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...

You might also read

Related Articles

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

Sort by

Same author

Enhanced IGFL1 translation in response to IL-1β is controlled by distinct 3'UTR elements.

PloS one·2026

Same author

Autoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniques.

GigaScience·2026

Same author

A leader-repeat hairpin blocks extraneous CRISPR RNA production in diverse CRISPR-Cas13 systems.

The EMBO journal·2026

Same author

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny.

Nature methods·2026

Same author

Comprehensive analysis of CRISPR array repeat mutations reveals subtype-specific patterns and links to spacer dynamics.

microLife·2026

Same author

The complexity of multiple CRISPR arrays in strains with (co-occurring) CRISPR systems.

microLife·2026

Same journal

EC-isHCR: A rapid method for in situ hybridization chain reaction in diverse animal samples.

Methods (San Diego, Calif.)·2026

Same journal

Single-Molecule methods to investigate mechanisms of transcription by RNA polymerase of Mycobacterium tuberculosis.

Methods (San Diego, Calif.)·2026

Same journal

Detection and sequencing of Usutu virus during mosquito surveillance: Use of multiple assays and techniques for identification at low levels.

Methods (San Diego, Calif.)·2026

Same journal

Experimental validation of an AI-driven digital healthcare platform for oral health behavior and plaque assessment among vietnamese children.

Methods (San Diego, Calif.)·2026

Same journal

Zeta potential: An efficient and cost-effective alternative for investigating cell-surface interactions.

Methods (San Diego, Calif.)·2026

Same journal

An automated workflow for quantifying the formation of synuclein aggregates in human dopaminergic neurons.

Methods (San Diego, Calif.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 6, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Computational analysis of CLIP-seq data.

Michael Uhl¹, Torsten Houwaart¹, Gianluca Corrado²

¹Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany.

Methods (San Diego, Calif.)

|March 4, 2017

Summary

This summary is machine-generated.

RNA binding protein (RBP) CLIP-seq experiments identify genome-wide binding sites. Computational analysis involves pre-processing, peak calling, and crucial binding model determination to improve accuracy and reduce false negatives.

Keywords:

CLIP-seq data analysis Peak calling RBP binding models RBP binding site prediction

More Related Videos

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

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Related Experiment Videos

Last Updated: Mar 6, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

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

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

CLIP-seq is a key method for mapping RNA binding protein (RBP) interactions genome-wide.
Computational analysis of CLIP-seq data is essential for identifying protein binding sites on RNA.
Standard analysis pipelines often have limitations in accuracy and scope.

Purpose of the Study:

To outline the computational workflow for CLIP-seq data analysis.
To highlight the importance of specific pre-processing and peak-calling strategies.
To emphasize the necessity of developing binding models beyond raw peak calling for robust results.

Main Methods:

Pre-processing of raw sequencing reads, including trimming and genome mapping, tailored to specific CLIP-seq protocols.
Peak calling to identify significant protein-RNA binding sites, with consideration for protocol-specific and generic tools.
Development and application of binding models to predict putative binding sites, complementing direct peak identification.

Main Results:

CLIP-seq data analysis requires careful protocol adaptation during read pre-processing.
Multiple peak-calling strategies exist, each with unique advantages and disadvantages, necessitating comparative analysis.
Binding models significantly enhance the accuracy of RBP binding site identification and mitigate false negatives.

Conclusions:

A comprehensive computational approach for CLIP-seq data analysis is crucial for accurate RBP binding site determination.
Moving beyond simple peak calling to incorporate binding models is essential for robust biological interpretation.
Optimized computational strategies improve the reliability and scope of findings from CLIP-seq experiments.