Test-drive the GATK tools and Best Practices pipelines on Terra
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Coverage - Read depth metrics
Coverage (a.k.a. read depth) describes the amount of sequence data that is available per position in the sequenced genome territory.
Coverage in overall sequence quality metrics
This documentation is in development; please check back later. In the meantime, have a look at the Picard metrics collection documentation.
Coverage in germline short variant annotations
The short variant callers generate two main coverage annotation metrics: the allele depth per sample (AD) and overall depth of coverage (DP, available both per sample and across all samples, with important differences):
- DepthPerAlleleBySample (AD) outputs the depth of coverage of each allele per sample.
- Coverage (DP) outputs the filtered depth of coverage for each sample and the unfiltered depth of coverage across all samples.
At the sample level, these annotations are highly complementary metrics that provide two important ways of thinking about the depth of the data available for a given sample at a given site. The key difference is that the AD metric is based on unfiltered read counts while the sample-level DP is based on filtered read counts (see tool documentation for a list of read filters that are applied by default for each tool). As a result, they should be interpreted differently.
The sample-level DP is in some sense reflective of the power I have to determine the genotype of the sample at this site, while the AD tells me how many times I saw each of the REF and ALT alleles in the reads, free of any bias potentially introduced by filtering the reads. If, for example, I believe there really is a an A/T polymorphism at a site, then I would like to know the counts of A and T bases in this sample, even for reads with poor mapping quality that would normally be excluded from the statistical calculations going into GQ and QUAL.
Note that because the AD includes reads and bases that were filtered by the caller (and in case of indels, is based on a statistical computation), it should not be used to make assumptions about the genotype that it is associated with. Ultimately, the phred-scaled genotype likelihoods (PLs) are what determines the genotype calls.