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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)...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...

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Related Experiment Video

Updated: May 18, 2026

Chromatin Isolation by RNA Purification (ChIRP)
11:09

Chromatin Isolation by RNA Purification (ChIRP)

Published on: March 25, 2012

Computational prediction of polycomb-associated long non-coding RNAs.

Galina V Glazko1, Boris L Zybailov, Igor B Rogozin

  • 1Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America. gvglazko@uams.edu

Plos One
|October 3, 2012
PubMed
Summary

We developed a machine learning model to classify long non-coding RNAs (lncRNAs) based on their function. This model can predict the function of lncRNAs in mice, suggesting conserved biological roles.

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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 18, 2026

Chromatin Isolation by RNA Purification (ChIRP)
11:09

Chromatin Isolation by RNA Purification (ChIRP)

Published on: March 25, 2012

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
  • Computational Biology
  • RNA Biology

Background:

  • Functional annotation of long non-coding RNAs (lncRNAs) is limited.
  • Understanding lncRNA function is crucial for genomic-scale annotation.
  • Polycomb Repressive Complex 2 (PRC2) binding is a key functional characteristic.

Purpose of the Study:

  • To computationally characterize human lncRNAs based on PRC2 binding.
  • To develop a machine learning classifier for predicting lncRNA function.
  • To assess the cross-species applicability of the classifier.

Main Methods:

  • Utilized sequence-structure patterns to differentiate PRC2-binding and non-binding lncRNAs.
  • Developed and evaluated various machine learning classifiers.
  • Identified Support Vector Machine (SVM) as the optimal classifier.
  • Tested classifier performance on independent datasets and across species.

Main Results:

  • The SVM-based classifier accurately distinguished between PRC2-binding and non-binding lncRNAs.
  • The classifier demonstrated generalization capabilities on independent datasets.
  • Trained on human lncRNAs, the classifier predicted 59.4% of PRC2-binding lncRNAs in mice.
  • Identified conserved functional roles for lncRNAs despite low sequence conservation.

Conclusions:

  • Computational classification of lncRNAs based on PRC2 binding is feasible.
  • Machine learning, particularly SVM, is effective for lncRNA functional annotation.
  • lncRNAs exhibit conserved functions across species, even with limited sequence homology.