We’re moving the GATK website, docs and forum to a new platform. Read the full story and breakdown of key changes on this blog.
If you happen to see a question you know the answer to, please do chime in and help your fellow community members. We encourage our fourm members to be more involved, jump in and help out your fellow researchers with their questions. GATK forum is a community forum and helping each other with using GATK tools and research is the cornerstone of our success as a genomics research community.We appreciate your help!
Test-drive the GATK tools and Best Practices pipelines on Terra
Check out this blog post to learn how you can get started with GATK and try out the pipelines in preconfigured workspaces (with a user-friendly interface!) without having to install anything.
We will be out of the office for a Broad Institute event from Dec 10th to Dec 11th 2019. We will be back to monitor the GATK forum on Dec 12th 2019. In the meantime we encourage you to help out other community members with their queries.
Thank you for your patience!
Suggestions for WGS 5X Sequences
Hi Geraldine or Sheila,
I am in the process of customizing a GATK pipeline for processing aDNAA. I have processed a couple of 3000 year old WGS sequences so far using GATK best practices, and although the resulting VCFs have been ok, they are far from perfect, and I was looking for any suggestions you can offer on how to minimize reference bias both during alignment as well as variant calling. To give you an idea where things stand, I have organized a color coded spreadsheet summarizing CollectVariantCallingMetrics using various GATK tools. Here is a link to the sheet https://docs.google.com/spreadsheets/d/1iPw-afPV6Z4zGzqfOlna_UyYf87gBv1ghxzLzDkqfFw/edit?usp=sharing
The attached spreadsheet will show you the metrics with and without BQSR, and with or without VQSR.
I have also experimented with omitting some of the annotations you use in VQSR and have plotted everything in R, although the output with all annotations using -mG 4 was not bad.
So far my outputs have been using HaplotypeCaller in GVCF and joint genotyping using 2 or 3 samples, I plan to increase to 10 to see if that helps establish better evidence for variants at low depth sites
Any suggestions you can offer that I may try to reduce reference bias at low depth positions. Filtering out positions that passed VQSR but had a QUAL score <100 with vcftools helped some. Any suggestions with regards changing the prior likelihood of true and not true training sites and known sites during VQSR or to use different resources than the HapMap 1000G or Omni that you suggest.