期刊
PLOS ONE
卷 15, 期 5, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0228479
关键词
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资金
- National Natural Science Foundation of China [61762026, 61462018]
- Guangxi Natural Science Foundation [2017GXNSFAA198278, 2016GXNSFAA380043]
- Innovation Project of GUET Graduate Education [2018YJCX47, 2019YCXS056]
- Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics [GIIP201502]
- Guangxi Key Laboratory of Trusted Software [kx201403]
Terminator is a DNA sequence that gives the RNA polymerase the transcriptional termination signal. Identifying terminators correctly can optimize the genome annotation, more importantly, it has considerable application value in disease diagnosis and therapies. However, accurate prediction methods are deficient and in urgent need. Therefore, we proposed a prediction method iterb-PPse for terminators by incorporating 47 nucleotide properties into PseKNC-I and PseKNC-II and utilizing Extreme Gradient Boosting to predict terminators based on Escherichia coli and Bacillus subtilis. Combing with the preceding methods, we employed three new feature extraction methods K-pwm, Base-content, Nucleotidepro to formulate raw samples. The two-step method was applied to select features. When identifying terminators based on optimized features, we compared five single models as well as 16 ensemble models. As a result, the accuracy of our method on benchmark dataset achieved 99.88%, higher than the existing state-of-the-art predictor iTerm-PseKNC in 100 times five-fold cross-validation test. Its prediction accuracy for two independent datasets reached 94.24% and 99.45% respectively. For the convenience of users, we developed a software on the basis of iterb-PPse with the same name. The open software and source code of iterb-PPse are available at https://github.com/Sarahyouzi/iterb-PPse.
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