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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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...
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lncRNA - Long Non-coding RNAs02:39

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Types of RNA01:20

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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...
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Types of RNA01:23

Types of RNA

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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...
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Ribosome Profiling02:24

Ribosome Profiling

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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.
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RNA-seq03:21

RNA-seq

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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. 
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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

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Bioinformatics Approaches in Noncoding RNAs Research.

Fariya Khan1, Ajay Kumar2, Salman Akhtar1,3

  • 1Department of Bioengineering, Integral University, Lucknow, India.

Experientia Supplementum (2012)
|January 1, 2026
PubMed
Summary
This summary is machine-generated.

Noncoding RNAs (ncRNAs) are key gene regulators. Bioinformatics tools and databases now enable advanced analysis of ncRNAs, revealing their roles in cellular processes and potential for treating diseases like cancer.

Keywords:
BioinformaticsEpigeneticsGenomeIn silicoTranscriptomelncRNAmiRNAncRNAsncRNA

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Noncoding RNAs (ncRNAs) are crucial regulators of gene expression and cellular processes.
  • The complexity of ncRNAs challenges traditional experimental study methods.
  • Bioinformatics offers advanced computational strategies for ncRNA research.

Purpose of the Study:

  • To explore recent advancements in ncRNA databases and computational analysis tools.
  • To provide an overview of ncRNA structure, classification, and function.
  • To highlight the therapeutic potential of ncRNAs in treating diseases, particularly cancer.

Main Methods:

  • Review of current ncRNA databases and bioinformatics tools.
  • In silico strategies for predicting and analyzing biological interactions of ncRNAs (miRNAs, lncRNAs).
  • Analysis of structural properties, classification, and functional roles of ncRNAs.

Main Results:

  • Advancements in ncRNA databases facilitate deeper understanding of ncRNA roles.
  • Computational strategies enable effective prediction and analysis of ncRNA interactions.
  • ncRNAs demonstrate significant therapeutic potential, especially in oncology.

Conclusions:

  • Bioinformatics tools are essential for overcoming challenges in ncRNA research.
  • Understanding ncRNA functions is critical for advancing precision medicine.
  • ncRNAs hold promise for developing novel targeted therapies for severe diseases.