TY - JOUR
T1 - Analysis and visualization of RNA-Seq expression data using Rstudio, bioconductor, and integrated genome browser
AU - Loraine, Ann E.
AU - Blakley, Ivory Clabaugh
AU - Jagadeesan, Sridharan
AU - Harper, Jeff
AU - Miller, Gad
AU - Firon, Nurit
N1 - Publisher Copyright:
© Springer Science+Business Media New York 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Differential gene expression
KW - Integrated genome browser
KW - Pollen
KW - R
KW - RNA-Seq
KW - Tomato
KW - Visualization
KW - edgeR
UR - http://www.scopus.com/inward/record.url?scp=84924891523&partnerID=8YFLogxK
U2 - 10.1007/978-1-4939-2444-8_24
DO - 10.1007/978-1-4939-2444-8_24
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AN - SCOPUS:84924891523
SN - 1064-3745
VL - 1284
SP - 481
EP - 501
JO - Methods in Molecular Biology
JF - Methods in Molecular Biology
ER -