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

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...

piRNA - Piwi-interacting RNAs

piRNA - Piwi-interacting RNAs

PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...

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...

RNA Structure

RNA Structure

The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...

Ribosome Profiling

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...

RNA Interference

RNA Interference

RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...

You might also read

Related Articles

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

Sort by

Same author

Chromosome-level genome assembly of Trichiurus japonicus resolves species boundaries and evolutionary history of cutlassfishes in Northwest Pacific.

BMC genomics·2026

Same author

Luteolin rescues high-fat diet-induced hippocampal mitochondrial oxidative stress and cognitive decline by regulating gut microbiota-kynurenine-aryl hydrocarbon receptor axis.

Free radical biology & medicine·2026

Same author

Distribution of Aquatic Vertebrates in the Winter Dry Season Informing Water Resource Management in a Large Floodplain Lake.

Biology·2026

Same author

Whole-genome resequencing reveals genetic divergence, local adaptation, and conservation priorities in three helmet catfishes (complex Cranoglanis bouderius).

BMC genomics·2026

Same author

<i>PhyloRef</i>: A Semi-Automated Workflow for eDNA Reference Database Curation via Phylogenetic Anomaly Detection.

Ecology and evolution·2026

Same author

Soil C/N ratio governs bacterial community assembly along an arid mountain elevational gradient.

Frontiers in microbiology·2026

Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026

Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026

Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026

Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026

Same journal

An AI-driven deep learning pipeline for taxonomic classification and biodiversity assessment of deep-sea environmental DNA.

Computers in biology and medicine·2026

Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 17, 2025

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

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

Published on: September 28, 2017

Non-coding RNA identification with pseudo RNA sequences and feature representation learning.

Xian-Gan Chen¹, Xiaofei Yang¹, Chenhong Li¹

¹School of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central Minzu University, Wuhan, 430074, China; Key Laboratory of Cognitive Science(South-Central Minzu University), State Ethnic Affairs Commission, Wuhan, 430074, China.

Computers in Biology and Medicine

|August 28, 2023

Summary

This summary is machine-generated.

This study introduces a novel bioinformatics method, CPPFLPS, for accurately distinguishing non-coding RNAs (ncRNAs) from coding RNAs using pseudo RNA sequences and feature representation learning.

Keywords:

Data augmentation Feature representation learning Machine learning Non-coding RNAs Pseudo RNA sequences

More Related Videos

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

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Related Experiment Videos

Last Updated: Jul 17, 2025

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

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

Published on: September 28, 2017

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

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate identification of non-coding RNAs (ncRNAs) from coding RNAs is crucial in bioinformatics.
Existing methods face challenges in improving ncRNA identification accuracy.

Purpose of the Study:

To propose a novel coding potential predictor, CPPFLPS, for enhanced ncRNA identification.
To leverage feature representation learning based on pseudo RNA sequences for improved accuracy.

Main Methods:

Generating pseudo RNA sequences via data augmentation using six RNA mutation simulations.
Employing a feature representation learning framework with baseline classifiers.
Utilizing selected feature vectors from baseline models to train a final predictive model.

Main Results:

The proposed CPPFLPS method demonstrates superior performance compared to existing state-of-the-art approaches.
Achieved improved accuracy in distinguishing ncRNAs from coding RNAs.

Conclusions:

CPPFLPS offers a robust and accurate solution for ncRNA identification.
The method's effectiveness is attributed to data augmentation with pseudo RNA sequences and feature representation learning.