Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.6K
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...
9.6K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.2K
3.2K
Types of RNA01:20

Types of RNA

8.6K
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...
8.6K
Types of RNA01:23

Types of RNA

72.0K
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...
72.0K
RNA-seq03:21

RNA-seq

11.5K
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...
11.5K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

11.5K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
11.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multidisciplinary Management of Chronic Atypical Facial Pain of Psychogenic Origin: A Unique Case Report.

International journal of clinical pediatric dentistry·2020
Same author

Beyond the flavour: a de-flavoured polyphenol rich extract of clove buds (Syzygium aromaticum L) as a novel dietary antioxidant ingredient.

Food & function·2015
See all related articles

Related Experiment Video

Updated: Dec 13, 2025

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
09:36

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

Published on: April 10, 2018

26.0K

A Heterogeneous Information Network Model for Long Non-Coding RNA Function Prediction.

Sunil Kumar P V, Adheeba Thahsin, Manju M

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 6, 2020
    PubMed
    Summary

    This study introduces a computational method to predict long non-coding RNA (lncRNA) functions, overcoming challenges of experimental characterization. The novel approach accurately identifies functions for thousands of lncRNAs, aiding biological research.

    More Related Videos

    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
    07:24

    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

    Published on: July 9, 2021

    2.6K
    Detection of RNA-binding Proteins by In Vitro RNA Pull-down in Adipocyte Culture
    10:34

    Detection of RNA-binding Proteins by In Vitro RNA Pull-down in Adipocyte Culture

    Published on: July 22, 2016

    24.2K

    Related Experiment Videos

    Last Updated: Dec 13, 2025

    RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
    09:36

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

    Published on: April 10, 2018

    26.0K
    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
    07:24

    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

    Published on: July 9, 2021

    2.6K
    Detection of RNA-binding Proteins by In Vitro RNA Pull-down in Adipocyte Culture
    10:34

    Detection of RNA-binding Proteins by In Vitro RNA Pull-down in Adipocyte Culture

    Published on: July 22, 2016

    24.2K

    Area of Science:

    • Genomics
    • Computational Biology
    • Molecular Biology

    Background:

    • Long non-coding RNAs (lncRNAs) are increasingly identified but poorly functionally annotated.
    • Experimental methods for lncRNA functional characterization are time-consuming and costly.
    • Predicting lncRNA functions computationally is crucial for advancing research.

    Purpose of the Study:

    • To develop an accurate computational method for predicting lncRNA functions.
    • To address the challenge of low functional annotation rates in lncRNAs.
    • To provide a valuable tool for researchers investigating lncRNA roles.

    Main Methods:

    • Constructed a Heterogeneous Information Network (HIN) integrating lncRNA-protein, lncRNA co-expression, and protein-protein interaction data.
    • Employed a meta-path based measure, AvgSim, within the HIN to predict lncRNA functions.
    • Evaluated the model's performance on a large set of lncRNAs.

    Main Results:

    • Successfully predicted functions for 2,695 out of 2,758 lncRNAs with 73.68% accuracy.
    • The proposed method outperformed existing state-of-the-art approaches on an independent test set.
    • Case studies on HOTAIR and H19 validated predicted functions against existing literature.

    Conclusions:

    • The developed computational method offers an efficient and accurate approach for lncRNA function prediction.
    • This tool can significantly accelerate the functional characterization of newly identified lncRNAs.
    • The freely available script and data facilitate broader adoption and further research.