Answers.Body: <p>The UCSC <a href="https://genome.ucsc.edu/cgi-bin/hgIntegrator" rel="nofollow noreferrer">Data Integrator</a> can combine multiple tracks (CpG Islands, TFBS, cCREs, etc.) into one output annotation file, and it works with any assembly you choose. You can select which output fields you want, then download the tab-delimited file.</p>
  <p>From there, you can use R, Python, and/or GNU tools such as <code>awk</code> and <code>sed</code> to coerce the data into a format which is useful for your purposes. You can then load it into R as a data frame and use the columns as arguments in the GRanges constructor to create a GRanges object.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-22T20:52:13.537Z
Answers.LastActivityDate: 2021-06-22T20:52:13.537Z
Answers.UserDisplayName: ethan-pawl
Body: <p>I want to retrieve an annotation object (as GRanges) containing different UCSC annotation tracks such as CpG Islands, TSS, TFBS and promoters for hg38 Human reference genome. But &quot;UCSC Genes&quot; was phased out for hg38. What could be the best resource for this ?</p>
  <p>Thanks</p>
  <p>RT</p>
  
Tags: <sequence-annotation><ucsc>
Title: Hg38 annotation tracks retrieval
ThreadText: # Hg38 annotation tracks retrieval
  
  (Asked by: username_0 on 2021-06-20)
  
  I want to retrieve an annotation object (as GRanges) containing different UCSC annotation tracks such as CpG Islands, TSS, TFBS and promoters for hg38 Human reference genome. But "UCSC Genes" was phased out for hg38. What could be the best resource for this ?
  
  Thanks
  
  RT
  
  **Tags:** sequence-annotation, ucsc
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-06-22. Score: 1
  
  The UCSC [Data Integrator](https://genome.ucsc.edu/cgi-bin/hgIntegrator) can combine multiple tracks (CpG Islands, TFBS, cCREs, etc.) into one output annotation file, and it works with any assembly you choose. You can select which output fields you want, then download the tab-delimited file.
  
  From there, you can use R, Python, and/or GNU tools such as `awk` and `sed` to coerce the data into a format which is useful for your purposes. You can then load it into R as a data frame and use the columns as arguments in the GRanges constructor to create a GRanges object.
  
  
  ---
  
  