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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...

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Related Experiment Video

Updated: Sep 12, 2025

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

Building Portable and Reproducible Cancer Informatics Workflows for Scalable Data Analysis: An RNA Sequencing

Rowan F Beck¹, Zelia F Worman¹, Gaurav Kaushik²

¹Velsera, Charlestown, MA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|August 8, 2025

Summary

This summary is machine-generated.

Cloud platforms now enable accessible genomics data analysis for researchers worldwide. This tutorial shows how to use the Cancer Genomics Cloud (CGC) for RNA sequencing analysis, promoting reproducible and collaborative biomedical research.

Keywords:

AWS Bioinformatics CWL Cancer informatics Cloud Docker Reproducibility Software design TCGA Workflows

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Last Updated: Sep 12, 2025

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

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Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Decreasing sequencing costs generate vast high-throughput data.
Cloud technologies and interoperability standards facilitate data sharing and analysis.
Genomic data analysis is now accessible beyond institutions with large local computational resources.

Purpose of the Study:

To demonstrate the analysis of RNA sequencing data using the Seven Bridges Cancer Genomics Cloud (CGC).
To illustrate how to integrate new computational algorithms into existing workflows on a cloud platform.
To highlight best practices for reproducible and scalable biomedical computation in the cloud.

Main Methods:

Utilizing the Seven Bridges Cancer Genomics Cloud (CGC) for data analysis.
Analyzing RNA sequencing data from the NIH Cancer Research Data Commons (CRDC).
Developing and implementing command line tools, Docker containers, and CWL descriptions for cloud-based analysis.

Main Results:

Successful execution of an RNA sequencing analysis on the cloud.
Demonstration of integrating a new computational algorithm into an existing workflow.
Highlighting best practices for creating reproducible and massively parallelized biomedical computations.

Conclusions:

The Cancer Genomics Cloud (CGC) simplifies large-scale genomic data handling.
Cloud-based platforms promote interdisciplinary collaboration in biomedical research.
Best practices enable reproducible and efficient analysis of diverse experimental data.