Identifying Sigma 70 Promoters Using Multiple Windowing and Optimal Features

Abstract

In bacterial DNA, there are specific sequences of nucleotides called promoters that can bind to the RNA polymerase. Sigma70 (σ 70) is one of the most important promoter sequences due to it’s presence in most of the DNA regulatory functions. In this thesis work, we have identified the most effective and optimal sequence-based features for prediction of σ 70 promoter sequences in a bacterial genome. We have used both short-range and long-range DNA sequences in our proposed method. A very small number of effective features have been selected from a large number of the extracted features using different sized multi-window within the DNA sequences. We call our prediction method iPro70-FMWin and have made it freely accessible online via a web application established at http://ipro70.pythonanywhere.com/server for the sake of convenience of the researchers. We have tested our method using a standard benchmark dataset. In the experiments, iPro70-FMWin has achieved an area under the curve of the receiver operating characteristic and accuracy of 0.959 and 90.57% respectively which significantly outperforms the state-of-the-art predictors.