Body: <p>I have to reproduce a result for school reasons (bioinformatics exam) and I was assigned a certain article. In particular I have to reproduce what some authors did in: doi: 10.1371 / journal.pone.0217889. From what I understand, they make functional enrichments in respect of three variants of protein disorder. They consider vectors whose components are proteins (belonging to a certain type of disorder flavour) in a functional class divided by the total number of proteins in that functional variant. The problem is that if I go for an enrichment in GO terms for a certain set of proteins, the same GO terms do not necessarily come out for another set of proteins. So my question is: in relation to the paragraph 'Clustering of the variants of disorder through functional profiles' how do I construct the vectors that they then use in clustering?</p>
  <p>Thanks in advance</p>
  <p>The full quote to the article is
  Deiana A, Forcelloni S, Porrello A, Giansanti A. Intrinsically disordered proteins and structured proteins with intrinsically disordered regions have different functional roles in the cell. PLoS One. 2019; 14 (8): e0217889. Published 2019 Aug 19. doi: 10.1371 / journal.pone.0217889</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-18T00:17:27.640Z
Comments.Text: I feel like it's for a given GO term, count the number of proteins belonging to a protein variant group in that GO term and divide it by the total number of proteins belonging to that variant.
Comments.UserDisplayName: Phoenix Mu
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-18T20:45:19.617Z
Comments.Text: Yep:up to there no problem. The problem is that when one feeds sets of different proteins, the GO terms are not comparable. I had an idea: at this point it would be better to take the entire proteome and see how it is noted and then divide by protein variants. At that point, GO labels would be obtained with which to build three vectors of GO terms. I'm doing so then I don't know how close it comes to the strategy adopted by the authors also because PANTHER is a bit dated DB and to be honest I don't like it very much. I think I will use g: profiler
Comments.UserDisplayName: Spartan 117
Tags: <go-enrichment>
Title: How to Interpret Author's Methods in this article?
ThreadText: # How to Interpret Author's Methods in this article?
  
  (Asked by: username_0 on 2021-02-17)
  
  I have to reproduce a result for school reasons (bioinformatics exam) and I was assigned a certain article. In particular I have to reproduce what some authors did in: doi: 10.1371 / journal.pone.0217889. From what I understand, they make functional enrichments in respect of three variants of protein disorder. They consider vectors whose components are proteins (belonging to a certain type of disorder flavour) in a functional class divided by the total number of proteins in that functional variant. The problem is that if I go for an enrichment in GO terms for a certain set of proteins, the same GO terms do not necessarily come out for another set of proteins. So my question is: in relation to the paragraph 'Clustering of the variants of disorder through functional profiles' how do I construct the vectors that they then use in clustering?
  
  Thanks in advance
  
  The full quote to the article is
  Deiana A, Forcelloni S, Porrello A, Giansanti A. Intrinsically disordered proteins and structured proteins with intrinsically disordered regions have different functional roles in the cell. PLoS One. 2019; 14 (8): e0217889. Published 2019 Aug 19. doi: 10.1371 / journal.pone.0217889
  
  **Tags:** go-enrichment
  
  > **Comments:**
  > **username_1**: I feel like it's for a given GO term, count the number of proteins belonging to a protein variant group in that GO term and divide it by the total number of proteins belonging to that variant.  
  > **username_0**: Yep:up to there no problem. The problem is that when one feeds sets of different proteins, the GO terms are not comparable. I had an idea: at this point it would be better to take the entire proteome and see how it is noted and then divide by protein variants. At that point, GO labels would be obtained with which to build three vectors of GO terms. I'm doing so then I don't know how close it comes to the strategy adopted by the authors also because PANTHER is a bit dated DB and to be honest I don't like it very much. I think I will use g: profiler  
  
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  ## 0 answers
  
  