Access Quality Control (v1)
  • Introduction
  • Meta information per sample
  • Raw read-pair counts (standard BAM)
  • On Target Coverage
  • Fraction of reads mapping to the human genome
  • “On Bait” reads localized to ACCESS panel
  • Coverage vs GC content
  • Insert Size Distribution
  • Distribution of ACCESS panel A coverage values
  • Average Coverage, Sample Level, Pool A Targets
  • UMI Family types Composition (Pool A)
  • Average Coverage, Sample Level, Pool B Targets
  • UMI Family types Composition (Pool B)
  • Base Quality Recalibration Scores
  • UMI family sizes (Simplex reads)
  • UMI family sizes (Duplex reads)
  • Sample Level Noise
  • Noise by Substitution Type
  • Contributing Sites for Noise
  • Hotspots In Normals
  • Sample mix-up
  • (Un)expected (Mis)matches Tables
  • Major Contamination
  • Minor Contamination
  • Duplex Minor Contamination
  • Sex Mismatch
  • FAQ
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Hotspots In Normals

Investigation of possible contamination of tumor DNA into normal sample

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Last updated 4 years ago

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Theoretical Method

Extract read counts for mutation hotspots from "normal" sample pileups. Then look into tumor samples to determine whether these mutations may have been due to contamination of tumor into the normal. Unfiltered bams are used for the normal samples to widen the search for hotspots, and duplex bams are then used for tumor samples.

Technical Methods

  • Tool Used:

    • Waltz PileupMetrics

    • BioinfoUtils.jar

    • plots_module.r

  • Input

    • sample_id-duplex-pileup.txt (for duplex noise calculation)

  • Output

    • hotspots-in-normals.txt

Interpretations

In the provided example we can see that there was potential contamination of the tumor sample into the normal sample for C-P835W4, as indicated by the 7 unfiltered reads that matched a mutation from the tumor. This may be due to improper separation of tumor and normal sample during extraction, or clonal hematopoiesis.