iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters
The sigma (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>) factor of RNA holoenzymes is essential for identifying and binding to promoter r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/12/6/829 |
| _version_ | 1797612849204297728 |
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| author | Muhammad Shujaat Hoonjoo Kim Hilal Tayara Kil To Chong |
| author_facet | Muhammad Shujaat Hoonjoo Kim Hilal Tayara Kil To Chong |
| author_sort | Muhammad Shujaat |
| collection | DOAJ |
| description | The sigma (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoter sequences to comprehend the underlying process of gene regulation. Herein, we come up with a convolutional neural network (CNN) based prediction tool named “iProm-Sigma54” for the prediction of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoters. The CNN consists of two one-dimensional convolutional layers, which are followed by max pooling layers and dropout layers. A one-hot encoding scheme was used to extract the input matrix. To determine the prediction performance of iProm-Sigma54, we employed four assessment metrics and five-fold cross-validation; performance was measured using a benchmark and test dataset. According to the findings of this comparison, iProm-Sigma54 outperformed existing methodologies for identifying <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoters. Additionally, a publicly accessible web server was constructed. |
| first_indexed | 2024-03-11T06:47:47Z |
| format | Article |
| id | doaj.art-8eac404c549c495db6cca6e8eea3143f |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-11T06:47:47Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-8eac404c549c495db6cca6e8eea3143f2023-11-17T10:12:26ZengMDPI AGCells2073-44092023-03-0112682910.3390/cells12060829iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> PromotersMuhammad Shujaat0Hoonjoo Kim1Hilal Tayara2Kil To Chong3Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of KoreaSchool of Pharmacy, Jeonbuk National University, Jeonju 54896, Republic of KoreaSchool of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of KoreaThe sigma (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoter sequences to comprehend the underlying process of gene regulation. Herein, we come up with a convolutional neural network (CNN) based prediction tool named “iProm-Sigma54” for the prediction of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoters. The CNN consists of two one-dimensional convolutional layers, which are followed by max pooling layers and dropout layers. A one-hot encoding scheme was used to extract the input matrix. To determine the prediction performance of iProm-Sigma54, we employed four assessment metrics and five-fold cross-validation; performance was measured using a benchmark and test dataset. According to the findings of this comparison, iProm-Sigma54 outperformed existing methodologies for identifying <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>54</mn></msup></semantics></math></inline-formula> promoters. Additionally, a publicly accessible web server was constructed.https://www.mdpi.com/2073-4409/12/6/829bioinformaticsdeep learningcomputational biologyDNA promotersconvolutional neural networkssigma factors |
| spellingShingle | Muhammad Shujaat Hoonjoo Kim Hilal Tayara Kil To Chong iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters Cells bioinformatics deep learning computational biology DNA promoters convolutional neural networks sigma factors |
| title | iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters |
| title_full | iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters |
| title_fullStr | iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters |
| title_full_unstemmed | iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters |
| title_short | iProm-Sigma54: A CNN Base Prediction Tool for <i>σ</i><sup>54</sup> Promoters |
| title_sort | iprom sigma54 a cnn base prediction tool for i σ i sup 54 sup promoters |
| topic | bioinformatics deep learning computational biology DNA promoters convolutional neural networks sigma factors |
| url | https://www.mdpi.com/2073-4409/12/6/829 |
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