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Types of RNA

Types of RNA

Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...

Types of RNA

Types of RNA

Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...

Translational Regulation

Translational Regulation

Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...

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Related Experiment Video

Updated: Jul 16, 2026

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

Published on: February 23, 2021

Prediction of small, noncoding RNAs in bacteria using heterogeneous data.

¹Computer Science Department, Wellesley College, Wellesley, MA 02481, USA. btjaden@wellesley.edu

Journal of Mathematical Biology

|March 14, 2007

Summary

sRNAFinder is a novel computational tool that identifies small RNA (sRNA) genes in bacteria. It integrates diverse data types to improve the prediction of these essential noncoding genes.

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Last Updated: Jul 16, 2026

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

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Published on: December 1, 2023

Area of Science:

Bacteriology
Genomics
Bioinformatics

Background:

Small RNAs (sRNAs) are crucial noncoding regulatory elements in prokaryotes.
Over 70 sRNA genes are known in Escherichia coli, with many more predicted.
Existing computational methods often fail to integrate diverse data for sRNA gene prediction.

Purpose of the Study:

To develop and present sRNAFinder, a novel computational system for systematic identification of bacterial noncoding genes.
To improve the accuracy of small RNA (sRNA) gene prediction by integrating heterogeneous data sources.

Main Methods:

A general probabilistic method based on a generalized Markov model.
Implementation as the computational tool sRNAFinder.
Integration of primary sequence data, microarray expression data, and comparative genomics for RNA structure conservation.

Main Results:

sRNAFinder successfully incorporates heterogeneous data for gene prediction.
The system demonstrates improved performance over existing tools for identifying bacterial sRNA genes.
Validation of sRNAFinder's effectiveness in discovering novel noncoding RNA genes.

Conclusions:

sRNAFinder represents a significant advancement in the computational identification of bacterial sRNA genes.
The integration of diverse data sources enhances the accuracy and scope of noncoding gene discovery.
sRNAFinder provides a robust platform for future research into bacterial regulatory networks.