Analysis and visualization of RNA-Seq expression data using Rstudio, bioconductor, and integrated genome browser

Ann E. Loraine, Ivory Clabaugh Blakley, Sridharan Jagadeesan, Jeff Harper, Gad Miller, Nurit Firon

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

Sequencing costs are falling, but the cost of data analysis remains high, often because unforeseen problems arise, such as insuffi cient 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 fi rst step toward building RNA-Seq data analysis expertise in a research group.

Original languageEnglish
Pages (from-to)481-501
Number of pages21
JournalMethods in Molecular Biology
Volume1284
DOIs
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media New York 2015.

Keywords

  • Differential gene expression
  • Integrated genome browser
  • Pollen
  • R
  • RNA-Seq
  • Tomato
  • Visualization
  • edgeR

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