<p>The way we do this (and I think its quite common?), is to merge all the files of the same condition (taking the same number of reads from each), and then calling peaks on the merged sample - so you would have two peak sets - one from normal and one from disease. You then do the union of the two peak sets. </p>

<p>This makes annotation much easier.</p>

<p>For downstream quantitative analysis, you can then quantitate each individual sample against the merged peak set. </p>

CC BY-SA 4.0
2019-11-14T14:49:54.043Z
since i have already done with the alignment part as you suggested this part "taking the same number of reads from each)," right now i can do but i want to have some preliminary result .So would it be wrong to merge peal files from similar condition and go ahead ?
kcm
CC BY-SA 4.0
2019-11-14T15:04:59.620Z
Not "wrong" as such, but you'll get more peaks from the samples that have more reads, which is a bias in the analysis.
Ian Sudbery
CC BY-SA 4.0
2019-11-14T19:20:06.087Z
I have a doubt , so i do see in paper where they cluster these chip seq or atac seq data.So is it just the tag count data which can be used or it needs some kind of normalisation? just in case of rna seq i use rlog or vst data for clustering or PCA ,which i do get from deseq.
kcm
CC BY-SA 4.0
2019-11-14T22:52:09.220Z
we've successfully used rlog in the past.
Ian Sudbery
CC BY-SA 4.0
2019-11-15T05:13:53.587Z
fro ATAC seq how do I get rlog values as i see only tag counts or normalized tag counts
kcm
CC BY-SA 4.0
2019-11-15T05:31:09.023Z
"their ATAC-seq signal was divided by scaling factors representing a median of signal ratios over a complete set of high-confidence ATAC-seq peaks (calculated with the function estimateSizeFactorsForMatrix,from DESeq2 v.1.20.0, Love et al. 2014)". Now Im confused how it can be done in deseq2 as it requires count matrix for analysis ,after homer analysis i get tag count which are not like raw read counts.
kcm
CC BY-SA 4.0
2019-11-15T10:34:44.343Z
We take the union peak set as I described above, and then run featureCounts with the files of tags and the union peaks this will give you raw read counts. You need to make sure that featureCounts is configured properly so that it only uses the first base from each read.
Ian Sudbery
CC BY-SA 4.0
2019-11-15T10:45:30.153Z
"You need to make sure that featureCounts is configured properly so that it only uses the first base from each read" , can i get a more programmatic insight to it ,for rna seq data i do run featurecounts on bam file and get reads. So how do i do it here any resources or examples it would be really helpful for me..
kcm
CC BY-SA 4.0
2019-11-14T11:35:02.673Z
2019-11-14T11:35:02.673Z
Ian Sudbery
<p>I have 7 different cell types, 4 normal cell types and 3 disease cell types. 
I have done until peak calling. Now I want to do peak annotation.</p>

<p>The sample I have is all biological replicates. Should I merge the peak file and then do peak annotation, or should I be doing individual peak annotation? </p>

<atac-seq><homer>
ATAC-seq peak annotation and downstream analysis
# ATAC-seq peak annotation and downstream analysis

(Asked by: username_0 on 2019-11-14)

I have 7 different cell types, 4 normal cell types and 3 disease cell types.
I have done until peak calling. Now I want to do peak annotation.

The sample I have is all biological replicates. Should I merge the peak file and then do peak annotation, or should I be doing individual peak annotation?

**Tags:** atac-seq, homer

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## 1 answers

### Answer by username_1 on 2019-11-14. Score: 1 | Accepted answer

The way we do this (and I think its quite common?), is to merge all the files of the same condition (taking the same number of reads from each), and then calling peaks on the merged sample - so you would have two peak sets - one from normal and one from disease. You then do the union of the two peak sets.

This makes annotation much easier.

For downstream quantitative analysis, you can then quantitate each individual sample against the merged peak set.

> **Comments:**
> **username_0**: since i have already done with the alignment part as you suggested this part "taking the same number of reads from each)," right now i can do but i want to have some preliminary result .So would it be wrong to merge peal files from similar condition and go ahead ?  
> **username_1**: Not "wrong" as such, but you'll get more peaks from the samples that have more reads, which is a bias in the analysis.  
> **username_0**: I have a doubt , so i do see in paper where they cluster these chip seq or atac seq data.So is it just the tag count data which can be used or it needs some kind of normalisation? just in case of rna seq i use rlog or vst data for clustering or PCA ,which i do get from deseq.  
> **username_1**: we've successfully used rlog in the past.  
> **username_0**: fro ATAC seq how do I get rlog values as i see only tag counts or normalized tag counts  
> **username_0**: "their ATAC-seq signal was divided by scaling factors representing a median of signal ratios over a complete set of high-confidence ATAC-seq peaks (calculated with the function estimateSizeFactorsForMatrix,from DESeq2 v.1.20.0, Love et al. 2014)". Now Im confused how it can be done in deseq2 as it requires count matrix for analysis ,after homer analysis i get tag count which are not like raw read counts.  
> **username_1**: We take the union peak set as I described above, and then run featureCounts with the files of tags and the union peaks this will give you raw read counts. You need to make sure that featureCounts is configured properly so that it only uses the first base from each read.  
> **username_0**: "You need to make sure that featureCounts is configured properly so that it only uses the first base from each read" , can i get a more programmatic insight to it ,for rna seq data i do run featurecounts on bam file and get reads. So how do i do it here any resources or examples it would be really helpful for me..  

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