...
- Use bedtools to count reads in features.
- Converting mapped reads to feature counts.
The data files for this example are in the path:
| Code Block |
|---|
/corral-repl/utexas/BioITeam/ngs_course/ecoli_rnaseq
|
File Name | Description |
|---|---|
| Illumina reads, 0K generation individual clone from population |
| Illumina reads, 20K generation mixed population |
| Illumina reads, 40K generation mixed population |
| E. coli B str. REL606 genome |
hg3. Using DESeq
| Code Block | ||
|---|---|---|
| ||
login1$ R
...
>source("http://bioconductor.org/biocLite.R")
>biocLite("DESeq")
|
| Code Block | ||
|---|---|---|
| ||
login1$ R
...
> library("DESeq")
> combined = read.csv("combined.csv", header=T, row.names=1)
> design <- data.frame(
row.names = colnames( combined ),
condition = c( "Anc", "Anc", "EL", "EL", "EW", "EW"),
libType = c( "single-end", "single-end", "single-end",
"single-end", "single-end", "single-end" ) )
> design
> conds <- factor(design$condition)
> cds <- newCountDataSet( combined, conds )
> cds <- estimateSizeFactors( cds )
> sizeFactors( cds )
> res <- nbinomTest( cds, "EL", "EW" )
> write.csv(res, "EL-vs-EW.csv")
|