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

• Published in: Molecular genetics and genomics : MGG 2019-02, Vol.294 (1), p.69-84
• Main Authors: Rahman, Md. Siddiqur, Aktar, Usma, Jani, Md Rafsan, Shatabda, Swakkhar
• Format: Article
• Language: English
• Subjects: Animal Genetics and Genomics; Biochemistry; Biomedical and Life Sciences; Deoxyribonucleic acid; DNA; DNA-directed RNA polymerase; Genomes; Human Genetics; Life Sciences; Microbial Genetics and Genomics; Nucleotide sequence; Original Article; Plant Genetics and Genomics; Promoters
• ISSN: 1617-4615; 1617-4623
• DOI: 10.1007/s00438-018-1487-5

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 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 σ 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.