ArticleBMC bioinformatics2019
DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Dual-SVF: a robust knowledge-guided multimodal method for structural variant filtering in long-read sequencing.Briefings in bioinformatics · 2026Article
- Unravelling the genetic architecture of cardiovascular disease through structural variant detection with whole-genome sequencing.Frontiers in genetics · 2026Review
- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in Normal Human Tissue Using scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.Briefings in bioinformatics · 2025Article
- Review
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Genome analysis through image processing with deep learning models.Journal of human genetics · 2024Review
- NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.Bioinformatics (Oxford, England) · 2024Article
- SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data.BMC bioinformatics · 2023Article
- cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network.BMC bioinformatics · 2023Article
- INSnet: a method for detecting insertions based on deep learning network.BMC bioinformatics · 2023Article
- LSnet: detecting and genotyping deletions using deep learning network.Frontiers in genetics · 2023Article
- Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice.Biomedicines · 2022Review
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- Towards accurate and reliable resolution of structural variants for clinical diagnosis.Genome biology · 2022Review
- Progress in Methods for Copy Number Variation Profiling.International journal of molecular sciences · 2022Review
- From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.Computational and structural biotechnology journal · 2022Review
- BreakNet: detecting deletions using long reads and a deep learning approach.BMC bioinformatics · 2021Article
- Deep learning in next-generation sequencing.Drug discovery today · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundCalling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data?
resultsIn this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project.
conclusionsOur work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs.
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