Ranking labs-of-origin for genetically engineered DNA using Metric Learning

TL;DR

This paper presents a metric learning approach to rank labs-of-origin for genetically engineered DNA, outperforming traditional methods and providing versatile embeddings for various genetic attribution tasks.

Contribution

The authors introduce a novel metric learning method that generates embeddings for DNA sequences and labs, improving ranking accuracy and enabling multiple downstream applications.

Findings

Outperforms classic training methods in lab-of-origin ranking

Generates useful embeddings for clustering and feature extraction

Supports multiple genetic attribution tasks

Abstract

With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also perform various other tasks, like clustering both DNA sequences and labs and using them as features for Machine Learning models applied to solve other problems. This work demonstrates that our method outperforms the classic training method for this task while generating other helpful information.