Coverage vs GC bias

Awareness of possible loss of accuracy in downstream sequencing results due to coverage due to GC content bias.

Introduction

This figure plots the normalized coverage against the % GC content from the ACCESS target regions. Each line is data from one sample.

Example MultiQC report showing % GC bias in coverage for 20 samples.

Methods

Tool used: GATK-CollectHsMetrics BAM type: (1) collapsed BAM and (2) uncollapsed BAM. Regions: Pool A

The data used to produce this figure are the values under the normalized_coverage and %gc columns, which are in the *_per_target_coverage.txt output file from CollectHsMetrics. For each sample separately, the % GC content for each target region is calculated, followed by binning the target regions by their GC content (in 5% intervals). Then for each bin, the mean coverage is calculated and then normalized across all regions that fall into each GC bin.

Interpretation

Extreme base compositions, i.e., GC-poor or GC-rich sequences, lead to an uneven coverage or even no coverage of reads across the genome. This can affect downstream small variant and copy number calling. Both of which rely on consistent sequencing depth across all regions. Ideally this plot should be as flat as possible. The above example depicts a slight decrease in coverage at really high GC-rich regions, but is a good result for ACCESS.