iPromoter-BnCNN: a Novel Branched CNN Based Predictor for Identifying and Classifying Sigma Promoters

TL;DR

iPromoter-BnCNN is a deep learning tool that accurately identifies and classifies six types of sigma promoters in DNA sequences, outperforming existing methods on benchmark datasets.

Contribution

The paper introduces a novel branched CNN model that combines multiple sequence features for precise sigma promoter classification.

Findings

Outperforms state-of-the-art tools on benchmark datasets

Achieves high accuracy in 5-fold cross-validation

Successfully classifies six sigma promoter types

Abstract

Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra and inter class variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. We present iPromoter-BnCNN for identification and accurate classification of six types of promoters - sigma24, sigma28, sigma32, sigma38, sigma54, sigma70. It is a Convolutional Neural Network (CNN) based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with two state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset. Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found at https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ.