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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 and visualization of RNA-Seq expression data using RStudio, Bioconductor, and Integrated Genome Browser.

Ann E Loraine¹, Ivory Clabaugh Blakley, Sridharan Jagadeesan

¹Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Charlotte, NC, USA, aloraine@uncc.edu.

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

|March 12, 2015

Summary

This study presents a protocol for analyzing RNA-Seq data using R Markdown and RStudio, addressing high data analysis costs. It helps researchers anticipate problems and build bioinformatics expertise for experiments like heat-stressed tomato pollen.

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Area of Science:

Bioinformatics
Plant Science
Genomics

Background:

Decreasing sequencing costs contrast with persistently high data analysis expenses.
Unforeseen issues like insufficient sequencing depth or batch effects frequently escalate analysis costs.
Proactive data analysis method experimentation during experimental planning can mitigate unforeseen problems.

Purpose of the Study:

To describe a protocol for analyzing and documenting RNA-Seq data using R Markdown and RStudio.
To demonstrate the application of this protocol using an example dataset of heat-stressed tomato pollen.
To showcase the use of Integrated Genome Browser for visualizing read coverage graphs of differentially expressed genes.

Main Methods:

Utilized R Markdown and RStudio for statistical analysis and reproducible bioinformatics research.
Applied the protocol to an RNA-Seq dataset from tomato pollen subjected to chronic heat stress.
Employed Integrated Genome Browser for visualizing read coverage graphs of differentially expressed genes.

Main Results:

The protocol effectively analyzes and documents RNA-Seq data, facilitating the identification of potential issues.
Demonstrated the practical application of R Markdown and RStudio for bioinformatics analysis in plant science.
Visualizations of read coverage graphs aided in understanding gene expression patterns under heat stress.

Conclusions:

Experimenting with data analysis methods early can prevent costly problems and build essential bioinformatics skills.
This protocol offers a valuable starting point for research groups to develop RNA-Seq data analysis expertise.
The use of user-friendly tools like RStudio and R Markdown enhances reproducibility and accessibility in bioinformatics.