iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features

Abstract

In bacterial DNA, there are specific sequences of nucleotides called promoters that can bind to the RNA polymerase. Sigma70 (\(\sigma ^{70}\)) is one of the most important promoter sequences due to its presence in most of the DNA regulatory functions. In this paper, we identify the most effective and optimal sequence-based features for prediction of \(\sigma ^{70}\) promoter sequences in a bacterial genome. We used both short-range and long-range DNA sequences in our proposed method. A very small number of effective features are selected from a large number of the extracted features using multi-window of different sizes within the DNA sequences. We call our prediction method iPro70-FMWin and 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.

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Correspondence to Swakkhar Shatabda.

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Rahman, M.S., Aktar, U., Jani, M.R. et al. iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features. Mol Genet Genomics 294, 69–84 (2019). https://doi.org/10.1007/s00438-018-1487-5

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Keywords

  • \(\sigma ^{70}\) promoter
  • Prokaryote
  • Sequence-based features
  • Multi-windowing
  • Feature selection