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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)...
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...
Types of RNA01:23

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...
Ribosome Profiling02:24

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...
Regulated mRNA Transport02:22

Regulated mRNA Transport

In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing specific...

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Related Experiment Video

Updated: Jun 27, 2026

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

RNA-Binding Protein Occupancy Composition Predicts Long Noncoding RNA Subcellular Localization.

Hidenori Tani1

  • 1Department of Health Pharmacy, Yokohama University of Pharmacy, Yokohama 245-0066, Kanagawa, Japan.

International Journal of Molecular Sciences
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

The composition of RNA-binding proteins (RBPs) bound to long noncoding RNAs (lncRNAs) predicts their cellular location. This RBP-based model complements sequence-based methods for understanding lncRNA localization and function.

Keywords:
RNA-binding proteincross-validated predictioneCLIPlong noncoding RNAnuclear retentionsubcellular localization

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Related Experiment Videos

Last Updated: Jun 27, 2026

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

Published on: July 22, 2016

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09:36

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

Published on: April 10, 2018

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Area of Science:

  • Molecular Biology
  • Genomics
  • Cell Biology

Background:

  • The function of long noncoding RNAs (lncRNAs) is determined by their subcellular localization.
  • The molecular mechanisms governing lncRNA localization are not fully understood.
  • Current prediction methods primarily rely on RNA sequence, neglecting protein interactions.

Purpose of the Study:

  • To investigate whether the repertoire of RNA-binding proteins (RBPs) associated with lncRNAs can predict their nuclear or cytoplasmic localization.
  • To develop a novel prediction model for lncRNA localization based on RBP composition.
  • To assess the contribution of RBP binding to lncRNA localization beyond sequence-based features.

Main Methods:

  • Integration of enhanced crosslinking and immunoprecipitation (eCLIP) data for 139 RBPs with subcellular fractionation data (cytoplasmic-nuclear relative concentration indices, CN-RCIs) in K562 cells.
  • Development of a localization prediction model using chromosome-grouped cross-validation and nested regularization.
  • Analysis of RBP functional roles based on the signed coefficient profile of the predictive model.

Main Results:

  • RBP-occupancy composition significantly predicted lncRNA localization, outperforming models based solely on transcript size and binding amount (delta-R-squared = 0.17, AUC = 0.73).
  • This predictive power remained significant even when accounting for transcript abundance, intron content, and exon number (delta-R-squared = 0.12).
  • The model's coefficient profile revealed that RBPs involved in nuclear processes were associated with nuclear localization, while those in cytoplasmic processes predicted cytoplasmic localization (p = 0.013).
  • The predictive model generalized across cell lines, with a K562-trained model accurately predicting HepG2 localization (AUC = 0.71) and vice versa (AUC = 0.77).

Conclusions:

  • The composition of bound RBPs is a significant determinant of lncRNA subcellular localization.
  • An RBP-occupancy-based model offers an interpretable complement to existing sequence-based predictors.
  • This finding provides new insights into the functional regulation of lncRNAs through protein-RNA interactions.