ArticleBioinformatics (Oxford, England)2023
DeepOM: single-molecule optical genome mapping via deep learning.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- One-click reconstruction in single-molecule localization microscopy via experimental parameter-aware deep learning.Npj imaging · 2025Article
- Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy.Small methods · 2025Article
- DeepMAP: Deep CNN Classifiers Applied to Optical Mapping for Fast and Precise Species-Level Metagenomic Analysis.ACS omega · 2025Article
- This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model.Small methods · 2025Article
- OM2Seq: learning retrieval embeddings for optical genome mapping.Bioinformatics advances · 2024Article
- Design of optimal labeling patterns for optical genome mapping via information theory.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
7 authors.
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Abstract
motivationEfficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optical genome mapping utilizing deep learning is presented, termed DeepOM. Utilization of a convolutional neural network, trained on simulated images of labeled DNA molecules, improves the success rate in the alignment of DNA images to genomic references.
resultsThe method is evaluated on acquired images of human DNA molecules stretched in nano-channels. The accuracy of the method is benchmarked against state-of-the-art commercial software Bionano Solve. The results show a significant advantage in alignment success rate for molecules shorter than 50 kb. DeepOM improves the yield, sensitivity, and throughput of optical genome mapping experiments in applications of human genomics and microbiology. AVAILABILITY AND IMPLEMENTATION: The source code for the presented method is publicly available at https://github.com/yevgenin/DeepOM.
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