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

Toward precision prognosis: Predicting recurrence-free survival in high-grade serous ovarian cancer patients using multi-time point clinical and computed tomography radiomics data.

Gynecologic oncology·2026

Same author

ESR1 mutations and CDK4/6 inhibitor choice shape clonal selection and adaptive cell states during acquired resistance.

Genome medicine·2026

Same author

CistromeMeta: a large language model powered tool for automated ChIP-seq metadata extraction.

Bioinformatics (Oxford, England)·2026

Same author

Epigenetic subtypes of high-grade T1 bladder cancer reveal intra-tumor heterogeneity and distinct interactions with tumor microenvironment.

Theranostics·2026

Same author

Whole-genome doubling drives immune evasion by silencing antigen presentation.

Cancer cell·2026

Same author

Lack of synergy between AR-targeted therapies and PARP inhibitors in homologous recombination-proficient prostate cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

Real-time Targeted Enrichment in Single-cell Long-read Sequencing.

Genomics, proteomics & bioinformatics·2026

Same journal

Decoding RNA N6-Methyladenosine Methylome of Wheat Using Machine Learning and Nanopore Direct RNA Sequencing.

Genomics, proteomics & bioinformatics·2026

Same journal

Tranquillyzer: A Neural Network Framework for Long-read Annotation and Demultiplexing.

Genomics, proteomics & bioinformatics·2026

Same journal

Advancing Functional Transcriptomics in Zebrafish with High-accuracy Full-length RNA Sequencing.

Genomics, proteomics & bioinformatics·2026

Same journal

NanoRAPID: A Deep Learning-based Framework for Single-molecule RNA Structure Analysis Using Nanopore Direct RNA Sequencing.

Genomics, proteomics & bioinformatics·2026

Same journal

Single-cell Multiomic and Spatiotemporal Dissection of the Liver Circadian Clock.

Genomics, proteomics & bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 27, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

CoBRA: Containerized Bioinformatics Workflow for Reproducible ChIP/ATAC-seq Analysis.

Xintao Qiu¹, Avery S Feit², Ariel Feiglin³

¹Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.

Genomics, Proteomics & Bioinformatics

|July 20, 2021

Summary

This summary is machine-generated.

We developed CoBRA, a containerized workflow for reproducible Chromatin Immunoprecipitation sequencing (ChIP-seq) and Assay for Transposase-Accessible Chromatin (ATAC-seq) analysis. This pipeline simplifies complex genomic data analysis for researchers.

Keywords:

ATAC-seq ChIP-seq Docker Snakemake Workflow

More Related Videos

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

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

Related Experiment Videos

Last Updated: Oct 27, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

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

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Chromatin immunoprecipitation sequencing (ChIP-seq) and Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) are crucial for studying protein-DNA interactions and chromatin accessibility.
Existing analysis pipelines often lack scalability, reproducibility, and integrated downstream analysis tools.

Purpose of the Study:

To present Containerized Bioinformatics workflow for Reproducible ChIP/ATAC-seq Analysis (CoBRA), a modular computational pipeline.
To provide a scalable, reproducible, and user-friendly solution for ChIP-seq and ATAC-seq data analysis.

Main Methods:

CoBRA is a modularized computational workflow utilizing containerization for reproducibility.
It quantifies ChIP-seq and ATAC-seq peak regions and integrates unsupervised and supervised analyses.
The pipeline incorporates sample normalization and copy number variation correction.

Main Results:

CoBRA offers a comprehensive, state-of-the-art analysis pipeline for ChIP-seq and ATAC-seq data.
It enables researchers with limited computational expertise to perform advanced analyses.
Key analyses include sample clustering, differential peak calling, motif enrichment, database comparisons, and pathway analysis.

Conclusions:

CoBRA empowers scientists to gain rapid insights into protein-DNA interactions and chromatin accessibility.
The workflow enhances the reproducibility and accessibility of complex genomic data analysis.
CoBRA is publicly available, promoting wider adoption and scientific advancement.