Loraine, A.E., Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
Blakley, I.C., Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
Jagadeesan, S., Department of Vegetable Research, Institute of Plant Sciences, Agricultural Research Organization, Bet Dagan, Israel
Harper, J., Department of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, United States
Miller, G., The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
Firon, N., Department of Vegetable Research, Institute of Plant Sciences, Agricultural Research Organization, Bet Dagan, Israel
Loraine, A.E.
Blakley, I.C.
Jagadeesan, S.
Harper, J.
Miller, G.
Firon, N.

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 and RStudio, user-friendly tools for statistical analysis and reproducible research in bioinformatics, to analyze and document the analysis of an example RNA-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. © Springer Science+Business Media New York 2015. All rights are reserved.