Answers.Body: <p>The only reason to normalize for GC content in RNA-seq is if it differs notably between samples/groups. If that's not the case and you aren't trying to compare genes withing samples then you have no reason to try to account for GC content. The same goes for ATAC-seq, though there the danger with trying to normalize for GC content is that you then mask changes in global accessibility.</p>
  <p>In short, normalize for things that should cause problems with your analysis and nothing else.</p>
  
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Answers.CreationDate: 2020-12-07T20:48:41.910Z
Answers.Text: Now my confusion is cleared
Answers.UserDisplayName: kcm
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Answers.CreationDate: 2020-12-06T07:56:09.673Z
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Answers.UserDisplayName: Devon Ryan
Body: <p>Is it really required to do GC normalization ATAC seq data ?</p>
  <p>One of the paper where they have ATAC seq data did the normaisation of GC bias after peak calling. When i looked for the library and the reason why GC bias i found this</p>
  <pre><code>Could it be true that genes with higher GC content are higher expressed? It has been suggested that genes that are either extremely high or extremely low expressed are under some form of selection leading to “extreme” GC content. What CQN does, is making the effect of GC content comparable across samples, and we show in [1] that this leads to improved inference. It also flattens the effect of GC content on gene expression, but we believe this is better than having the effect of GC content depend on the sample.
  https://www.bioconductor.org/packages/release/bioc/vignettes/cqn/inst/doc/cqn.pdf
  </code></pre>
  <p>Now that scenario is for RNA seq would it be logical to do the same for ATAC seq?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-07T20:50:29.790Z
Comments.Text: " I personally do not do it. It is not established from what I know and I do not see the advantage. " i have the doubt but not the knowledge  if i should do or not ! ..i came across paper where they did that but not sure why ..but now im clear how to proceed thank you and @Devon Ryan for the answer
Comments.UserDisplayName: kcm
Tags: <atac-seq>
Title: GC normalization ATAC seq data
ThreadText: # GC normalization ATAC seq data
  
  (Asked by: username_0 on 2020-12-05)
  
  Is it really required to do GC normalization ATAC seq data ?
  
  One of the paper where they have ATAC seq data did the normaisation of GC bias after peak calling. When i looked for the library and the reason why GC bias i found this
  
  ```
  Could it be true that genes with higher GC content are higher expressed? It has been suggested that genes that are either extremely high or extremely low expressed are under some form of selection leading to “extreme” GC content. What CQN does, is making the effect of GC content comparable across samples, and we show in [1] that this leads to improved inference. It also flattens the effect of GC content on gene expression, but we believe this is better than having the effect of GC content depend on the sample.
  https://www.bioconductor.org/packages/release/bioc/vignettes/cqn/inst/doc/cqn.pdf
  
  ```
  
  Now that scenario is for RNA seq would it be logical to do the same for ATAC seq?
  
  **Tags:** atac-seq
  
  > **Comments:**
  > **username_0**: " I personally do not do it. It is not established from what I know and I do not see the advantage. " i have the doubt but not the knowledge  if i should do or not ! ..i came across paper where they did that but not sure why ..but now im clear how to proceed thank you and @username_1 for the answer  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-12-06. Score: 2 | Accepted answer
  
  The only reason to normalize for GC content in RNA-seq is if it differs notably between samples/groups. If that's not the case and you aren't trying to compare genes withing samples then you have no reason to try to account for GC content. The same goes for ATAC-seq, though there the danger with trying to normalize for GC content is that you then mask changes in global accessibility.
  
  In short, normalize for things that should cause problems with your analysis and nothing else.
  
  > **Comments:**
  > **username_0**: Now my confusion is cleared  
  
  ---
  
  