text: user439: I have short (67bp) Hi-C reads from a highly heterozygous organism (~15% between-haplotype SNP divergence) and I have both reference haplotypes.
  I wanted to try and compare different haplotyping softwares for Hi-C reads using these reads as benchmarking dataset. When mapping the reads separately to each haplotype, I get good mapping statistics. When I map the reads to a single haplotype with all the heterozygous SNPs masked (into N's), I get  very poor mapping rates.
  I would like to be able to map the reads when the real haplotypes are unknown (reference is a mix of haplotypes).
  I use minimap2 to map the reads with the sr preset. I tried decreasing the mismatch penalty (-B) to 1 and increasing the --score-N value, but this had no effect.
  As shown in the attached IGV screenshot, coverage drops to 0 when the SNP density increases.
  Is it feasible to map reads with such a high heterozygosity on a single (masked) reference ? Should I use another tool ?
  
  user3499: I believe you may be able to map your reads, but I don't know how to do that with minimap2. 
  I recommend running gsnap, which is more SNP tolerant and provides a number of parameters which are likely to help.
  For instance, I believe that most aligners will treat 'N' characters as mismatches when you align. GSNAP has parameters to account for this. 
  --query-unk-mismatch=INT       Whether to count unknown (N) characters in the query as a mismatch
                                 (0=no (default), 1=yes)
  --genome-unk-mismatch=INT      Whether to count unknown (N) characters in the genome as a mismatch
                                 (0=no, 1=yes (default))
  
  It also has a mismatch parameter similar to the one you described for minimap2.
  -m, --max-mismatches=FLOAT     Maximum number of mismatches allowed
  
  Try running with the 'genome-unk-mismatch' parameter above (with the masked reference). That may be your best bet. There are also other parameters that may help, but this should be a good start.
  
original_text: 439: <p>I have short (67bp) Hi-C reads from a highly heterozygous organism (~15% between-haplotype SNP divergence) and I have both reference haplotypes.</p>
  
  <p>I wanted to try and compare different haplotyping softwares for Hi-C reads using these reads as benchmarking dataset. When mapping the reads separately to each haplotype, I get good mapping statistics. When I map the reads to a single haplotype with all the heterozygous SNPs masked (into N's), I get  very poor mapping rates.</p>
  
  <p>I would like to be able to map the reads when the real haplotypes are unknown (reference is a mix of haplotypes).</p>
  
  <p>I use minimap2 to map the reads with the sr preset. I tried decreasing the mismatch penalty (-B) to 1 and increasing the --score-N value, but this had no effect.</p>
  
  <p>As shown in the attached IGV screenshot, coverage drops to 0 when the SNP density increases.<a href="https://i.stack.imgur.com/ycZ4G.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ycZ4G.png" alt="enter image description here"></a>
  Is it feasible to map reads with such a high heterozygosity on a single (masked) reference ? Should I use another tool ?</p>
   3499: <p>I believe you may be able to map your reads, but I don't know how to do that with minimap2. </p>
  
  <p>I recommend running gsnap, which is more SNP tolerant and provides a number of parameters which are likely to help.</p>
  
  <p>For instance, I believe that most aligners will treat 'N' characters as mismatches when you align. GSNAP has parameters to account for this. </p>
  
  <pre><code>--query-unk-mismatch=INT       Whether to count unknown (N) characters in the query as a mismatch
                                 (0=no (default), 1=yes)
  --genome-unk-mismatch=INT      Whether to count unknown (N) characters in the genome as a mismatch
                                 (0=no, 1=yes (default))
  </code></pre>
  
  <p>It also has a mismatch parameter similar to the one you described for minimap2.</p>
  
  <pre><code>-m, --max-mismatches=FLOAT     Maximum number of mismatches allowed
  </code></pre>
  
  <p>Try running with the 'genome-unk-mismatch' parameter above (with the masked reference). That may be your best bet. There are also other parameters that may help, but this should be a good start.</p>
  