Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

lncRNA - Long Non-coding RNAs

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 - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

Non-LTR Retrotransposons

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...

RNA-seq

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...

You might also read

Related Articles

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

Sort by

Same author

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

Bioinformatics (Oxford, England)·2024

Same author

DIRMC: a database of immunotherapy-related molecular characteristics.

Database : the journal of biological databases and curation·2024

Same author

BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis.

Nature communications·2024

Same author

Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information.

Bioinformatics (Oxford, England)·2023

Same author

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network.

Bioinformatics (Oxford, England)·2023

Same author

MEAHNE: miRNA-Disease Association Prediction Based on Semantic Information in a Heterogeneous Network.

Life (Basel, Switzerland)·2022

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 5, 2025

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

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

Published on: July 9, 2021

IIMLP: integrated information-entropy-based method for LncRNA prediction.

Junyi Li¹, Huinian Li², Xiao Ye²

¹School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, Guangdong, China. lijunyi@hit.edu.cn.

BMC Bioinformatics

|May 13, 2021

Summary

This summary is machine-generated.

This study introduces a novel computational method for predicting long non-coding RNAs (lncRNAs) using information entropy features and machine learning, achieving over 99% accuracy. This advancement aids in understanding lncRNA roles in human diseases.

Keywords:

Generalized topological entropy Information entropy Long non-coding RNA Machine learning

More Related Videos

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Published on: May 30, 2025

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

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

Published on: April 10, 2018

Related Experiment Videos

Last Updated: Nov 5, 2025

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

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

Published on: July 9, 2021

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Published on: May 30, 2025

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

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

Published on: April 10, 2018

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) are increasingly linked to complex human diseases.
Accurate lncRNA prediction is crucial for disease research.
Computational methods leverage sequence data for lncRNA identification.

Purpose of the Study:

To develop an accurate and efficient computational method for predicting human long non-coding RNAs.
To explore the utility of novel information entropy-based features in lncRNA classification.

Main Methods:

Integration of generalized topological entropy to derive 6 novel sequence features.
Utilized machine learning algorithms including Support Vector Machine, XGBoost, and Random Forest.
Combined novel features with existing features like open reading frame for classification.

Main Results:

The developed method achieved a high Area Under the Curve (AUC) of 99.7905%.
Outperformed a comparative method utilizing a larger set of K-mer features.
Demonstrated superior accuracy in distinguishing human lncRNAs.

Conclusions:

A novel, accurate, and efficient method for lncRNA analysis and classification was developed.
The method effectively utilizes information entropy features for improved prediction.
The approach is adaptable for identifying other functional elements within DNA sequences.