ArticleScientific reports2021
Detecting operons in bacterial genomes via visual representation learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Transformer-based operon prediction using textual representations of gene pairs.Bioinformatics advances · 2026Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- ExcludonFinder: mapping transcriptional overlaps between neighboring genes.Nucleic acids research · 2025Article
- The carRS-ompV-virK operon of Vibrio cholerae senses antimicrobial peptides and activates the expression of multiple resistance systems.Scientific reports · 2025Article
- OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data.PloS one · 2025Article
- Identification of Angiogenesis-Related Gene Signatures and Prediction of Potential Therapeutic Targets in Ulcerative Colitis Using Integrated Bioinformatics.Journal of inflammation research · 2024Article
- Current and Emerging Tools of Computational Biology To Improve the Detoxification of Mycotoxins.Applied and environmental microbiology · 2022Article
- OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data.PLoS computational biology · 2022Article
- Identifying genomic islands with deep neural networks.BMC genomics · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network's decisions to be subsequently cross-checked by human experts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.