text: user734: I would like to compare GATK with graph-based variant callers. I have seen the Fast and accurate genomic analyses using genome graphs paper from SevenBridges and the paragraph tool by Illumina, though it does not look like folks use their platform or it is not open-sourced (in the case of SevenBridges's GRAF).
  The idea is to see if graph-based approaches detect better both SNPs and structural variants compared with existing methods. Additionally, I believe that if there exists some novel graph-based aligner/assembler one could use the metadata downstream to identify variants.
  
  user492: Yes, there are. There are some suggestions in the comments (VG, WhatsHap, GraphAligner, Minigraph).
  However, to be clear, the current default variant calling algorithm used in the GATK (the haplotype caller) is a graph-based algorithm. It constructs a De Bruijn graph from mapped reads for local assembly around variants. Other tools that also follow this strategy include Platypus and freebayes.
  You might also consider multi-color genome assemblers. For example, Cortex and Corticall use colored De Bruijn graphs to aggregate reads from multiple genomes and call variants.
  
original_text: 734: <p>I would like to compare GATK with graph-based variant callers. I have seen the <a href="https://www.nature.com/articles/s41588-018-0316-4" rel="nofollow noreferrer">Fast and accurate genomic analyses using genome graphs</a> paper from <a href="https://www.sevenbridges.com/pan-genome-graphs/" rel="nofollow noreferrer">SevenBridges</a> and the <a href="https://github.com/Illumina/paragraph" rel="nofollow noreferrer">paragraph</a> tool by Illumina, though it does not look like folks use their platform or it is not open-sourced (in the case of SevenBridges's GRAF).</p>
  <p>The idea is to see if <a href="https://www.biomedcentral.com/collections/graphgenomes" rel="nofollow noreferrer">graph-based approaches</a> detect better both SNPs and structural variants compared with existing methods. Additionally, I believe that if there exists some novel graph-based aligner/assembler one could use the metadata downstream to identify variants.</p>
   492: <p>Yes, there are. There are some suggestions in the comments (VG, WhatsHap, GraphAligner, Minigraph).</p>
  <p>However, to be clear, the current default variant calling algorithm used in the GATK (the haplotype caller) is a graph-based algorithm. It constructs a De Bruijn graph from mapped reads for local assembly around variants. Other tools that also follow this strategy include <a href="https://www.well.ox.ac.uk/research/research-groups/lunter-group/lunter-group/platypus-a-haplotype-based-variant-caller-for-next-generation-sequence-data" rel="nofollow noreferrer">Platypus</a> and <a href="https://github.com/freebayes/freebayes" rel="nofollow noreferrer">freebayes</a>.</p>
  <p>You might also consider multi-color genome assemblers. For example, <a href="http://cortexassembler.sourceforge.net/index_cortex_var.html" rel="nofollow noreferrer">Cortex</a> and <a href="https://github.com/mcveanlab/Corticall" rel="nofollow noreferrer">Corticall</a> use colored De Bruijn graphs to aggregate reads from multiple genomes and call variants.</p>
  