Gene finding revisited: improved robustness through structured decoding from learned embeddings

TL;DR

This paper introduces a new gene-finding method that combines learned sequence embeddings with structured decoding, achieving state-of-the-art performance with increased robustness and less manual tuning, facilitating cross-organism applications.

Contribution

The authors propose a novel approach integrating deep learning embeddings with a latent conditional random field for gene finding, enhancing robustness and reducing manual parameter tuning.

Findings

Achieves performance matching current state-of-the-art methods.

Increases training robustness and reduces manual tuning.

Eliminates the need for manually fitted length distributions.

Abstract

Gene finding is the task of identifying the locations of coding sequences within the vast amount of genetic code contained in the genome. With an ever increasing quantity of raw genome sequences, gene finding is an important avenue towards understanding the genetic information of (novel) organisms, as well as learning shared patterns across evolutionarily diverse species. The current state of the art are graphical models usually trained per organism and requiring manually curated datasets. However, these models lack the flexibility to incorporate deep learning representation learning techniques that have in recent years been transformative in the analysis of pro tein sequences, and which could potentially help gene finders exploit the growing number of the sequenced genomes to expand performance across multiple organisms. Here, we propose a novel approach, combining learned embeddings of raw genetic sequences with exact decoding using a latent conditional random field. We show that the model achieves performance matching the current state of the art, while increasing training robustness, and removing the need for manually fitted length distributions. As language models for DNA improve, this paves the way for more performant cross-organism gene-finders.