Analysis and Visualization of RNA-Seq Expression Data Using RStudio, Bioconductor, and Integrated Genome Browser, Methods Mol Biol. 2015 Mar, Loraine AE1, Blakley IC, Jagadeesan S, Harper J, Miller G, Firon N.
Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Charlotte, NC, USA, email@example.com.
Sequencing costs are falling, but the cost of data analysis remains high, often because unforeseen problems arise, such as insufficient depth of sequencing or batch effects. Experimenting with data analysis methods during the planning phase of an experiment can reveal unanticipated problems and build valuable bioinformatics expertise in the organism or process being studied. This protocol describes using R Markdown andRStudio, user-friendly tools for statistical analysis and reproducible research in bioinformatics, to analyze and document the analysis of an exampleRNA-Seq data set from tomato pollen undergoing chronic heat stress. Also, we show how to use Integrated Genome Browser to visualize read coverage graphs for differentially expressed genes. Applying the protocol described here and using the provided data sets represent a useful first step toward building RNA-Seq data analysis expertise in a research group.
- [PubMed – in process]