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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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Analysis of RNA Sequencing Data Using CLC Genomics Workbench.

Chia-Hsin Liu¹, Y Peter Di²

¹Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, USA.

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

|January 29, 2020

Summary

This summary is machine-generated.

RNA sequencing (RNA-seq) offers accurate transcriptome profiling for complex biological questions. This guide details RNA-seq data analysis and pathway analysis using specialized software for interpretable results.

Keywords:

CLC Genomic Workbench Ingenuity pathway analysis (IPA)RNA sequencing (RNA-seq)

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Last Updated: Dec 29, 2025

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Published on: February 13, 2013

Area of Science:

Genomics
Bioinformatics

Background:

RNA sequencing (RNA-seq) is an advanced method for transcriptome profiling using next-generation sequencing (NGS).
RNA-seq accurately measures transcript levels and isoforms, crucial for understanding complex transcriptomes.
Decreasing costs and increasing data availability support RNA-seq for hypothesis generation.

Purpose of the Study:

To demonstrate a workflow for analyzing RNA sequencing data.
To guide users in generating interpretable results from RNA-seq experiments.
To showcase downstream pathway analysis using Ingenuity Pathway Analysis (IPA).

Main Methods:

Utilizing CLC Genomics Workbench software for RNA-seq data analysis.
Applying Ingenuity Pathway Analysis (IPA) for downstream pathway analysis.
Focusing on generating interpretable results from sequencing data.

Main Results:

Provides a clear methodology for RNA-seq data analysis.
Enables the generation of interpretable transcriptomic data.
Facilitates downstream pathway analysis for biological insights.

Conclusions:

RNA-seq is a powerful tool for transcriptome profiling and hypothesis generation.
CLC Genomics Workbench and IPA are effective tools for RNA-seq data analysis and interpretation.
This approach aids researchers in addressing complex biological questions through transcriptomic analysis.