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A detailed guide to assessing genome assembly based on long-read sequencing data using Inspector

Nature Protocols volume 20, pages 2845–2864 (2025)Cite this article

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Abstract

Long-read sequencing technologies yield extended DNA sequences capable of spanning intricate, repetitive genome regions, thereby facilitating the generation of more precise and comprehensive genome assemblies. However, assembly errors are inevitable owing to inherent genomic complexity and limitations of sequencing technology and assembly algorithms, making assembly evaluation crucial. The genome assembly evaluation tool Inspector presents several advantages over existing long-read de novo assembly evaluation tools, including (1) both reference-free and reference-guided assembly evaluation; (2) the ability to detect both small- and large-scale structural errors; (3) the option of assembly error correction, which can improve the quality value of the original assembly; and (4) the ability to perform haplotype-resolved assembly evaluation. Inspector can provide not only basic contig and alignment statistics, but also the precise locations and types of the different structural errors. These advantages provide a robust framework for long-read assembly evaluation. In this Protocol, we showcase four procedures to demonstrate the different applications of Inspector for long-read assembly evaluation. Inspector software and additional guides can be found at https://github.com/ChongLab/Inspector_protocol.

Key points

  • Long-read sequencing has been instrumental in improving de novo assembly of genomes. However, genome complexity and limitations of sequencing technology and assembly algorithms necessitate comprehensive evaluation of the accuracy of the assembled genomes. Inspector is a flexible tool for reference-free or reference-guided assembly evaluation, showcased in four use-case scenarios described in this protocol.

  • Inspector identifies the types and precise locations of small- and large-scale structural errors and provides an error-correction module that can improve the quality of the original assembly.

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Fig. 1: The impact of long-read sequencing technologies on the growth of de novo assembly stuides.
Fig. 2: The workflow of Inspector.
Fig. 3: IGV visualization of structural assembly errors.

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Data availability

The dataset used in this protocol can be downloaded with the provided links.

Code availability

All code and commands used in this protocol are available as Supplementary Data. Additionally, the code and commands used for this protocol can be found at https://github.com/ChongLab/Inspector_protocol. The original code for Inspector is hosted at https://github.com/ChongLab/Inspector.

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Acknowledgements

This study was supported by the Department of Biomedical Informatics and Data Science, Marnix E. Heersink School of Medicine, University of Alabama at Birmingham, and the Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami. Y.G. was supported by P30CA240139, R01ES030993 and R01ES035421 from NIH, USA Z.C. was supported by the MIRA award (1R35GM138212) from NIH/NIGMS.

Author information

Author notes

  1. These authors contributed equally: Yan Guo, Yuwei Song.

Authors and Affiliations

  1. Department of Public Health and Sciences, University of Miami, Miami, FL, USA

    Yan Guo, Limin Jiang & Michele Ceccarelli

  2. Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama, Birmingham, AL, USA

    Yuwei Song, Yu Chen, Min Gao & Zechen Chong

  3. HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA

    Zechen Chong

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  1. Yan Guo
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  4. Yu Chen
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Contributions

Y.G. and Z.C. conceived and managed the project. Y.S. collected all the datasets and performed all the analyses. L.J., Y.C., M.C. and M.G. were involved in testing and evaluating the tool. Y.G., Y.S., L.J. and Z.C. prepared the figures and tables, wrote the manuscript draft and revised the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Yan Guo or Zechen Chong.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

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Key reference

Chen, Y. et al. Genome Biol. 22, 312 (2021): https://doi.org/10.1186/s13059-021-02527-4

Supplementary information

Supplementary Tables 1–3

Supplementary Tables 1–3.

Supplementary Code 1

Source codes for the entire protocol.

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Guo, Y., Song, Y., Jiang, L. et al. A detailed guide to assessing genome assembly based on long-read sequencing data using Inspector. Nat Protoc 20, 2845–2864 (2025). https://doi.org/10.1038/s41596-025-01149-5

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