Abstract
Paralogous genes challenge short-read sequencing (SRS) due to high sequence similarity. Although long-read sequencing (LRS) improves resolution, the extent to which it resolves paralogous genes remains unclear. This study evaluates the capability of LRS by integrating in silico mappability-based predictions with clinical data to generate SRS- and LRS-unresolved gene lists, and by assessing whether a paralog-specific phasing, Paraphase, can overcome remaining limitations. Mappability was simulated across read lengths (250 bp to 14 kb) to predict unresolved regions and validated against mapping quality (MQ) from 66 high-fidelity LRS samples. Paraphase was applied to 79 paralog groups. Among 645 medically relevant (MR) genes unresolved by SRS, 419 (65.0%) were predicted to be resolved by LRS, while 226 (35.0%) remained unresolved. These predictions correlated with clinical MQ (χ² = 92.43, p < 2.2 × 10−16; κ = 0.37), with significant differences between LRS-resolved and LRS-unresolved MR genes (W = 63,656, p < 2.2 × 10−16; r = 0.36). Paraphase resolved 61 groups (77.2%), providing additional resolution beyond LRS. LRS improves paralogous gene resolution but cannot fully eliminate paralog blind spots. Curated gene lists define boundaries of LRS utility for clinical interpretation, while Paraphase adds complementary resolution, supporting an integrated framework combining predictive modeling with algorithmic strategies.
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Data availability
The GRCh38 reference genome FASTA file used for mappability analysis is available from the UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/). The GRCh38 version with excluded alternate contigs was downloaded from the Pacific Biosciences GitHub repository (https://github.com/PacificBiosciences/reference_genomes). The GEM library tool was downloaded from SourceForge (https://sourceforge.net/projects/gemlibrary/files/). The code for Paraphase is available on GitHub (https://github.com/PacificBiosciences/paraphase). Owing to ethical constraints and the sensitive nature of clinical genomic data, full BAM files from patients cannot be made publicly available. The data are available following review and approval by the corresponding author’s institution and under appropriate data use agreements upon request.
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Acknowledgements
This work was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2025-02263889).
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Conceptualization: S.K., J.L, and M.S.; Data curation: H.S., H.L., H.I., and S.C.; Formal analysis: S.K. and J.J.; Methodology: S.K., J.J., Y.K., J.L., and M.S.; Supervision: J.L. and M.S.; Visualization: S.K.; Writing—original draft: S.K.; Writing—review and editing: all authors. All authors reviewed and approved the final manuscript.
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Kim, S.K., Jang, J., Kim, Y. et al. Integrative analysis of in silico predictions and clinical evidence to delineate the capability of HiFi long-read sequencing in paralogous genes. npj Genom. Med. (2026). https://doi.org/10.1038/s41525-026-00555-2
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DOI: https://doi.org/10.1038/s41525-026-00555-2
