# OM2Seq: learning retrieval embeddings for optical genome mapping

## TL;DR

OM2Seq is a new method for quickly and accurately mapping DNA images to a reference genome using machine learning, improving speed and accuracy over existing methods.

## Contribution

OM2Seq introduces a Transformer-based model for optical genome mapping that achieves significant improvements in speed and accuracy.

## Key findings

- OM2Seq outperforms baseline methods in computational speed by two orders of magnitude.
- The model achieves higher accuracy in mapping DNA fragment images to a reference genome.

## Abstract

Genomics-based diagnostic methods that are quick, precise, and economical are essential for the advancement of precision medicine, with applications spanning the diagnosis of infectious diseases, cancer, and rare diseases. One technology that holds potential in this field is optical genome mapping (OGM), which is capable of detecting structural variations, epigenomic profiling, and microbial species identification. It is based on imaging of linearized DNA molecules that are stained with fluorescent labels, that are then aligned to a reference genome. However, the computational methods currently available for OGM fall short in terms of accuracy and computational speed.

This work introduces OM2Seq, a new approach for the rapid and accurate mapping of DNA fragment images to a reference genome. Based on a Transformer-encoder architecture, OM2Seq is trained on acquired OGM data to efficiently encode DNA fragment images and reference genome segments to a common embedding space, which can be indexed and efficiently queried using a vector database. We show that OM2Seq significantly outperforms the baseline methods in both computational speed (by 2 orders of magnitude) and accuracy.

https://github.com/yevgenin/om2seq.