OMKar automates genome karyotyping using optical maps to identify constitutional abnormalities.
Siavash Raeisi Dehkordi1,2, Zhaoyang Jia1, Joey Estabrook2
1Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA.
Genome Research
|November 14, 2025
Summary
OMKar is a new computational method that creates virtual karyotypes from optical genome mapping data. This tool accurately reconstructs whole-genome karyotypes, aiding in the diagnosis of genetic disorders.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Karyotype analysis is crucial for diagnosing genetic disorders and understanding genetic risk.
- Current microscopic chromosome examination is complex, expertise-dependent, and has limited resolution.
- Optical genome mapping (OGM) offers an efficient method for detecting large-scale genomic variations.
Purpose of the Study:
- To introduce OMKar, a computational method for generating virtual karyotypes from OGM data.
- To assess the accuracy and utility of OMKar for constitutional karyotyping.
Main Methods:
- OMKar integrates structural variants (SVs) and copy number (CN) variants into a breakpoint graph.
- It uses integer linear programming to re-estimate CNs and identify Eulerian paths for chromosome structures.
- The method was evaluated on simulated data and 154 clinical samples.
Main Results:
- OMKar achieved high concordance for SVs (88% precision, 95% recall) and CNs (95% Jaccard score) in simulations.
- It correctly reconstructed karyotypes in 144 out of 154 clinical samples, including aneuploidies and translocations.
- The tool identified genetic mechanisms in previously unexplained cases and diagnosed various genetic syndromes.
Conclusions:
- OMKar demonstrates high accuracy and utility for OGM-based constitutional karyotyping.
- This computational method enhances the diagnostic capabilities for genetic disorders.
- OMKar has the potential to improve genetic risk assessment, diagnosis, and counseling.
