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

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-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

Genetic Determinants of Severe Hypertriglyceridemia: Rare Variants in <i>LPL</i>, <i>APOC2</i>, <i>APOA5</i>, <i>GPIHBP1</i>, <i>LMF1</i>, <i>APOE</i> and Polygenic Risk.

International journal of molecular sciences·2026

Same author

Sequence-Based Models for RNA-Protein Interactions Imputation Might Be Insufficient for Novel Signal Prediction in eCLIP Data.

International journal of molecular sciences·2026

Same author

Genetic and Metabolic Factors of Familial Dysbetalipoproteinemia Phenotype: Insights from a Cross-Sectional Study.

International journal of molecular sciences·2025

Same author

A role of pigment epithelium-derived factor in zinc-mediated mechanism of neurodegeneration in glaucoma.

Communications biology·2025

Same author

Immunocytochemical detection of proteins within cellular structures inaccessible to specific antibodies.

Histochemistry and cell biology·2025

Same author

A Novel Bradycardia-Associated Variant in <i>HCN4</i> as a Candidate Modifier in Type 3 Long QT Syndrome: Case Report and Deep In Silico Analysis.

Biomedicines·2025

Search research articles

Related Experiment Video

Updated: Jun 25, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

BaRDIC: robust peak calling for RNA-DNA interaction data.

Dmitry E Mylarshchikov¹, Arina I Nikolskaya¹, Olesja D Bogomaz¹

¹Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia.

NAR Genomics and Bioinformatics

|May 22, 2024

Summary

A new computational method, BaRDIC (Binomial RNA-DNA Interaction Caller), accurately identifies specific RNA-DNA interactions from genome-wide sequencing data. It addresses key biases, improving peak detection for non-coding RNA targeting studies.

More Related Videos

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

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

Related Experiment Videos

Last Updated: Jun 25, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

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

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Non-coding RNAs (ncRNAs) are crucial regulators of cellular processes, interacting with specific genomic loci.
Genome-wide sequencing (NGS) methods like 'one-to-all' and 'all-to-all' detect these RNA-DNA interactions but are susceptible to biases and noise.
Accurate identification of specific RNA-DNA interaction 'peaks' is essential for understanding ncRNA function.

Purpose of the Study:

To develop a novel computational tool, BaRDIC (Binomial RNA-DNA Interaction Caller), for robust peak detection in RNA-DNA interaction data.
To address inherent biases in NGS data, including chromatin heterogeneity and distance-dependent decay of interaction frequency.
To improve the accuracy and reliability of identifying genomic targets of non-coding RNAs.

Main Methods:

BaRDIC employs a binomial model tailored for RNA-DNA interaction data.
It uniquely accounts for chromatin heterogeneity and distance-dependent decay of interaction frequency.
The method adaptively determines peak sizes based on individual RNA abundances and contact patterns.

Main Results:

BaRDIC demonstrates superior performance in peak calling compared to existing algorithms.
It effectively handles the sparsity characteristic of 'all-to-all' RNA-DNA interaction datasets.
The tool provides more robust predictions of RNA-genomic target interactions.

Conclusions:

BaRDIC represents a significant advancement in analyzing RNA-DNA interaction data.
It offers a more accurate and reliable method for identifying ncRNA genomic targets.
The freely available BaRDIC package facilitates further research in ncRNA function and regulation.