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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 have been developed for short reads, and according to this 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 which is used in the epi2melabs workflow for SNPs 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, 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, 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 but for long reads)

  • Have you used tools for short reads like mutect2 with long reads?

  • Have you attempted the "naive approach" of simply subtracting SNVs?

Thanks, Walter.

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    $\begingroup$ 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. $\endgroup$
    – terdon
    Commented Jun 1, 2022 at 12:38

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

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    Commented Jan 30, 2023 at 3:17

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