Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition
By Hao Lin, Zhi-Yong Liang, Hua Tang, Wei Chen. 2019
162 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Computational Biology & Bioinformatics
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Citations in the corpus
- Big Bird: Transformers for Longer Sequences (2020, methodology): “Multiple methods have been proposed to identify the promoter regions in a given DNA sequence [100, 59, 11, 99, 71], as it is an important first step in understanding gene regulation.”
- DeePromoter: Robust Promoter Predictor Using Deep Learning (2019, background): “Lin et al. (2017) proposed a sequence-based predictor, named “iPro70-PseZNC”, for sigma70 promoter’s identification in the prokaryote.”