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Updated: Jun 28, 2026
09:34
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Near-perfect genome sequencing in medical genetics
Quentin Sabbagh1,2, Christian Gilissen1, Helger G Yntema1
1Department of Human Genetics and Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands.
Nature Genetics
|June 26, 2026
Summary
Near-perfect genome sequencing (NPGS) using long-read technology offers a unified approach to overcome limitations of current genetic diagnostics. This advanced method improves variant interpretation and clinical utility across various settings.
Area of Science:
- Genomics
- Medical Genetics
- Bioinformatics
Background:
- Current medical genetics relies on short-read sequencing, facing limitations in detecting complex genomic regions and structural variants.
- These limitations hinder accurate diagnosis and interpretation of variants of uncertain significance.
Purpose of the Study:
- To propose near-perfect genome sequencing (NPGS) as a foundational technology for next-generation genetic diagnostics.
- To introduce a Bayesian framework integrating genomic completeness for variant interpretation.
Main Methods:
- Leveraging long-read genome sequencing for diploid genome assembly.
- Utilizing pangenome references and artificial intelligence for variant interpretation.
- Developing a Bayesian framework for variant classification based on genomic completeness.
Main Results:
- NPGS addresses blind spots of short-read sequencing, including homologies, repeats, and structural variants.
- Genomic completeness provides interpretive evidence, aiding in the classification of variants of uncertain significance.
- Potential applications identified in postnatal, prenatal, and oncological diagnostics.
Conclusions:
- NPGS represents a significant advancement toward a unified 'one-test' paradigm in genetic diagnostics.
- Implementation requires addressing challenges in cost, computational resources, equity, and ethics.
- This approach promises to enhance diagnostic accuracy and clinical decision-making.
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