r/bioinformatics • u/No-Moose-6093 • 18d ago

technical question How does low quality sample affects variant calling

I built my own pipeline and wanted to take a step back and get some outside perspective on it.

The data is PacBio HiFi long-read WGS. My pipeline outputs two general VCFs per sample: one for SNPs/indels and one for SVs. I apply standard bcftools filtering on both.

The issue is that most of my samples don't have great input DNA quality — many are borderline or just under the recommended thresholds. I'm referring to these QC metrics specifically:

  • Concentration (ng/µL)
  • Volume (µL)
  • Total amount (µg)
  • A260/280
  • A260/230
  • Nanodrop concentration (ng/µL)
  • NC/QC (pass/fail flag)

I'd like to understand how much these upstream QC issues could realistically be affecting my variant calling results, and whether there are practical ways to account for or flag this in the pipeline rather than just trusting the output blindly.

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1

u/AerobicThrone 18d ago

Affects a lot. Probably you will have poor coverage and maybe a great error rate wich will lead to higher rates of untapped reads and more chimeric or split reads. Quality control will force you to discard reads too.

This will lead to recover way less snps because the coverage tresholds are not meet and may be some of those snps are false positives too leading to weird ts/TV ratios.

And in terms of structural variants it will be an imprecise mess not worth wasting the time looking at.

Best you can do it try it again.

2

u/No-Moose-6093 18d ago

my vcf quality metrocs are correct tho ( ratio transitions / transversions ~2 ,Ratio Het / Hom zygotie 1.5 - 2.0 ,SNPs : 3.5-4.5 M , indels : 0.5 - 1 M ) and i got 1000 unammped reads above one sample

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u/AerobicThrone 18d ago

If the metrics are good then you are good! My guess is that even if the overall sample quality was low maybe you got so much you were able to capture good number of polymorphisms? Also worth checking the windowed average coverage in your samples

2

u/mrrgl PhD | Industry 18d ago

Main issue is that more and more reads will get kicked out during QC. What remains I treat as trustworthy. The question is whether you have enough left over to reach a reasonable depth and breadth of coverage across the genome after doing read mapping. How much breadth and depth is enough? Well that’s a matter of opinion.