An optimized nanopore-based bioinformatic pipeline for PKD1 variant detection: development and validation with clinical samples

Abstract Background Accurate variant detection in PKD1 , the primary causative gene of autosomal dominant polycystic kidney disease (ADPKD), requires long-range PCR (LR-PCR) strategies to selectively amplify the functional gene and circumvent interference from highly homologous pseudogenes. Oxford Nanopore Technology (ONT) offers a cost-accessible sequencing alternative to short-read platforms, with particular relevance for clinical implementation in public health systems and low-resource settings. However, ONT data are characterized by higher error rates compared to Illumina sequencing, making careful bioinformatic optimization essential for reliable variant detection. Here we describe the development and validation with clinical samples of an optimized ONT MinION-based bioinformatic pipeline for PKD1 variant detection. Results Genomic DNA was sequenced using the ONT MinION platform following LR-PCR amplification of PKD1 , and a nine-stage bioinformatic pipeline was developed through systematic, criteria-based tool selection at each stage. Validation was performed in two independent cohorts: eight samples with previously confirmed PKD1 variants, assessed for concordance with Sanger sequencing, and 26 ADPKD patients with no prior molecular diagnosis. The optimized pipeline achieved complete concordance with Sanger sequencing across all eight validation samples. Applied to 26 uncharacterized patients, it identified pathogenic or likely pathogenic PKD1 variants in 69% of cases, including six novel variants; 17 of 18 prioritized findings were confirmed by Sanger sequencing. Conclusions The proposed pipeline provides a reproducible and computationally optimized framework for PKD1 variant detection using ONT sequencing following LR-PCR amplification. Its structured development approach, demonstrated diagnostic performance, and low sequencing infrastructure cost support its applicability in genomic research and clinical settings requiring high-sensitivity analysis of large, complex genes, including public health systems in low-resource environments.