Detecting operons in bacterial genomes via visual representation learning

Assaf, Rida; Xia, Fangfang; Stevens, Rick

doi:10.1038/s41598-021-81169-9

Detecting operons in bacterial genomes via visual representation learning

Journal Article · Fri Jan 22 04:00:00 EST 2021 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-021-81169-9· OSTI ID:1785368

Assaf, Rida ^[1]; Xia, Fangfang ^[2]; Stevens, Rick ^[3]

Univ. of Chicago, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering; Argonne National Lab. (ANL), Argonne, IL (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; National Institute of Allergy and Infectious Diseases (NIAID); National Institutes of Health (NIH)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1785368

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 11; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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