text: user247: PacBio is selling ~10x PacBio SEQUEL long reads as an upgrade to Illumina data for SV discovery.
  In a clinical setting, the main requirements are proper sensitivity and specificity but also the processing of cohorts, at least families. This requires a genotyping step, such that it can be identified whether a given variant is shared by two or more individuals or whether it is not.
  What are the tools of the trade for this task?
  As having 50-60x PacBio reads is not an option from a economic point of view, one has to make do with 10x coverage.
  
  user247: I'm aware of the following (very few and suboptimal) options:
  
  Sniffles -- sadly not very reliable in my experience, also no genotyping step or multi-sample support
  PB Honey -- no genotyping step or multi-sample support
  
  
  user57: There is an evaluation of PB Honey and Sniffles algorithms for low coverage PacBio datasets in this preprint and another evaluation is shown on this poster. Both reports agree that optimal is (surprisingly) combination of PB Honey and Sniffles.
  Author of Sniffles have benchmarked Sniffles against PB Honey, where he shown that Sniffles performs significantly better. Take a look on this presentation (slide 15).
  Another option is SMRT-SV, but I am not aware of any benchmarking.
  
original_text: 247: <p>PacBio is selling ~10x PacBio SEQUEL long reads as an upgrade to Illumina data for SV discovery.</p>
  
  <p>In a clinical setting, the main requirements are proper sensitivity and specificity but also the processing of cohorts, at least families. This requires a genotyping step, such that it can be identified whether a given variant is shared by two or more individuals or whether it is not.</p>
  
  <p>What are the tools of the trade for this task?</p>
  
  <p>As having 50-60x PacBio reads is not an option from a economic point of view, one has to make do with 10x coverage.</p>
   247: <p>I'm aware of the following (very few and suboptimal) options:</p>
  
  <ul>
  <li><a href="https://github.com/fritzsedlazeck/Sniffles/releases" rel="nofollow noreferrer">Sniffles</a> -- sadly not very reliable in my experience, also no genotyping step or multi-sample support</li>
  <li><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-180" rel="nofollow noreferrer">PB Honey</a> -- no genotyping step or multi-sample support</li>
  </ul>
   57: <p>There is an evaluation of PB Honey and Sniffles algorithms for low coverage PacBio datasets in <a href="http://www.biorxiv.org/content/early/2016/12/17/092544.full.pdf+html" rel="nofollow noreferrer">this preprint</a> and another evaluation is shown on <a href="http://www.pacb.com/wp-content/uploads/2016-ashg-poster-andrew-carroll-dnanexus.pdf" rel="nofollow noreferrer">this poster</a>. Both reports agree that optimal is (surprisingly) combination of <a href="https://sourceforge.net/projects/pb-jelly/" rel="nofollow noreferrer">PB Honey</a> and <a href="https://github.com/fritzsedlazeck/Sniffles" rel="nofollow noreferrer">Sniffles</a>.</p>
  
  <p>Author of Sniffles have benchmarked Sniffles against PB Honey, where he shown that Sniffles performs significantly better. Take a look on <a href="http://schatzlab.cshl.edu/presentations/2016/2016.10.28.BIODATA.PacBioSV.pdf" rel="nofollow noreferrer">this presentation (slide 15)</a>.</p>
  
  <p>Another option is <a href="https://github.com/EichlerLab/pacbio_variant_caller" rel="nofollow noreferrer">SMRT-SV</a>, but I am not aware of any benchmarking.</p>
  