ArticleRNA biology2018
A comparative study of sequence- and structure-based features of small RNAs and other RNAs of bacteria.
Article in RNA biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 28 citations in OpenAlex.
- Intersegmental transfers drive target search in an RNA-targeting CRISPR system.bioRxiv : the preprint server for biology · 2026Article
- Role of Small Non-Coding RNA in Gram-Negative Bacteria: New Insights and Comprehensive Review of Mechanisms, Functions, and Potential Applications.Molecular biotechnology · 2025Review
- pmiRScan: a LightGBM based method for prediction of animal pre-miRNAs.Functional & integrative genomics · 2025Article
- BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.RNA biology · 2024Article
- Benchmarking of five NGS mapping tools for the reference alignment of bacterial outer membrane vesicles-associated small RNAs.Frontiers in microbiology · 2024Article
- High-Resolution Small RNAs Landscape Provides Insights into Alkane Adaptation in the Marine Alkane-Degrader Alcanivorax dieselolei B-5International journal of molecular sciences · 2022Article
- BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.Briefings in bioinformatics · 2022Article
- NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae.Quantitative plant biology · 2022Article
- The intergenic small non-coding RNA ittA is required for optimal infectivity and tissue tropism in Borrelia burgdorferi.PLoS pathogens · 2020Article
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small RNAs (sRNAs) in bacteria have emerged as key players in transcriptional and post-transcriptional regulation of gene expression. Here, we present a statistical analysis of different sequence- and structure-related features of bacterial sRNAs to identify the descriptors that could discriminate sRNAs from other bacterial RNAs. We investigated a comprehensive and heterogeneous collection of 816 sRNAs, identified by northern blotting across 33 bacterial species and compared their various features with other classes of bacterial RNAs, such as tRNAs, rRNAs and mRNAs. We observed that sRNAs differed significantly from the rest with respect to G+C composition, normalized minimum free energy of folding, motif frequency and several RNA-folding parameters like base-pairing propensity, Shannon entropy and base-pair distance. Based on the selected features, we developed a predictive model using Random Forests (RF) method to classify the above four classes of RNAs. Our model displayed an overall predictive accuracy of 89.5%. These findings would help to differentiate bacterial sRNAs from other RNAs and further promote prediction of novel sRNAs in different bacterial species.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.