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

Frederikke Isa Marin, Dennis Pultz, Wouter Boomsma

Keywords: gene finding, graphical model, gene prediction, gene splicing, conditional random fields, structured decoding, DNA, learned embeddings

TL;DR: Improving the robustness of predicting the exact coding sequences of genomes by combining deep learning with a graphical model encoding gene structure.

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 data sets. However, these models lack the flexibility to incorporate deep learning representation learning techniques that have in recent years been transformative in the analysis of protein sequences, and which could potentially help gene finders exploit the growing number of 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.

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