this article

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A Publisher Correction to this article was published on 16 October 2025

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Abstract

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.

Key points

  • NanoVar is an optimized structural variant caller for long-read sequencing data and allows repeat element annotation of non-reference insertion variants.

  • This protocol provides researchers with a comprehensive structural variation detection guide from quality assessment of raw data to downstream analysis.

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Fig. 1: Types of SVs.
Fig. 2: A metro map depicting the workflow for SV detection using LRS data.
Fig. 3: An example of NanoVar’s HTML output file.
Fig. 4: An example of the output of NanoPlot for two tumor samples.
Fig. 5: Distribution of SV types.
Fig. 6: Distribution of repeat classes.
Fig. 7: Annotation of somatic SVs by the Ensembl VEP.

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Data availability

The example dataset used in this protocol is from a published research article74 and is available at the Genome Sequence Archive for Human under accession number HRA002638. The protocol output directories are available on Zenodo at https://doi.org/10.5281/zenodo.1658352347.

Code availability

NanoVar and NanoINSight are publicly available at https://github.com/benoukraflab.

Change history

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Acknowledgements

The authors thank H. Alloway for her proof reading as well as the Centre for Analytics, Informatics and Research (CAIR) at Memorial University and the Digital Research Alliance of Canada (the Alliance) in partnership with ACENET, for their support in providing high-performance computing resources that facilitated the development and testing of our protocol.

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Authors and Affiliations

  1. Division of BioMedical Sciences, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada

    Asmaa Samy, Matthew Dyer & Touati Benoukraf

  2. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Cheng Yong Tham & Touati Benoukraf

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  1. Asmaa Samy
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  2. Cheng Yong Tham
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Contributions

A.S. and C.Y.T. conceived and designed the protocol. M.D. tested the protocol and reported issues. A.S. and T.B. wrote the manuscript. T.B. supervised the design of the protocol.

Corresponding author

Correspondence to Touati Benoukraf.

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The authors declare no competing interests.

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Nature Protocols thanks Andrew Beggs and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key reference

Tham, C.Y. et al. Genome Biol. 21, 56 (2020): https://doi.org/10.1186/s13059-020-01968-7

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Samy, A., Tham, C.Y., Dyer, M. et al. NanoVar: a comprehensive workflow for structural variant detection to uncover the genome’s hidden patterns. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01270-5

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