  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.
