text: user294: I'd like to call diploid SNV variants from long-reads data (~80SMRTcells PacBio). 
  I have generated a draft reference genome for an indivudual from a heterozygous (~4%) species (Canu+Haplomerger2). 
  I can use this reference for some applications:
  
  RNAseq mapping;
  functional annotation;
  duplicate detection;
  synteny analysis
  
  but not others:
  
  analysis of compound heterozygotes; 
  allele-specific expression; 
  variant linkage
  
  This is mainly why I need phasing. FYI, I tried Falcon + FalconUnzip too, but the daligner phase was just taking too much time/resources and I need to find a strategy to make it work in reasonable time (working on it).
  Meanwhile, for phasing, It was suggested to use HapCUT2 - from Canu's FAQ
  
  (1) "Avoid collapsing the genome" in Canu to get raw diploid assembly as complete & separate as possible (2) HaploMerger2 to get high-quality reference and alternative haploid assemblies (3) HapCUT2 or other phasing tools to get the high-quality haplotype assembly based on the reference haploid assembly. 
  
  However, HapCUT2 requires the input of a diploid SNV file. Which could be done if I had short-reads data, but yet haven't found a method for doing so with in my hands just long-reads data. 
  Strategies I tried to get the SNVs:
  
  marginPhase : method paper 
  
  but I had a issue in running it 
  
  Clairvoyante : method paper
  
  but I currently don't have access to suitable GPU machines to build the model - and I can't use the models provided by the authors because my organism is too different from human
  
  
  Any input would be greatly appreciated!
  
  user492: You could try aligning your reads to the draft reference genome with for example minimap2 and calling variants with freebayes. It appears there is a protocol for long reads: https://github.com/ekg/freebayes#re-genotyping-known-variants-and-calling-long-haplotypes
  Disclaimer: I have not run freebayes myself, and it may be that this is a terrible idea. I would expect that calling SNVs (Single-nucleotide variants) from PacBio reads is very challenging.  However, with enough coverage, I think it could be possible.
  
  user4162: You may want to try the longshot tool (https://github.com/pjedge/longshot) developed for calling variants in diploid genomes from long read data. 
  
original_text: 294: <p>I'd like to call diploid SNV variants from long-reads data (~80SMRTcells PacBio). </p>
  
  <p>I have generated a draft reference genome for an indivudual from a heterozygous (~4%) species (<a href="https://github.com/marbl/canu" rel="noreferrer">Canu</a>+<a href="https://github.com/mapleforest/HaploMerger2" rel="noreferrer">Haplomerger2</a>). </p>
  
  <p>I can use this reference for some applications:</p>
  
  <ul>
  <li>RNAseq mapping;</li>
  <li>functional annotation;</li>
  <li>duplicate detection;</li>
  <li>synteny analysis</li>
  </ul>
  
  <p>but not others:</p>
  
  <ul>
  <li>analysis of compound heterozygotes; </li>
  <li>allele-specific expression; </li>
  <li>variant linkage</li>
  </ul>
  
  <p>This is mainly why I need phasing. FYI, I tried <a href="https://github.com/PacificBiosciences/FALCON/wiki/FALCON-FALCON-Unzip-%22For-Phased-Diploid-Genome-Assembly-with-Single-Molecule-Real-Time-Sequencing%22" rel="noreferrer">Falcon + FalconUnzip</a> too, but the daligner phase was just taking too much time/resources and I need to find a strategy to make it work in reasonable time (working on it).</p>
  
  <p>Meanwhile, for phasing, It was suggested to use <a href="https://github.com/vibansal/HapCUT2/blob/master/README.md" rel="noreferrer">HapCUT2</a> - from Canu's FAQ</p>
  
  <blockquote>
    <p>(1) "Avoid collapsing the genome" in Canu to get raw diploid assembly as complete &amp; separate as possible (2) HaploMerger2 to get high-quality reference and alternative haploid assemblies (3) HapCUT2 or other phasing tools to get the high-quality haplotype assembly based on the reference haploid assembly. </p>
  </blockquote>
  
  <p>However, HapCUT2 requires the input of a diploid SNV file. Which <a href="https://bioinformatics.stackexchange.com/questions/4019/converting-a-bam-file-into-vcf/4022#4022">could be done</a> if I had short-reads data, but yet haven't found a method for doing so with in my hands just long-reads data. </p>
  
  <p>Strategies I tried to get the SNVs:</p>
  
  <ul>
  <li><p><a href="https://github.com/benedictpaten/marginPhase" rel="noreferrer">marginPhase</a> : <a href="https://www.biorxiv.org/content/early/2018/04/12/293944" rel="noreferrer">method paper</a> </p>
  
  <ul>
  <li>but I had a <a href="https://github.com/benedictpaten/marginPhase/issues/12" rel="noreferrer">issue</a> in running it </li>
  </ul></li>
  <li><p><a href="https://github.com/aquaskyline/Clairvoyante" rel="noreferrer">Clairvoyante</a> : <a href="https://www.biorxiv.org/content/early/2018/04/28/310458" rel="noreferrer">method paper</a></p>
  
  <ul>
  <li>but I currently don't have access to suitable GPU machines to build the model - and I can't use the models provided by the authors because my organism is too different from human</li>
  </ul></li>
  </ul>
  
  <p>Any input would be greatly appreciated!</p>
   492: <p>You could try aligning your reads to the draft reference genome with for example <code>minimap2</code> and calling variants with <code>freebayes</code>. It appears there is a protocol for long reads: <a href="https://github.com/ekg/freebayes#re-genotyping-known-variants-and-calling-long-haplotypes" rel="nofollow noreferrer">https://github.com/ekg/freebayes#re-genotyping-known-variants-and-calling-long-haplotypes</a></p>
  
  <p>Disclaimer: I have not run <code>freebayes</code> myself, and it may be that this is a terrible idea. I would expect that calling SNVs (Single-nucleotide variants) from PacBio reads is very challenging.  However, with enough coverage, I think it could be possible.</p>
   4162: <p>You may want to try the longshot tool (<a href="https://github.com/pjedge/longshot" rel="nofollow noreferrer">https://github.com/pjedge/longshot</a>) developed for calling variants in diploid genomes from long read data. </p>
  