ArticlePloS one2020
iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 7 citations in OpenAlex.
- Canonical transcription termination mechanisms explain a minority of operons in cyanobacteria.mSystems · 2026Article
- Canonical transcription termination mechanisms explain a minority of operons in cyanobacteria.bioRxiv : the preprint server for biology · 2025Article
- Optimization Strategy of Expression Vectors and Regulatory Elements for Enhanced Protein Production inInternational journal of molecular sciences · 2025Review
- Dual-channel heterogeneous feature fusion neural network for the prediction of post-transcriptional gene expression in Escherichia coli.BMC bioinformatics · 2025Article
- BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble.NAR genomics and bioinformatics · 2025Article
- m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier.International journal of molecular sciences · 2023Article
- Phage Annotation Guide: Guidelines for Assembly and High-Quality Annotation.PHAGE (New Rochelle, N.Y.) · 2021Article
- Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features.BMC bioinformatics · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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