Answers.Body: <p>Qualimap will do this for you. </p>
  
  <ol>
  <li>Go to <a href="http://qualimap.bioinfo.cipf.es" rel="nofollow noreferrer">qualimap.bioinfo.cipf.es</a></li>
  <li>Run qualimap (default params are fine) on each BAM file</li>
  <li>Open up the HTML output, and you can read off the %identity (they measure the opposite, i.e. mismatch rate, but 100% - mismatch rate is %identity of course), indel rate, etc.</li>
  </ol>
  
  <p>One thing to watch out for (you don't mention it in your question, but just in case) is that you cannot directly compare Q scores - these are a bit of a mess and calculated very differently in each piece of software. </p>
  
  <p>Unsolicited suggestion: you might also try <a href="https://github.com/philres/ngmlr" rel="nofollow noreferrer">NGM-LR</a> for mapping MinION data. We've found it beats the others for our data (though we map to a distant reference).</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-05-17T04:25:35.110Z
Answers.Text: Thanks for the suggestion. To clarify, is this the aligner you were referring to? https://github.com/philres/ngmlr
Answers.UserDisplayName: Scott Gigante
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-05-17T05:16:55.260Z
Answers.Text: Yep. Poster is here: http://schatzlab.cshl.edu/publications/posters/2015/2015.GI.NextGenMap-LR.pdf
  
  You could check out Sniffles too - detects structural variants from 3rd gen data. https://github.com/fritzsedlazeck/Sniffles
Answers.UserDisplayName: roblanf
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-05-17T03:10:02.837Z
Answers.LastActivityDate: 2017-05-18T00:01:18.567Z
Answers.LastEditDate: 2017-05-18T00:01:18.567Z
Answers.UserDisplayName: roblanf
Body: <p>I have run Oxford Nanopore Technologies' MinION sequencing on the same DNA sample using three flowcells, each aligned against the same reference genome (E.coli K12 MG1655) using both BWA MEM and GraphMap and stored as BAM files.</p>
  
  <p>How can I quantitatively and efficiently analyse the quality of alignment (percentage identity, insertion rate, deletion rate) of each of these files?</p>
  
Tags: <sam><phylogenetics><nanopore>
Title: Compare alignment quality of multiple sequencing runs aligned against the same reference genome
ThreadText: # Compare alignment quality of multiple sequencing runs aligned against the same reference genome
  
  (Asked by: username_0 on 2017-05-17)
  
  I have run Oxford Nanopore Technologies' MinION sequencing on the same DNA sample using three flowcells, each aligned against the same reference genome (E.coli K12 MG1655) using both BWA MEM and GraphMap and stored as BAM files.
  
  How can I quantitatively and efficiently analyse the quality of alignment (percentage identity, insertion rate, deletion rate) of each of these files?
  
  **Tags:** sam, phylogenetics, nanopore
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2017-05-17. Score: 6 | Accepted answer
  
  Qualimap will do this for you.
  
  1. Go to [qualimap.bioinfo.cipf.es](http://qualimap.bioinfo.cipf.es)
  2. Run qualimap (default params are fine) on each BAM file
  3. Open up the HTML output, and you can read off the %identity (they measure the opposite, i.e. mismatch rate, but 100% - mismatch rate is %identity of course), indel rate, etc.
  
  One thing to watch out for (you don't mention it in your question, but just in case) is that you cannot directly compare Q scores - these are a bit of a mess and calculated very differently in each piece of software.
  
  Unsolicited suggestion: you might also try [NGM-LR](https://github.com/philres/ngmlr) for mapping MinION data. We've found it beats the others for our data (though we map to a distant reference).
  
  > **Comments:**
  > **username_0**: Thanks for the suggestion. To clarify, is this the aligner you were referring to? https://github.com/philres/ngmlr  
  > **username_1**: Yep. Poster is here: http://schatzlab.cshl.edu/publications/posters/2015/2015.GI.NextGenMap-LR.pdf  You could check out Sniffles too - detects structural variants from 3rd gen data. https://github.com/fritzsedlazeck/Sniffles  
  
  ---
  
  