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Updated: Jun 8, 2026
08:34
MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria
Published on: February 23, 2021
sRNATarBase: a comprehensive database of bacterial sRNA targets verified by experiments
Yuan Cao1, Jiayao Wu, Qian Liu
1Center of Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China.
RNA (New York, N.Y.)
|September 17, 2010
Summary
sRNATarBase is a new database detailing bacterial small regulatory RNA (sRNA) interactions. It compiles experimentally confirmed sRNA targets to aid in understanding sRNA functions and developing prediction models.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- Bacterial small regulatory RNAs (sRNAs) are crucial regulators of gene expression, influencing diverse biological processes.
- Understanding sRNA-target interactions is essential for deciphering their regulatory roles and developing predictive models.
Purpose of the Study:
- To establish sRNATarBase, a comprehensive database of experimentally validated bacterial sRNA-target interactions.
- To facilitate systematic analysis of sRNA functions and support the development of computational prediction tools.
Main Methods:
- Manual curation of 138 sRNA-target interactions and 252 non-interaction entries from peer-reviewed literature.
- Inclusion of detailed information: experimental protocols, phylogenetic analysis, predicted secondary structures, and binding regions.
- Integration with external databases like GenBank, SWISS-PROT, and MPIDB via hyperlinks.
Main Results:
- sRNATarBase houses 138 experimentally confirmed sRNA-target interactions.
- The database provides rich annotations, including experimental evidence and bioinformatic analyses.
- Cross-linking to other major biological databases enhances data accessibility and utility.
Conclusions:
- sRNATarBase serves as a valuable resource for researchers studying bacterial sRNAs.
- The curated data supports systematic investigation of sRNA-mediated gene regulation.
- This database can advance the field of sRNA research and computational biology.
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