iProm-Sigma54: A CNN Base Prediction Tool for σ54 Promoters.

The sigma (σ) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. σ54 promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify σ54 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 σ54 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 σ54 promoters. Additionally, a publicly accessible web server was constructed.

iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network.

The increased interest in phages as antibacterial agents has resulted in a rise in the number of sequenced phage genomes, necessitating the development of user-friendly bioinformatics tools for genome annotation. A promoter is a DNA sequence that is used in the annotation of phage genomes. In this study we proposed a two layer model called "iProm-phage" for the prediction and classification of phage promoters. Model first layer identify query sequence as promoter or non-promoter and if the query sequence is predicted as promoter then model second layer classify it as phage or host promoter. Furthermore, rather than using non-coding regions of the genome as a negative set, we created a more challenging negative dataset using promoter sequences. The presented approach improves discrimination while decreasing the frequency of erroneous positive predictions. For feature selection, we investigated 10 distinct feature encoding approaches and utilized them with several machine-learning algorithms and a 1-D convolutional neural network model. We discovered that the one-hot encoding approach and the CNN model outperformed based on performance metrics. Based on the results of the 5-fold cross validation, the proposed predictor has a high potential. Furthermore, to make it easier for other experimental scientists to obtain the results they require, we set up a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-phage/.

iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.

A promoter is a short DNA sequence near the start codon, responsible for initiating the transcription of a specific gene in the genome. The accurate recognition of promoters is important for achieving a better understanding of transcriptional regulation. Because of their importance in the process of biological transcriptional regulation, there is an urgent need to develop in silico tools to identify promoters and their types in a timely and accurate manner. A number of prediction methods have been developed in this regard; however, almost all of them are merely used for identifying promoters and their strength or sigma types. The TATA box region in TATA promoter influences the post-transcriptional processes; therefore, in the current study, we developed a two-layer predictor called "iProm-Zea" using the convolutional neural network (CNN) for identify TATA and TATA less promoters. The first layer can be used to identify a given DNA sequence as a promoter or non-promoter. The second layer can be used to identify whether the recognized promoter is the TATA promoter. To find an optimal feature encoding scheme and model, we employed four feature encoding schemes on different machine learning and CNN algorithms, and based on the evaluation results, we selected a one-hot encoding scheme and a CNN model for iProm-Zea. The 5-fold cross validation testing results demonstrated that the constructed predictor showed great potential for identifying promoters and classifying them as TATA and TATA less promoters. Furthermore, we performed cross-species analysis of iProm-Zea to evaluate its performance in other species. Moreover, to make it easier for other experimental scientists to obtain the results they need, we established a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-Zea/.

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters.

A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.