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

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