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

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 . Author summary The most recent operon definition highlights the complexity of bacteria gene regulation: 1) Operons consist of one or more overlapping transcription units; 2) genes in an operon might be involved in multiple metabolic pathways; 3) operons might have internal promoters that might be differentially regulated; and 4) not all genes in an operon might be cotranscribed. Most of these characteristics were not considered in the initial operon definition. However, that initial operon definition guided the development of several existing operon detection computational tools which are based, for example, on functional annotations. In this work, we develop a species-agnostic, data-driven, machine learning-based approach for operon detection that allows detected operons to have all the characteristics mentioned above. Our method (OpDetect) directly uses RNA-seq read counts from up to six samples to identify pairs of adjacent genes belonging to the same operon. OpDetect outperforms four state-of-the-art approaches for operon detection and is general enough to be able to accurately detect operons in Caenorhabditis elegans , one of few eukaryotic organisms known to have operons.

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