<p>ClairS is now released for long-read somatic variant calling. <a href="https://github.com/HKU-BAL/Clairs" rel="nofollow noreferrer">https://github.com/HKU-BAL/Clairs</a></p>

CC BY-SA 4.0
2023-01-30T03:17:39.763Z
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2023-01-30T03:08:19.223Z
2023-01-30T03:08:19.223Z
Laurent
<p>I'm looking for a tool to call somatic single nucleotide variations on reads from an Oxford Nanopore Technologies (ONT) run.
I have both tumor and normal samples and have already aligned them to a reference genome.</p>
<p>Popular tools like <a href="https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2" rel="nofollow noreferrer">mutect2</a> have been developed for short reads, and according to <a href="https://link.springer.com/chapter/10.1007/978-3-030-91836-1_3" rel="nofollow noreferrer">this</a> paper it is not recommended to use tools specifically designed for short reads with long reads.</p>
<p>I know of tools that work with ONT reads like <a href="https://github.com/HKU-BAL/Clair3" rel="nofollow noreferrer">clair3</a> which is used in the epi2melabs workflow for SNPs <a href="https://github.com/epi2me-labs/wf-human-snp" rel="nofollow noreferrer">wf-human-snp</a>,  but clair3 takes only one sample at a time, so seems for germline calling only.</p>
<p>Another approach would be to simply call the SNVs in both tumor and normal samples independently with <a href="https://github.com/HKU-BAL/Clair3" rel="nofollow noreferrer">clair3</a>, and then subtract normal SNVs from tumor SNVs, but according to what I have read in a few papers and online resources like <a href="https://gatk.broadinstitute.org/hc/en-us/articles/360035890491-Somatic-calling-is-NOT-simply-a-difference-between-two-callsets" rel="nofollow noreferrer">this from gatk</a>, this is a &quot;naive approach&quot;, and a somatic caller should use both normal and tumor samples together.</p>
<ul>
<li><p>Do you know of any tool that takes both tumor and normal long read samples to detect somatic variants (similar to <a href="https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2" rel="nofollow noreferrer">mutect2</a> but for long reads)</p>
</li>
<li><p>Have you used tools for short reads like <a href="https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2" rel="nofollow noreferrer">mutect2</a> with long reads?</p>
</li>
<li><p>Have you attempted the &quot;naive approach&quot; of simply subtracting SNVs?</p>
</li>
</ul>
<p>Thanks,
Walter.</p>

CC BY-SA 4.0
2022-06-01T12:38:32.950Z
Note that tumor/normal is only one way of doing somatic variant calling. It is unquestionably the best way, but in the clinical space, it isn't as common as you'd expect and in many cases you only have the somatic sample without the normal control. The main reason you don't want to use standard variant callers is that you need to be able to call variants with very low allelic balance (proportion of reads supporting the variant) because of the heterogeneity of somatic samples which will be a mix of tumor and normal cells, as well as the heterogeneity of the tumor cells themselves.
terdon
<variant-calling><nanopore><long-reads><somatic><snv>
somatic SNV tool for ONT samples
# somatic SNV tool for ONT samples

(Asked by: username_0 on 2022-06-01)

I'm looking for a tool to call somatic single nucleotide variations on reads from an Oxford Nanopore Technologies (ONT) run.
I have both tumor and normal samples and have already aligned them to a reference genome.

Popular tools like [mutect2](https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2) have been developed for short reads, and according to [this](https://link.springer.com/chapter/10.1007/978-3-030-91836-1_3) paper it is not recommended to use tools specifically designed for short reads with long reads.

I know of tools that work with ONT reads like [clair3](https://github.com/HKU-BAL/Clair3) which is used in the epi2melabs workflow for SNPs [wf-human-snp](https://github.com/epi2me-labs/wf-human-snp), but clair3 takes only one sample at a time, so seems for germline calling only.

Another approach would be to simply call the SNVs in both tumor and normal samples independently with [clair3](https://github.com/HKU-BAL/Clair3), and then subtract normal SNVs from tumor SNVs, but according to what I have read in a few papers and online resources like [this from gatk](https://gatk.broadinstitute.org/hc/en-us/articles/360035890491-Somatic-calling-is-NOT-simply-a-difference-between-two-callsets), this is a "naive approach", and a somatic caller should use both normal and tumor samples together.

* Do you know of any tool that takes both tumor and normal long read samples to detect somatic variants (similar to [mutect2](https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2) but for long reads)
* Have you used tools for short reads like [mutect2](https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2) with long reads?
* Have you attempted the "naive approach" of simply subtracting SNVs?

Thanks,
Walter.

**Tags:** variant-calling, nanopore, long-reads, somatic, snv
> **Comments:**
> **username_1**: Note that tumor/normal is only one way of doing somatic variant calling. It is unquestionably the best way, but in the clinical space, it isn't as common as you'd expect and in many cases you only have the somatic sample without the normal control. The main reason you don't want to use standard variant callers is that you need to be able to call variants with very low allelic balance (proportion of reads supporting the variant) because of the heterogeneity of somatic samples which will be a mix of tumor and normal cells, as well as the heterogeneity of the tumor cells themselves.  

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## 1 answers

### Answer by username_2 on 2023-01-30. Score: 1 | Accepted answer

ClairS is now released for long-read somatic variant calling. <https://github.com/HKU-BAL/Clairs>

> **Comments:**
> **username_3**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  

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