Tagged with #coveragebysample
2 documentation articles | 0 announcements | 2 forum discussions

Created 2014-10-17 19:19:39 | Updated 2014-10-29 15:40:36 | Tags: coveragebysample depthofcoverage depthperallelebysample ad dp
Comments (0)


This document covers the use of various tools and metrics associated with depth of coverage.

Note that at the moment this document is incomplete; in future we will add more details pertaining to the DepthOfCoverage and DiagnoseTargets analysis tools. For now, this just gives you some keys to understanding the coverage annotations given in the AD and DP fields of the VCF files output by the variant calling tools.

Coverage annotations: DP and AD

The 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), controlled by the following annotator modules:

  • 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.

TO BE CONTINUED... meanwhile, check out the DepthOfCoverage and DiagnoseTargets tool docs.

Created 2012-07-23 23:55:27 | Updated 2012-07-23 23:55:27 | Tags: coveragebysample gatkdocs
Comments (0)

A new tool has been released!

Check out the documentation at CoverageBySample.

No posts found with the requested search criteria.

Created 2013-02-19 12:24:36 | Updated 2013-02-19 20:07:54 | Tags: coveragebysample coverage
Comments (3)

I am also trying to check the coverage at each position of my reference using the CoverageBySample tool (with and without the –L argument):

java -Xmx30g -jar GenomeAnalysisTK.jar \
-T UnifiedGenotyper \
–T CoverageBySample \
–R ref.fasta  \
-I  input.bam  \
-o output.cov\

The output (below) is giving the right coverage but without the positions on the reference and also skipping all positions with no coverage. Is there any way to get these positions in the output file?

eo78       10
eo78       10
eo78       10
eo78       10
eo78       10
eo78       11
eo78       12
eo78       12
eo78       12

Created 2012-09-03 20:36:34 | Updated 2012-09-04 16:13:56 | Tags: unifiedgenotyper coveragebysample bacteria
Comments (5)

UnifiedGenotyper- Can it be used to call SNP's in bacterial genomes