In most samples, INDELs are several times more than SNVs.

[monagai]

I tried NanoSeq 3.5.1 for rats and mice data.
In most samples, INDELs are several times more than SNVs.
The number of SNVs seems proper. INDELs seems too much.

Is it reasonable status as a result of NanoSeq 3.5.1 or shoud I change some of my procedures?
May latest NanoSeq (INDEL caller was changed in 3.5.2) output much different results?
Any advice would be appreciated.

[conditions]

• NanoSeq 3.5.1
• tumor samples We used normal samples with certain treatments as tumor samples. They were dealt with "HpyCH4V"
• Matched normal samples (control samples) : WGS, x30
• I executed NanoSeq step by step. I didn't use Nextflow.
• I made neat bams for normal filtered bams with randomreadinbundle.
• I didn't specify -C (SNP.sorted.bed.gz) and -D (NOISE.sorted.bed.gz) for runNanoSeq.py dsa because there seems to be no such data for rats or mice.
• I didn't execute VerifyBamId and efficiency_nanoseq.pl.
• I followed all the instruction other than above.

[fa8sanger]

Hi Momoko, That’s strange. The new indel caller should work better, but it should not make a huge difference (I recommend switching to the new version, though). Which parameters did you use for indel calling? I wonder if you are not being strict enough for indels and you are calling indels that are also seen in the matched normal (likely germline SNPs). One way to check this is inspect those indel calls in IGV (or with samtools mpileup) and see what’s in there for the matched normal (parameter vaf|max-vaf=f in the new version) Let me know if this helps How do the SNV calls look? On 2 Jan 2024, at 15:00, Momoko NAGAI ***@***.***> wrote: I tried NanoSeq 3.5.1 for rats and mice data. In most samples, INDELs are several times more than SNVs. The number of SNVs seems proper. INDELs seems too much. Is it reasonable status as a result of NanoSeq 3.5.1 or shoud I change some of my procedures? May latest NanoSeq (INDEL caller was changed in 3.5.2) output much different results? Any advice would be appreciated. [conditions] * NanoSeq 3.5.1 * tumor samples: dealt with "HpyCH4V" * matched normal samples: WGS, x30 * I executed NanoSeq step by step. I didn't use Nextflow. * I made neat bams for normal filtered bams with randomreadinbundle. * I didn't specify -C (SNP.sorted.bed.gz) and -D (NOISE.sorted.bed.gz) for runNanoSeq.py dsa because there seems to be no such data for rats or mice. * I didn't execute VerifyBamId and efficiency_nanoseq.pl. * I followed all the instruction other than above. — Reply to this email directly, view it on GitHub [github.com]<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_cancerit_NanoSeq_issues_82&d=DwMCaQ&c=D7ByGjS34AllFgecYw0iC6Zq7qlm8uclZFI0SqQnqBo&r=v9-R7fUmjpv-9Zaqyk1nlnlOC3qPkTEJz5tyYxg2uec&m=ellQoBHnQGVIIptZ5w4tgg8gKNJjSlXU7dZh7fHOpoTyzuieu9-GQ_16ypDd_fDh&s=cLWo_MRzi9jDK5uVlV-8J9C8RYRNvmP2Qp2cXderXGo&e=>, or unsubscribe [github.com]<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_ADNUT3KQ3JOUW526IVGFYMDYMQOHXAVCNFSM6AAAAABBKEWMU6VHI2DSMVQWIX3LMV43ASLTON2WKOZSGA3DENJUGA2TIOA&d=DwMCaQ&c=D7ByGjS34AllFgecYw0iC6Zq7qlm8uclZFI0SqQnqBo&r=v9-R7fUmjpv-9Zaqyk1nlnlOC3qPkTEJz5tyYxg2uec&m=ellQoBHnQGVIIptZ5w4tgg8gKNJjSlXU7dZh7fHOpoTyzuieu9-GQ_16ypDd_fDh&s=-on0M3UNA3ubRkdy6VXxmO4xKtXUzURTbT98N_WHi30&e=>. You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

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[monagai]

Thank you for your advice!
Sorry for that my first comment for tumor was wrong. I revised it.
I used default parameters for all steps.
I will re-run using the latest version and check the data with your advice!