OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data.
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 facilitat...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0329355 |
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| author | Rezvan Karaji Lourdes Peña-Castillo |
| author_facet | Rezvan Karaji Lourdes Peña-Castillo |
| author_sort | Rezvan Karaji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36eb5d3ac6c440bfb9211bf2d6a70be2 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-36eb5d3ac6c440bfb9211bf2d6a70be22025-08-20T02:56:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032935510.1371/journal.pone.0329355OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data.Rezvan KarajiLourdes Peña-CastilloAn 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.https://doi.org/10.1371/journal.pone.0329355 |
| spellingShingle | Rezvan Karaji Lourdes Peña-Castillo OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data. PLoS ONE |
| title | OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data. |
| title_full | OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data. |
| title_fullStr | OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data. |
| title_full_unstemmed | OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data. |
| title_short | OpDetect: A convolutional and recurrent neural network classifier for precise and sensitive operon detection from RNA-seq data. |
| title_sort | opdetect a convolutional and recurrent neural network classifier for precise and sensitive operon detection from rna seq data |
| url | https://doi.org/10.1371/journal.pone.0329355 |
| work_keys_str_mv | AT rezvankaraji opdetectaconvolutionalandrecurrentneuralnetworkclassifierforpreciseandsensitiveoperondetectionfromrnaseqdata AT lourdespenacastillo opdetectaconvolutionalandrecurrentneuralnetworkclassifierforpreciseandsensitiveoperondetectionfromrnaseqdata |