RNA-Seq reveals roles for AINTEGUMENTA and AINTEGUMENTA-LIKE6 in cell wall remodeling and plant defense pathways in Arabidopsis
This repository contains data files and (mostly) R code used to analyze an RNA-Seq data set from flowers collected from wild-type and ant ail6 double mutant plants. Our goal was to identify targets (genes and
pathways) regulated by DNA binding proteins ANT and AIL6, which play important roles in plant growth and development.
About this project
This project is a collaboration between the Krizek lab at University
of South Carolina and the Loraine Lab at the North Carolina Research
Campus in Kannapolis.
A grant from the US National Science Foundation to PI Krizek and Co-PI
Ann Loraine funded this work.
About this repository
This repository contains analysis folders named for the type of data
analysis code and results they contain.
Most analysis folders contain:
- .Rproj file - an RStudio project file. Open this in RStudio to re-run project code.
- .Rmd files - R Markdown files that contain an analysis focused on answering one or two discrete questions. These are typically formatted like miniature research articles, with Introduction, Results/Analysis, and Conclusions sections.
- .html files - output from running knitr on .Rmd files.
- results folders - contain files (typically tab-delimited) and images produced by .Rmd files.
- data - data files obtained from a compute cluster or other external source upstream of this data analysis
- src - R, python, or perl scripts used for simple data processing tasks
- README.md - documentation
Code used for analysis, making figures, and making data files for
visualization in external site and programs are grouped into folders
with names indicating their common purpose or analysis type.
Each module folder is designed to be run as a mostly
self-contained project in RStudio. As such, each folder contains an
".Rproj" file. To run the code in RStudio, just open the .Rproj file
and go from there. However, note that the code expects Unix-style file
paths and depends on external libraries, mainly from Bioconductor.
Some modules depend on the output of other modules. Some modules also
depend on externally supplied data files, which are version-controlled
in ExternalDataSets but may also be available from external sites.
Analysis modules and other directories include .Rmd files (R
Markdown), output from knitting Rmd files in HTML (Web pages), folders
containing results (results) and (sometimes) folders containing
externally supplied data used only in one module (data). Results folders
also contain high-quality image files suitable for inclusion in slides or publications.
Used IGB to examine read alignments overlapping AIL6 in the
Look here for SAM files containing read alignments for mutant
and wild-type samples. You can open these in IGB.
Identifies alternatively spliced genes whose splicing patterns are
different between mutant and wild-type samples.
Look here for plots comparing percent-spliced in and other alternative
splicing statistics between sample types.
This module investigates differential expression of genes encoding
metabolic pathway enzymes annotated in the AraCyc database.
Look here for files that can be uploaded into the AraCyc Omics viewer.
This module also contains code useful for converting AraCyc annotation
files to a format that can be read into GOSeq for pathway enrichment
Makes counts, RPM, and RPKM files. Contains code for
- processing counts data (Counts.Rmd)
- making RPM and RPKM files (MakeRpmRpkmFiles.Rmd)
- plotting expression levels (PlottingExpression.Rmd)
Look in Counts/results for RPM, RPKM, and other counts-related results files.
Identifies differentially expressed genes using tools and libraries
Look here for results from differential expression analysis.
Contains gene annotations and other data sets downloaded from IGBQuickLoad.org, GeneOntology.org, and other sources.
Identifies over and under represented GO categories among differentially expressed genes using the GOSeq library from BioConductor.
Same as GOSeq, but used on-line tools GOrilla and AgriGO.
Contains genome browser images made using Integrated Genome Browser.
- Ann Loraine email@example.com
- Beth Krizek firstname.lastname@example.org
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