ArticleAmerican journal of human genetics2024
Detection of elusive DNA copy-number variations in hereditary disease and cancer through the use of noncoding and off-target sequencing reads.
Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Search for the most precise diagnosis of monogenic diabetes - the usefulness of short-read NGS method in molecular testing in Polish patients.Acta diabetologica · 2026Article
- PScnv: personalized self-normalizing CNV detection with a hierarchical multi-phase framework.Bioinformatics (Oxford, England) · 2026Article
- Whole exome sequencing: a new era in prenatal diagnostics.Journal of translational medicine · 2026Article
- GermlineCurrent oncology (Toronto, Ont.) · 2025Article
- Combined genomics and proteomics unveils elusive variants and vast aetiologic heterogeneity in dystonia.Brain : a journal of neurology · 2025Article
- A comprehensive genetic landscape of inherited retinal diseases in a large Pakistani cohort.NPJ genomic medicine · 2025Article
- Bi-allelic variants in three genes encoding distinct subunits of the vesicular AP-5 complex cause hereditary macular dystrophy.American journal of human genetics · 2025Article
- Substitution of a single non-coding nucleotide upstream of TMEM216 causes non-syndromic retinitis pigmentosa and is associated with reduced TMEM216 expression.American journal of human genetics · 2024Article
- Limited Added Diagnostic Value of Whole Genome Sequencing in Genetic Testing of Inherited Retinal Diseases in a Swiss Patient Cohort.International journal of molecular sciences · 2024Article
- The Clinical and Genetic Landscape of Hereditary Cancer: Experience from a Single Clinical Diagnostic Laboratory.Cancer genomics & proteomicsArticle
Corrections and comments
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Authors and funding
27 authors at 9 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Copy-number variants (CNVs) play a substantial role in the molecular pathogenesis of hereditary disease and cancer, as well as in normal human interindividual variation. However, they are still rather difficult to identify in mainstream sequencing projects, especially involving exome sequencing, because they often occur in DNA regions that are not targeted for analysis. To overcome this problem, we developed OFF-PEAK, a user-friendly CNV detection tool that builds on a denoising approach and the use of "off-target" DNA reads, which are usually discarded by sequencing pipelines. We benchmarked OFF-PEAK on data from targeted sequencing of 96 cancer samples, as well as 130 exomes of individuals with inherited retinal disease from three different populations. For both sets of data, OFF-PEAK demonstrated excellent performance (>95% sensitivity and >80% specificity vs. experimental validation) in detecting CNVs from in silico data alone, indicating its immediate applicability to molecular diagnosis and genetic research.
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