# OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data

## TL;DR

OpDetect is a new tool that uses RNA-seq data to accurately detect operons in prokaryotic genomes and even in some eukaryotes.

## Contribution

OpDetect introduces a species-agnostic deep learning approach for operon detection using RNA-seq data.

## Key findings

- OpDetect outperforms existing methods in recall, F1-score, and AUROC metrics.
- The method works across a wide range of bacterial species and even in C. elegans.
- OpDetect uses a convolutional and recurrent neural network architecture for operon detection.

## Abstract

An operon refers to a group of neighbouring genes belonging to one or more overlapping transcription units that are transcribed in the same direction and have at least one gene in common. Operons are a characteristic of prokaryotic genomes. Identifying which genes belong to the same operon facilitates understanding of gene function and regulation. There are several computational approaches for operon detection; however, many of these computational approaches have been developed for a specific target bacterium or require information only available for a restricted number of bacterial species. Here, we introduce a general method, OpDetect, that directly utilizes RNA-sequencing (RNA-seq) reads as a signal over nucleotide bases in the genome. This representation enabled us to employ a convolutional and recurrent deep neural network architecture which demonstrated superior performance in terms of recall, F1-score and Area under the Receiver-Operating characteristic Curve (AUROC) compared to previous approaches. Additionally, OpDetect showcases species-agnostic capabilities, successfully detecting operons in a wide range of bacterial species and even in Caenorhabditis elegans, one of few eukaryotic organisms known to have operons. OpDetect is available at https://github.com/BioinformaticsLabAtMUN/OpDetect.