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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

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

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)...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Types of RNA01:20

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...
RNA-seq03:21

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...

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Related Experiment Video

Updated: May 17, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks.

Xingli Guo1, Lin Gao, Qi Liao

  • 1School of computer science and technology, Xidian University, 2 South Taibai Road, Xi'an Shaanxi, 710071, PR China.

Nucleic Acids Research
|November 8, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces lnc-GFP, a novel network-based tool to predict functions for long non-coding RNAs (lncRNAs). The method accurately annotates lncRNA functions, aiding biological process understanding.

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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

Related Experiment Videos

Last Updated: May 17, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

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

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long non-coding RNAs (lncRNAs) are increasingly recognized for their crucial roles in various biological processes.
  • A significant challenge in current research is the functional annotation of the rapidly growing number of identified lncRNAs.
  • Existing methods often lack the scalability to address the vastness of lncRNA data.

Purpose of the Study:

  • To develop and validate a novel, large-scale computational strategy for predicting the functions of long non-coding RNAs.
  • To introduce the long non-coding RNA global function predictor ('lnc-GFP'), a tool designed for high-throughput lncRNA function annotation.
  • To address the critical need for functional characterization of numerous unannotated lncRNAs.

Main Methods:

  • Development of a bi-colored network incorporating gene expression and protein interaction data.
  • Application of the 'lnc-GFP' tool for global function prediction across a large set of lncRNAs.
  • Performance evaluation using cross-validation on protein-coding genes with established functional annotations.

Main Results:

  • The lnc-GFP method achieved a precision of up to 95% in predicting functions for protein-coding genes.
  • Functional characterization was successfully assigned to 94.9% of lncRNAs within the largest connected component of the bi-colored network (1625 out of 1713).
  • Predicted functions for lncRNAs in mouse embryonic stem cells and neuronal cells showed strong concordance with existing literature.

Conclusions:

  • The developed network-based approach, implemented in lnc-GFP, provides an effective and scalable solution for lncRNA function prediction.
  • This method significantly advances the functional annotation of lncRNAs, contributing to a deeper understanding of their biological roles.
  • lnc-GFP demonstrates high accuracy and applicability, offering a valuable resource for researchers in genomics and molecular biology.