Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 16; no. 4; pp. 1316 - 1321
• Main Authors: Lin, Hao, Liang, Zhi-Yong, Tang, Hua, Chen, Wei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bioinformatics; Composition; Computational Biology - methods; Computer applications; Correlation; Deoxyribonucleic acid; DNA; DNA - genetics; DNA-Directed RNA Polymerases - genetics; Escherichia coli - genetics; False Positive Reactions; Feature extraction; Gene Regulatory Networks; Gene sequencing; Genomics; Genomics - methods; Internet; local DNA structural property; Microorganisms; multi-window Z-curve; Nucleotide sequence; Nucleotides; Nucleotides - genetics; Prokaryote; Promoter Regions, Genetic; Promoters; PseZNC; Regulatory sequences; ROC Curve; Sequence Analysis, DNA; Sigma Factor - genetics; sigma70 promoter; Software; Support Vector Machine; Support vector machines; Transcription initiation
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2017.2666141

Promoters are DNA regulatory elements located directly upstream or at the 5' end of the transcription initiation site (TSS), which are in charge of gene transcription initiation. With the completion of a large number of microorganism genomics, it is urgent to predict promoters accurately in bacteria by using the computational method. In this work, a sequence-based predictor named "iPro70-PseZNC" was designed for identifying sigma70 promoters in prokaryote. In the predictor, the samples of DNA sequences are formulated by a novel pseudo nucleotide composition, called PseZNC, into which the multi-window Z-curve composition and six local DNA structural properties are incorporated. In the 5-fold cross-validation, the area under the curve of receiver operating characteristic of 0.909 was obtained on our benchmark dataset, indicating that the proposed predictor is promising and will provide an important guide in this area. Further studies showed that the performance of PseZNC is better than it of multi-window Z-curve composition. For the sake of convenience for researchers, a user-friendly online service was established and can be freely accessible at http://lin.uestc.edu.cn/server/iPro70-PseZNC. The PseZNC approach can be also extended to other DNA-related problems.