ProtiGeno: a prokaryotic short gene finder using protein language models

TL;DR

ProtiGeno is a deep learning-based tool that improves the detection of short prokaryotic genes by leveraging protein language models trained on large datasets, outperforming existing gene finders.

Contribution

It introduces a novel deep learning method specifically designed for short prokaryotic gene prediction using protein language models, addressing limitations of current tools.

Findings

ProtiGeno achieves higher accuracy and recall for short genes in 4,288 genomes.

It effectively predicts both coding and noncoding short genes.

The model's features are interpretable through structural visualization.

Abstract

Prokaryotic gene prediction plays an important role in understanding the biology of organisms and their function with applications in medicine and biotechnology. Although the current gene finders are highly sensitive in finding long genes, their sensitivity decreases noticeably in finding shorter genes (<180 nts). The culprit is insufficient annotated gene data to identify distinguishing features in short open reading frames (ORFs). We develop a deep learning-based method called ProtiGeno, specifically targeting short prokaryotic genes using a protein language model trained on millions of evolved proteins. In systematic large-scale experiments on 4,288 prokaryotic genomes, we demonstrate that ProtiGeno predicts short coding and noncoding genes with higher accuracy and recall than the current state-of-the-art gene finders. We discuss the predictive features of ProtiGeno and possible limitations by visualizing the three-dimensional structure of the predicted short genes. Data, codes, and models are available at https://github.com/tonytu16/protigeno.