gatk-3.8

Author	SHA1	Message	Date
depristo	4c6d0e6143	Added stratification by discrete allele count, just like AF, but requiring genotypes so it can be exact. Added docs on wiki, and integrationtest using Kiran's very nice fundamental VCF VariantEvalWalker now passes a pointer to itself to the Stratefication setVariantEvalWalker (and assoc. get method) so that stratefications can look at VEWalker variables to obtain information necessary for their calculations, like the list of eval samples. This is a better interface, in my opinion, than the current approach of extending the base abstract Stratefication to include an initialize function that has all arguments necessarily for any Strat. JEXL expressions now provide access to the VariantContext vc object itself, so you can write JEXL's that directly use VariantContext and associated functions from the command line. ExomePostQC Queue script now creates a byAC eval using the new strat, and no longer produces a byAF file (as this was not exact, and lead to strange punctile behavior when actual AF quantization was out of sync with fix quantization of AF strat. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@6015 348d0f76-0448-11de-a6fe-93d51630548a	2011-06-19 03:11:00 +00:00
depristo	38d7733989	Now accepts any number of VCFs to evaluate. Runs the standard (now three) variant eval commands and invokes the exomeQC R script. Has some annoying assumptions about paths encoded right now. Example usage below: setenv DATA ~/Desktop/broadLocal/localData/ java -Djava.io.tmpdir=tmp -jar ../dist/Queue.jar -S ../scala/qscript/oneoffs/depristo/ExomePostQCEval.scala --gatkjarfile ../dist/GenomeAnalysisTK.jar -R $DATA/human_g1k_v37.fasta $* -eval $DATA/ESPGO_Gabriel_NHLBI_eomi_june_2011_batch1.vcf -intervals ~/Desktop/broadLocal/localData/whole_exome_agilent_1.1_refseq_plus_3_boosters.Homo_sapiens_assembly19.targets.interval_list -dbSNP ~/Desktop/broadLocal/localData/dbsnp_132_b37.vcf -eval $DATA/ESPGO_Gabriel_NHLBI_eomi_june_2011_batch2.vcf git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@6004 348d0f76-0448-11de-a6fe-93d51630548a	2011-06-16 12:49:54 +00:00
depristo	165befd38a	V1 of the post processing QC plotting scala script and R function. The scala script runs VariantEval on a VCF file, and computes QC metrics. The R script generates the report. Will discuss usage with data processing group. Ryan -- please add your additional plotting routines to this script, as you see fit. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5980 348d0f76-0448-11de-a6fe-93d51630548a	2011-06-13 17:06:42 +00:00

Author

SHA1

Message

Date

depristo

Added stratification by discrete allele count, just like AF, but requiring genotypes so it can be exact. Added docs on wiki, and integrationtest using Kiran's very nice fundamental VCF

VariantEvalWalker now passes a pointer to itself to the Stratefication setVariantEvalWalker (and assoc. get method) so that stratefications can look at VEWalker variables to obtain information necessary for their calculations, like the list of eval samples.  This is a better interface, in my opinion, than the current approach of extending the base abstract Stratefication to include an initialize function that has all arguments necessarily for any Strat.  
JEXL expressions now provide access to the VariantContext vc object itself, so you can write JEXL's that directly use VariantContext and associated functions from the command line.
ExomePostQC Queue script now creates a byAC eval using the new strat, and no longer produces a byAF file (as this was not exact, and lead to strange punctile behavior when actual AF quantization was out of sync with fix quantization of AF strat.

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@6015 348d0f76-0448-11de-a6fe-93d51630548a

2011-06-19 03:11:00 +00:00

depristo

38d7733989

Now accepts any number of VCFs to evaluate. Runs the standard (now three) variant eval commands and invokes the exomeQC R script. Has some annoying assumptions about paths encoded right now. Example usage below:

setenv DATA ~/Desktop/broadLocal/localData/
java -Djava.io.tmpdir=tmp -jar ../dist/Queue.jar -S ../scala/qscript/oneoffs/depristo/ExomePostQCEval.scala --gatkjarfile ../dist/GenomeAnalysisTK.jar -R $DATA/human_g1k_v37.fasta $* -eval $DATA/ESPGO_Gabriel_NHLBI_eomi_june_2011_batch1.vcf -intervals ~/Desktop/broadLocal/localData/whole_exome_agilent_1.1_refseq_plus_3_boosters.Homo_sapiens_assembly19.targets.interval_list -dbSNP ~/Desktop/broadLocal/localData/dbsnp_132_b37.vcf -eval $DATA/ESPGO_Gabriel_NHLBI_eomi_june_2011_batch2.vcf

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@6004 348d0f76-0448-11de-a6fe-93d51630548a

2011-06-16 12:49:54 +00:00

depristo

165befd38a

V1 of the post processing QC plotting scala script and R function. The scala script runs VariantEval on a VCF file, and computes QC metrics. The R script generates the report. Will discuss usage with data processing group. Ryan -- please add your additional plotting routines to this script, as you see fit.

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5980 348d0f76-0448-11de-a6fe-93d51630548a

2011-06-13 17:06:42 +00:00

3 Commits (4f7a64a79853863473a0ea1e0912db08b327ad3c)