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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: Jun 21, 2025

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

Published on: April 25, 2015

Reusable tutorials for using cloud-based computing environments for the analysis of bacterial gene expression data

Steven Allers¹, Kyle A O'Connell^2,3, Thad Carlson^2,3

¹Department of Molecular and Biomedical Sciences, University of Maine, 5735 Hitchner Hall, Orono, ME 04469, United States.

Briefings in Bioinformatics

|July 12, 2024

Summary

This summary is machine-generated.

This study developed interactive RNA sequencing (RNAseq) data analysis tutorials on the Google Cloud Platform. These open-source resources aim to improve access to bioinformatics tools and cloud computing training for researchers.

Keywords:

RNA sequencing analysis workflow cloud computing gene expression microbial genomics training

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Last Updated: Jun 21, 2025

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biomedical research is increasingly data-driven, necessitating robust data management and analysis.
Cloud computing offers opportunities to expand bioinformatics applications but presents access and training challenges for researchers, especially at smaller institutions.

Purpose of the Study:

To develop a resource module for the NIGMS Sandbox for Cloud-based Learning platform.
To provide interactive learning materials for RNA sequencing (RNAseq) data analysis.
To address obstacles in accessing tailored bioinformatics environments and cloud computing training.

Main Methods:

Developed five reusable tutorials for bulk RNA sequencing data analysis.
Utilized Jupyter notebooks on the Google Cloud Platform for interactive workflows.
Employed an RNAseq dataset from a study on prophage altered drug resistance in Mycobacterium chelonae.

Main Results:

Tutorials guide users through RNAseq data analysis, starting with a data subset and progressing to the full dataset.
Included analysis of read count data for differentially expressed genes using R/DESeq2.
Generated read counts using Snakemake and Nextflow with Google Batch.

Conclusions:

The developed tutorials are open-source and serve as templates for RNAseq data analysis.
The NIGMS Sandbox module enhances accessibility to cloud-based bioinformatics tools and training.
Facilitates rigorous, robust, and reproducible biomedical research through improved data analysis capabilities.