DeepOM: single-molecule optical genome mapping via deep learning

Yevgeni Nogin, Tahir Detinis Zur, Sapir Margalit, Ilana Barzilai, Onit Alalouf, Yuval Ebenstein, Yoav Shechtman

Research output: Contribution to journalArticlepeer-review


Motivation: 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. Results: 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.

Original languageEnglish
Article numberbtad137
Issue number3
StatePublished - 1 Mar 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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