Russel Berrie Nanotechnology Institute, Technion, Haifa 320003, Israel.
Raymond and Beverly Sackler Faculty of Exact Sciences, Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 6997801, Israel.
Bioinformatics. 2023 Mar 1;39(3). doi: 10.1093/bioinformatics/btad137.
Efficient 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.
The 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.
The source code for the presented method is publicly available at https://github.com/yevgenin/DeepOM.
从完整的 DNA 分子的单个微观图像中高效挖掘基因组信息是一项突出的挑战，其解决方案将为分子诊断开辟新的前沿。在这里，提出了一种利用深度学习进行光学基因组图谱绘制的新计算方法，称为 DeepOM。利用在标记 DNA 分子的模拟图像上训练的卷积神经网络，可以提高 DNA 图像与基因组参考物的对齐成功率。
该方法在纳米通道中拉伸的人类 DNA 分子的采集图像上进行了评估。该方法的准确性与最先进的商业软件 Bionano Solve 进行了基准测试。结果表明，对于短于 50kb 的分子，对齐成功率有显著优势。DeepOM 提高了人类基因组学和微生物学应用中光学基因组图谱绘制实验的产量、灵敏度和通量。
所提出方法的源代码可在 https://github.com/yevgenin/DeepOM 上公开获取。
