Journal Article DZNE-2026-00918

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CNV-Finder: streamlining copy number variation discovery.

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2026
Oxford University Press Oxford

Bioinformatics advances 6(1), vbag205 () [10.1093/bioadv/vbag205]

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Abstract: Copy Number Variations (CNVs) play pivotal roles in complex disease etiology, often requiring large sample sizes to analyze disease associations. While genotyping arrays offer a cost-effective approach for CNV detection using Log R Ratio (LRR) and B Allele Frequency (BAF) signals, existing independent array-based callers suffer from high false positive rates and noise susceptibility, burdening manual validation.We present CNV-Finder, a deep learning pipeline employing Long Short-Term Memory (LSTM) networks for large-scale CNV identification within user-defined genomic regions. Trained on expert-annotated samples from the Global Parkinson's Genetics Program across four neurodegenerative disease-associated genes (PRKN, LINGO2, MAPT, SNCA), CNV-Finder integrates human feedback to iteratively improve performance. In benchmarking across 105 936 samples spanning 11 ancestries and nearly 150 cohorts, the model achieved 91% and 89% visual confirmation rates for PRKN deletions and duplications at high-confidence thresholds. In two validation cohorts, CNV-Finder nominated 83% fewer candidates than a popular Hidden Markov Model-based caller while maintaining higher confirmation rates. Validation through MLPA, short-read, and long-read sequencing demonstrated robust performance, generalizing to diverse signatures including homozygous deletions and SNCA triplications absent from training. Our findings highlight human expertise's value in complex loci like 17q21.31.CNV-Finder is freely available at https://github.com/nvk23/CNV-Finder.

Classification:

Contributing Institute(s):
  1. Parkinson Genetics (AG Gasser)
Research Program(s):
  1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2026
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 Record created 2026-09-02, last modified 2026-09-14