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Updated: May 11, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

OM2Seq: learning retrieval embeddings for optical genome mapping.

Yevgeni Nogin¹, Danielle Sapir², Tahir Detinis Zur³

¹Russel Berrie Nanotechnology Institute, Technion, Haifa 320003, Israel.

Bioinformatics Advances

|June 25, 2024

Summary

This summary is machine-generated.

OM2Seq accelerates genomic analysis by accurately mapping DNA fragment images to reference genomes. This novel approach significantly improves computational speed and precision for optical genome mapping applications.

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomics-based diagnostics are crucial for precision medicine, enabling applications in infectious disease, cancer, and rare disease diagnosis.
Optical Genome Mapping (OGM) is a promising technology for detecting structural variations, epigenomic profiling, and microbial identification.
Current computational methods for OGM lack the necessary accuracy and speed for widespread clinical adoption.

Purpose of the Study:

To introduce OM2Seq, a novel computational approach for rapid and accurate DNA fragment image mapping in OGM.
To enhance the efficiency and precision of OGM data analysis.

Main Methods:

Developed OM2Seq, a Transformer-encoder based architecture.
Trained OM2Seq on acquired OGM data to create a common embedding space for DNA fragment images and reference genome segments.
Utilized a vector database for efficient indexing and querying of the embedding space.

Main Results:

OM2Seq demonstrates significantly improved accuracy compared to baseline methods.
OM2Seq achieves a 2-orders-of-magnitude increase in computational speed.
The approach enables rapid and precise mapping of OGM data.

Conclusions:

OM2Seq represents a substantial advancement in OGM computational analysis.
The method offers a faster and more accurate solution for genomic variant detection and other OGM applications.
OM2Seq has the potential to accelerate the adoption of OGM in clinical diagnostics and research.