#gatk

[pythonjobsupport]

WGS Variant Calling: Variant calling with GATK - Part 1 | Detailed NGS Analysis Workflow

This is a detailed workflow tutorial of how to call variants (SNPs + Indels) from whole genome sequencing (WGS) data. In this … source

[andreaspinelli]

#gatk #genome #toolkit #improveknowledge # (presso Aalborg Universitet København - AAU CPH) https://www.instagram.com/p/BtNzadZhZ-J/?utm_source=ig_tumblr_share&igshid=1nmoc7d7a3a3

[bioinfographics-blog]

The Genome Analysis Toolkit or GATK is a software package developed at the Broad Institute to analyse next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size.

[rustbeltscientist]

Indel Realigner can improve assemblies!

Congratulations to the individual in my former lab who has been doing all the work on this. Several rounds of local realignment around indels using GATK. Informed by indel calling and generating fresh consensus sequences can improve alignments in some cases. Neat.

[rustbeltscientist]

Tip of the day GATK Indel Calling

Remember to set the Genotype Likelihoods Model (-glm) to BOTH when running Unified Genotyper.

 I knew that filtering/Variant Quality Score Recalibration have different procedures for SNPs/INDELs so I had left the INDEL step to last (bad idea). I kept finding 0 INDELs for my data set (when I had a huge number of SNP calls). Turns out I had forgotten to set the -glm to BOTH way back at the Unified Genotyper Step, whoops. I'll probably go back and run on INDELs only later, but for now mystery solved.

[rustbeltscientist]

There has been some debate over whether RNA-Seq produces good enough SNP calling data in mammals This paper shows that >90% of the known SNPs could be detected in the RNA-Seq data set. 

[rustbeltscientist]

VariantEval

VariantEval produces some great metrics using the CompOverlap module, I had alot of trouble discerning how to find comparisons between more than 2 VCF files (I have four conditions). This allows several files to be compared to a single comparison track ( I used a VCF that should represent background genotype). The module outputs metrics such as the number of Concordant loci, which I found very helpful to determine how SNPs might accumulate with the transfer process.