Design of optimal labeling patterns for optical genome mapping via information theory

Yevgeni Nogin, Daniella Bar-Lev, Dganit Hanania, Tahir Detinis Zur, Yuval Ebenstein, Eitan Yaakobi, Nir Weinberger, Yoav Shechtman

Research output: Contribution to journalArticlepeer-review


Motivation: Optical genome mapping (OGM) is a technique that extracts partial genomic information from optically imaged and linearized DNA fragments containing fluorescently labeled short sequence patterns. This information can be used for various genomic analyses and applications, such as the detection of structural variations and copy-number variations, epigenomic profiling, and microbial species identification. Currently, the choice of labeled patterns is based on the available biochemical methods and is not necessarily optimized for the application. Results: In this work, we develop a model of OGM based on information theory, which enables the design of optimal labeling patterns for specific applications and target organism genomes. We validated the model through experimental OGM on human DNA and simulations on bacterial DNA. Our model predicts up to 10-fold improved accuracy by optimal choice of labeling patterns, which may guide future development of OGM biochemical labeling methods and significantly improve its accuracy and yield for applications such as epigenomic profiling and cultivation-free pathogen identification in clinical samples. Availability and implementation:

Original languageEnglish
Article numberbtad601
Issue number10
StatePublished - 1 Oct 2023

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Design of optimal labeling patterns for optical genome mapping via information theory'. Together they form a unique fingerprint.

Cite this