Respected sir, Currently, I am working on chicken ovarian transcriptomics data analysis. Since I am new to this, I am following your previously published paper (Beginner’s guide to using the DESeq2 package) for analysing my data. So far I have followed few steps which I have mention below. Further, I don't know which steps I have to follow. If you help, it will be greatly helpful for me.

Sincerely KAMALAKKANNAN R

Note:

library("DESeq2")
library("GenomicFeatures")
chicken <- makeTxDbFromGFF("F:/ChickenTranscrptomics data analysis/Chicken/Gallus.gff", format="gff")
exonsByGene <- exonsBy(chicken, by="gene")
fsl <- list.files("F:/Duck Transcrptomics data analysis/Chicken/BAM", pattern="bam$", full=TRUE)
library("Rsamtools")
bamLt <- BamFileList(fsl, yieldSize=100000)
library("GenomicAlignments")
kamal <- summarizeOverlaps(exonsByGene, bamLt, mode="Union", singleEnd=TRUE, ignore.strand=TRUE, fragments=FALSE)
colData(kamal)
rowData(kamal)
tail(assay (kamal))
head(assay (kamal))
sample <- read.csv("F:/ChickenTranscrptomics data analysis/Chicken.csv")
head( sample )
head( colnames(kamal) )
sample1 <- DataFrame(sample)
sample1
id <- match(colnames(kamal), sample1$run)
id
head( cbind( colData(kamal)[, 1:2 ], sample1 [ id, ] ) )
colData(kamal) <- cbind( colData(kamal), sample1[ id, ] )

On the support site I can provide specific software issues related to DESeq2, if you have a need for general bioinformatic support, I suggest you reach out to a bioinformatic collaborator.

The workflow and vignette both provide detailed documentation and guidance.