Answers.Body: <p>I would start with metaplots for a visual/exploratory analysis to guide your statistical testing.  Cut the genome into short segments e.g. centred on the centre of each nucleosome peak, overlay all the segments on top of each other, and plot the abundance of secondary structure features at each base count as you step away from the centres.  This should give an easy view on whether there is any clear structure in the relative positions of the different objects.</p>
  
  <p>Do the plots both ways round, and try different subsets of the objects (e.g. ones overlapping genes or not, different stringency thresholds for peak calling).  This should hopefully give you some insights on what is worth testing for.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-28T11:27:48.117Z
Answers.LastActivityDate: 2019-06-28T11:27:48.117Z
Answers.UserDisplayName: Jay Moore
Answers.Body: <p>You can express the annotations of the genome with a dot plot, x and y represent the two annotations along the genome. The you can summary the result with Spearman correlation coefficient(s) based on whole set of annotations or particular gene families.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-27T04:39:45.553Z
Answers.LastActivityDate: 2019-06-27T04:39:45.553Z
Answers.UserDisplayName: Zhi-Ping Feng
Body: <p>I have one-dimentional array (human genome). Also I have two annotations for it, we can think about them as different peaks (it's nucleosome and secondary structures). How can we find correlation and/or causation between this annotations? Currently we are forming hypothesis like "one element is always before other" and statistically testing them. Can we be smarter and use known tools or at least automate this? </p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-17T20:26:22.620Z
Comments.Text: Do you mean with something like a Markov model?
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-17T21:32:15.353Z
Comments.Text: Yeah, but how will you implement them?
Comments.UserDisplayName: D M
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-18T08:33:36.410Z
Comments.Text: I am also interested in applying such models to multiple 'omics data, I hope this question will get some useful answers.
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-21T11:44:49.380Z
Comments.Text: Could you specify which kind of hypothesis do you want to test? What I have seen is centering all the positions you are interested in and observe if there is a common pattern on the other variables observed (annotations, CpG, mutation rates...) which is not noise. See [here](https://bbglab.irbbarcelona.org/wp-content/uploads/2016/10/Capture-1.png) an example
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-12-19T10:34:35.183Z
Comments.Text: Perhaps phrase this as time series analysis? Does one event precede another in a statistical sense? I think if you look for tools in this domain, you'll find some good solutions.
Comments.UserDisplayName: Dan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-07-01T08:51:48.530Z
Comments.Text: See answers to this plot https://bioinformatics.stackexchange.com/questions/83/how-to-correlate-two-zero-inflated-bedgraph-like-signals?rq=1
Comments.UserDisplayName: Ian Sudbery
Tags: <genome><r><sequence-annotation>
Title: Comparing two genome annotations
ThreadText: # Comparing two genome annotations
  
  (Asked by: username_0 on 2017-11-17)
  
  I have one-dimentional array (human genome). Also I have two annotations for it, we can think about them as different peaks (it's nucleosome and secondary structures). How can we find correlation and/or causation between this annotations? Currently we are forming hypothesis like "one element is always before other" and statistically testing them. Can we be smarter and use known tools or at least automate this?
  
  **Tags:** genome, r, sequence-annotation
  
  > **Comments:**
  > **username_1**: Do you mean with something like a Markov model?  
  > **username_0**: Yeah, but how will you implement them?  
  > **username_1**: I am also interested in applying such models to multiple 'omics data, I hope this question will get some useful answers.  
  > **username_2**: Could you specify which kind of hypothesis do you want to test? What I have seen is centering all the positions you are interested in and observe if there is a common pattern on the other variables observed (annotations, CpG, mutation rates...) which is not noise. See [here](https://bbglab.irbbarcelona.org/wp-content/uploads/2016/10/Capture-1.png) an example  
  > **username_3**: Perhaps phrase this as time series analysis? Does one event precede another in a statistical sense? I think if you look for tools in this domain, you'll find some good solutions.  
  > **username_4**: See answers to this plot https://bioinformatics.stackexchange.com/questions/83/how-to-correlate-two-zero-inflated-bedgraph-like-signals?rq=1  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_5 on 2019-06-28. Score: 1
  
  I would start with metaplots for a visual/exploratory analysis to guide your statistical testing. Cut the genome into short segments e.g. centred on the centre of each nucleosome peak, overlay all the segments on top of each other, and plot the abundance of secondary structure features at each base count as you step away from the centres. This should give an easy view on whether there is any clear structure in the relative positions of the different objects.
  
  Do the plots both ways round, and try different subsets of the objects (e.g. ones overlapping genes or not, different stringency thresholds for peak calling). This should hopefully give you some insights on what is worth testing for.
  
  
  ---
  
  ### Answer by username_6 on 2019-06-27. Score: 1
  
  You can express the annotations of the genome with a dot plot, x and y represent the two annotations along the genome. The you can summary the result with Spearman correlation coefficient(s) based on whole set of annotations or particular gene families.
  
  
  ---
  
  