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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“On Bait” reads localized to ACCESS panel

Ensure there was adequate coverage of genomic regions in the ACCESS panel.

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Last updated 4 years ago

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Theoretical Method

Divide the number of reads mapping to ACCESS genome bait regions by the sample’s total read count.

Note: This metric comes from the standard BAM

Technical Methods

  • Tool Used:

    • waltz.jar CountReads

    • aggregate_bam_metrics.sh

    • tables_module.py

    • plots_module.r

  • Input

    • ACCESS pool A and pool B bed files

    • Standard Bam (tables also produced for U / S / D bams)

  • Output

    • Text file with the read count information: “sample_id.bam.read-counts.txt”

    • Text files for aggregated results “read-counts.txt”

Interpretations Ideally should be between 60%-80% and should not drop below 50% for ctDNA samples (A + B targets combined). Because A and B targets are mixed in 50:1 ratio, there should be a larger on-target rate for the A targets. If the rate drops below 50%, with adequate read counts, this could be indicative of a bad capture. For buffy coats, this is about 35%-45%.