ArticleNucleic acids research2014
iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers.
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Who cites it
155 citing papers in PubMed.
- EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.PLoS computational biology · 2026Article
- Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.Scientific reports · 2026Article
- PromoterAtlas: decoding regulatory sequences across Gammaproteobacteria using a transformer model.Nature communications · 2026Article
- PBP_ICBA: a prediction of bacterial promoters in specific organisms using an improved convolutional block attention module.Journal of computer-aided molecular design · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
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- Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering.ACS synthetic biology · 2025Article
- Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction.PLoS computational biology · 2025Article
- An ensemble strategy for piRNA identification through hybrid moment-based feature modeling.Scientific reports · 2025Article
- Inducible promoters of bacterial microcompartments improve the CRISPR/Cas9 tools for efficient metabolic engineering ofApplied and environmental microbiology · 2025Article
- ProPr54 web server: predicting σNAR genomics and bioinformatics · 2025Article
- Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024Article
- Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.BMC bioinformatics · 2024Article
- Anaerobic benzene oxidation inApplied and environmental microbiology · 2024Article
- A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications.Scientific reports · 2024Article
- CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria.bioRxiv : the preprint server for biology · 2024Article
- msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths.BMC biology · 2024Article
- Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification.Scientific reports · 2024Article
- iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.Heliyon · 2024Article
95 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The σ(54) promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ(54) promoters. Here, a predictor called 'iPro54-PseKNC' was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called 'pseudo k-tuple nucleotide composition', which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ(54) promoters.
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