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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

Types of RNA

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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.
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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RNA Splicing01:32

RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Related Experiment Video

Updated: Mar 18, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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Long non-coding RNAs and complex diseases: from experimental results to computational models.

Xing Chen, Chenggang Clarence Yan, Xu Zhang

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    |June 28, 2016
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    Summary

    Long non-coding RNAs (lncRNAs) play crucial roles in cellular processes and disease development. This review highlights lncRNA functions, disease associations, and computational models for identifying disease-related lncRNAs.

    Keywords:
    biological networkcomplex diseasecomputational modellncRNA–disease association predictionlong non-coding RNAmachine learning

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    Area of Science:

    • Molecular Biology
    • Genomics
    • Bioinformatics

    Background:

    • Long non-coding RNAs (lncRNAs) are increasingly recognized for their diverse roles in cellular functions.
    • Dysregulation of lncRNAs is implicated in the pathogenesis of various human complex diseases.
    • Thousands of lncRNAs have been identified across eukaryotic organisms, highlighting their biological significance.

    Purpose of the Study:

    • To provide an overview of lncRNA functions and their involvement in human diseases.
    • To review key lncRNA-disease associations and relevant databases.
    • To introduce and analyze computational models for predicting lncRNA-disease relationships.

    Main Methods:

    • Literature review of lncRNA functions, disease associations, and databases.
    • Analysis of existing computational models for lncRNA-disease association prediction.
    • Discussion of limitations and future directions in computational lncRNA research.

    Main Results:

    • lncRNAs are involved in numerous biological processes and disease mechanisms.
    • A limited number of lncRNAs are experimentally validated for disease relevance, necessitating predictive approaches.
    • Various computational models exist for large-scale identification and validation of disease-related lncRNAs.

    Conclusions:

    • lncRNA-disease association analysis is crucial for understanding disease mechanisms and developing biomarkers.
    • Computational models offer powerful tools for identifying novel disease-related lncRNAs.
    • Further development of computational models is essential for advancing lncRNA research and its clinical applications.