Answers.Body: <p>This is something that Qiagen's <a href="https://www.qiagen.com/us/shop/analytics-software/biological-data-tools/ingenuity-pathway-analysis/" rel="nofollow noreferrer">Ingenuity Pathway Analysis</a> is meant to solve. Unfortunately, the process of solving pathway dependency problems like these is difficult, and frequently leads to the discovery of super-linked proteins that are seen everywhere (and consequently fairly uninteresting from a biological perspective). The challenge is in filtering out the useless noise of "statistical significance", and identifying the biologically-significant signal. That's not something that computers are particularly good at doing at the moment.</p>
  
  <p>My own suggestion is that you would be better off finding someone who has domain knowledge in the area of secreted proteins, and trying to present results to them in such a way that it makes it easy to interpret the results and ask further questions. If you are that person, perhaps there's a more specific question that you could ask to further investigate the problem that you have.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-01T09:41:16.277Z
Answers.LastActivityDate: 2018-01-01T09:41:16.277Z
Answers.UserDisplayName: gringer
Body: <p>I have not found any work which investigates assessment of differences in levels of secreted proteins by taking advantage of differential expression of the genes which mediate the secretory pathway.</p>
  
  <p>For example, suppose the gene whose product binds specifically to a certain signal peptide is down-regulated. I would assume that this implies a down-regulation in all the levels of secreted proteins to which it binds.</p>
  
  <p>Is such an analysis possible to date? If so, where? </p>
  
  <p>Thanks in advance.</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-01-01T11:43:17.543Z
Comments.Text: One big problem is that expression levels don't always end up the same way: there are post-transcriptional changes that interfere with the final function. Finding this regulations is hard to do only using RNA-seq/expression data, and even less easy to understand what do they mean. That's why metabolomic analysis are useful, to understand what compounds are available.
Comments.UserDisplayName: llrs
Tags: <proteins><differential-expression><networks><interactions>
Title: Using signal peptide and the expression levels of signal recognition particle in secretome analysis
ThreadText: # Using signal peptide and the expression levels of signal recognition particle in secretome analysis
  
  (Asked by: username_0 on 2017-08-17)
  
  I have not found any work which investigates assessment of differences in levels of secreted proteins by taking advantage of differential expression of the genes which mediate the secretory pathway.
  
  For example, suppose the gene whose product binds specifically to a certain signal peptide is down-regulated. I would assume that this implies a down-regulation in all the levels of secreted proteins to which it binds.
  
  Is such an analysis possible to date? If so, where?
  
  Thanks in advance.
  
  **Tags:** proteins, differential-expression, networks, interactions
  
  > **Comments:**
  > **username_1**: One big problem is that expression levels don't always end up the same way: there are post-transcriptional changes that interfere with the final function. Finding this regulations is hard to do only using RNA-seq/expression data, and even less easy to understand what do they mean. That's why metabolomic analysis are useful, to understand what compounds are available.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2018-01-01. Score: 1
  
  This is something that Qiagen's [Ingenuity Pathway Analysis](https://www.qiagen.com/us/shop/analytics-software/biological-data-tools/ingenuity-pathway-analysis/) is meant to solve. Unfortunately, the process of solving pathway dependency problems like these is difficult, and frequently leads to the discovery of super-linked proteins that are seen everywhere (and consequently fairly uninteresting from a biological perspective). The challenge is in filtering out the useless noise of "statistical significance", and identifying the biologically-significant signal. That's not something that computers are particularly good at doing at the moment.
  
  My own suggestion is that you would be better off finding someone who has domain knowledge in the area of secreted proteins, and trying to present results to them in such a way that it makes it easy to interpret the results and ask further questions. If you are that person, perhaps there's a more specific question that you could ask to further investigate the problem that you have.
  
  
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