iProm-Sigma54: A CNN Base Prediction Tool for sigma(54) Promoters

卷:12

Article

关键词: TRANSCRIPTION START SITES;    RECOGNITION;    EXPRESSION;    SEQUENCES;    REGIONS;    GENES;   

DOI  :  10.3390/cells12060829

来源: SCIE

【 摘 要 】

The sigma (s) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. s(54) promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify s(54) 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 s(54) 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 s(54) promoters. Additionally, a publicly accessible web server was constructed.