I generated count data with count feature tool. How do I normalize the reads to total reads per sample using galaxy?
Thank you
Welcome, @Gulcin
I don’t think it is possible with this tool. What is the goal?
Thank you for responding. I am new in this, any help is highly appreciated 
I managed to align my reads and then obtain count data. The count per gene varies between replicates, I wanted to normalize it. How shall I proceed to obtain DeSeq data?
Hi @Gulcin
Great, thanks for explaining!
From Featurecounts you can head directly to DESeq2.
We have several examples in the Galaxy Training Network (GTN) tutorials.
You can find these tutorials here.
Then navigate by analysis domain, or explore Pathways.
Or, scroll to the bottom of tool forms to find tutorials that include the target tool.
- GTN Tutorials for featureCounts: Measure gene expression in RNA-Seq experiments from SAM or BAM files
- GTN Tutorials for DESeq2: Determines differentially expressed features from count tables
With a bit of extra help in FAQs at the same training site plus user troubleshooting examples and discussion at this forum.
- FAQ: Extended Help for Differential Expression Analysis Tools
- And tags based on tool names featurecounts, deseq2 or domain transcriptomics, single-cell or data types like reference-genome reference-annotation reference-transcriptome
So that is a lot of information! I would suggesting starting here.
Then if you are completely brand new to Galaxy, or are curious about how to use our workflows, this short introduction tutorial will help to get you oriented.
- Hands-on: Galaxy Basics for everyone / Galaxy Basics for everyone / Introduction to Galaxy Analyses
- Tip: if you load up the starting data, and the tutorial’s workflow, you can launch it to get a quick idea of how easy workflows are to use. Most tutorials can be used this way.
Hope this helps and you can ask follow up questions! 