ArticleG3 (Bethesda, Md.)2019
A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Leveraging long-read assemblies and machine learning to enhance short-read transposable element detection and genotyping.Genetics · 2026Article
- A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes.Plants (Basel, Switzerland) · 2025Review
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- Towards accurate and reliable resolution of structural variants for clinical diagnosis.Genome biology · 2022Review
- Current Status of Next-Generation Sequencing Approaches for Candidate Gene Discovery in Familial Parkinson´s Disease.Frontiers in genetics · 2022Article
- Rapid divergence of the male reproductive proteins in the Drosophila dunni group and implications for postmating incompatibilities between species.G3 (Bethesda, Md.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Copy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequencing data. Here, inspired by recent applications of machine learning in genomics, we describe a method to detect duplications and deletions in short-read sequencing data. In low coverage data, machine learning appears to be more powerful in the detection of CNVs than the gold-standard methods of coverage estimation alone, and of equal power in high coverage data. We also demonstrate how replicating training sets allows a more precise detection of CNVs, even identifying novel CNVs in two genomes previously surveyed thoroughly for CNVs using long read data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.