<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>

CC BY-SA 4.0
2021-02-11T07:22:14.283Z
I think there is a difference between using de Bruin graph and represent a genome as a graph. The colored graph assemblers are interesting. Thank you.
0x90
CC BY-SA 4.0
2021-02-11T07:35:53.037Z
I completely agree.
winni2k
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2021-02-10T12:32:02.733Z
2021-02-10T12:32:02.733Z
winni2k
<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>

CC BY-SA 4.0
2021-02-09T09:48:46.713Z
Not sure about variant callers, but since you asked about aligners, you should check out [vg](https://github.com/vgteam/vg).
user438383
CC BY-SA 4.0
2021-02-09T11:48:46.937Z
@user438383 thanks, it's worth mentioning WhatsHap, GraphAligner, and Minigraph as well.
0x90
<variant-calling><reference-genome><graphs>
Are there open-sourced graph-based variant callers?
# Are there open-sourced graph-based variant callers?

(Asked by: username_0 on 2021-02-09)

I would like to compare GATK with graph-based variant callers. I have seen the [Fast and accurate genomic analyses using genome graphs](https://www.nature.com/articles/s41588-018-0316-4) paper from [SevenBridges](https://www.sevenbridges.com/pan-genome-graphs/) and the [paragraph](https://github.com/Illumina/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](https://www.biomedcentral.com/collections/graphgenomes) 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.

**Tags:** variant-calling, reference-genome, graphs
> **Comments:**
> **username_1**: Not sure about variant callers, but since you asked about aligners, you should check out [vg](https://github.com/vgteam/vg).  
> **username_0**: @user438383 thanks, it's worth mentioning WhatsHap, GraphAligner, and Minigraph as well.  

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## 1 answers

### Answer by username_2 on 2021-02-10. Score: 4

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](https://www.well.ox.ac.uk/research/research-groups/lunter-group/lunter-group/platypus-a-haplotype-based-variant-caller-for-next-generation-sequence-data) and [freebayes](https://github.com/freebayes/freebayes).

You might also consider multi-color genome assemblers. For example, [Cortex](http://cortexassembler.sourceforge.net/index_cortex_var.html) and [Corticall](https://github.com/mcveanlab/Corticall) use colored De Bruijn graphs to aggregate reads from multiple genomes and call variants.

> **Comments:**
> **username_0**: I think there is a difference between using de Bruin graph and represent a genome as a graph. The colored graph assemblers are interesting. Thank you.  
> **username_2**: I completely agree.  

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