ArticleBMC bioinformatics2021
BreakNet: detecting deletions using long reads and a deep learning approach.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- TM-Loop: Transformer multi-omics hierarchical detection of chromatin loop.Scientific reports · 2026Article
- A method for structural variant detection using Hi-C contact matrix and neural networks.Scientific reports · 2026Article
- HiSVision: A Method for Detecting Large-Scale Structural Variations Based on Hi-C Data and Detection Transformer.Interdisciplinary sciences, computational life sciences · 2025Article
- GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.Briefings in bioinformatics · 2025Article
- A Hitchhiker's Guide to long-read genomic analysis.Genome research · 2025Review
- DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.BMC bioinformatics · 2025Article
- SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture.Journal of translational medicine · 2025Article
- Article
- GTasm: a genome assembly method using graph transformers and HiFi reads.Frontiers in genetics · 2024Article
- cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network.BMC bioinformatics · 2023Article
- LSnet: detecting and genotyping deletions using deep learning network.Frontiers in genetics · 2023Article
- Evaluation of the Available Variant Calling Tools for Oxford Nanopore Sequencing in Breast Cancer.Genes · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundStructural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs.
resultsIn this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at https://github.com/luojunwei/BreakNet .
conclusionsOur work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods.
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