jvarkit
VcfStats
Produce VCF statitics
Usage
Usage: vcfstats [options] Files
Options:
--binSize
[20170718] When plotting data over a genome, divide it into 'N' bp.
Default: 1000000
--disableGTConcordance
Disable Plot Sample vs Sample Genotypes (Faster...)
Default: false
--disableMAFPlot
Disable MAF plot
Default: false
-h, --help
print help and exit
--helpFormat
What kind of help. One of [usage,markdown,xml].
-K, -kg, --knownGenes
UCSC knownGene File/URL. The knowGene format is a compact alternative to
GFF/GTF because one transcript is described using only one line. Beware
chromosome names are formatted the same as your REFERENCE. A typical
KnownGene file is
http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/knownGene.txt.gz
.If you only have a gff file, you can try to generate a knownGene file
with [http://lindenb.github.io/jvarkit/Gff2KnownGene.html](http://lindenb.github.io/jvarkit/Gff2KnownGene.html)
-mafTag, --mafTag
Do not calculate MAF for controls, but use this tag to get Controls' MAF
-nchr, --nocallishomref
treat no call as HomRef
Default: false
* -o, --output
output Directory or zip file. The output contains the data files as well
as a Makefile to convert the data files to graphics using gnuplot.
-ped, --pedigree
A pedigree is a text file delimited with tabs. No header. Columns are
(1) Family (2) Individual-ID (3) Father Id or '0' (4) Mother Id or '0'
(5) Sex : 1 male/2 female / 0 unknown (6) Status : 0 unaffected, 1
affected,-9 unknown
--prefix
File/zip prefix
Default: <empty string>
-so, --soterms
Sequence ontology Accession to observe. VCF must be annotated with
SNPEFF or VEP. e.g: "SO:0001818" (protein altering variant) "SO:0001819"
(synonymouse variant)
Default: []
-tee, --tee
output the incoming vcf to stdout. Useful to get intermediary stats in a
pipeline
Default: false
--trancheAffected
tranches for the number of affected. A 'range of integers' is a list of
integers in ascending order separated with semicolons.
Default: [[-Inf/0[, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, [10/20[, [20/50[, [50/100[, [100/200[, [200/300[, [300/400[, [400/500[, [500/1000[, [1000/Inf[]
--trancheAlts
tranches for the number of ALTs. A 'range of integers' is a list of
integers in ascending order separated with semicolons.
Default: [[-Inf/0[, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, [10/Inf[]
--trancheDP
tranches for the DEPTH. A 'range of integers' is a list of integers in
ascending order separated with semicolons.
Default: [[-Inf/0[, [0/10[, [10/20[, [20/30[, [30/50[, [50/100[, [100/200[, [200/300[, [300/400[, [400/500[, [500/600[, [600/700[, [700/800[, [800/900[, [900/1000[, [1000/2000[, [2000/3000[, [3000/4000[, [4000/5000[, [5000/10000[, [10000/20000[, [20000/30000[, [30000/40000[, [40000/50000[, [50000/100000[, [100000/Inf[]
--trancheDistance
tranches for the distance between the variants. A 'range of integers' is
a list of integers in ascending order separated with semicolons.
Default: [[-Inf/0[, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, [10/20[, [20/100[, [100/200[, [200/300[, [300/400[, [400/500[, [500/1000[, [1000/Inf[]
--trancheIndelSize
tranches for the Indel size A 'range of integers' is a list of integers
in ascending order separated with semicolons.
Default: [[-Inf/0[, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, [10/15[, [15/20[, [20/50[, [50/100[, [100/Inf[]
--vckey
Variant Context Key. if defined, I will look at this key in the INFO
column and produce a CASE/CTRL graf for each item. If undefined, I will
produce a default graph with all variant
--version
print version and exit
Keywords
- vcf
- stats
- burden
- gnuplot
Compilation
Requirements / Dependencies
- java compiler SDK 11. Please check that this java is in the
${PATH}. Setting JAVA_HOME is not enough : (e.g: https://github.com/lindenb/jvarkit/issues/23 )
Download and Compile
$ git clone "https://github.com/lindenb/jvarkit.git"
$ cd jvarkit
$ ./gradlew vcfstats
The java jar file will be installed in the dist directory.
Source code
Unit Tests
Contribute
- Issue Tracker: http://github.com/lindenb/jvarkit/issues
- Source Code: http://github.com/lindenb/jvarkit
License
The project is licensed under the MIT license.
Citing
Should you cite vcfstats ? https://github.com/mr-c/shouldacite/blob/master/should-I-cite-this-software.md
The current reference is:
http://dx.doi.org/10.6084/m9.figshare.1425030
Lindenbaum, Pierre (2015): JVarkit: java-based utilities for Bioinformatics. figshare. http://dx.doi.org/10.6084/m9.figshare.1425030
Tip: Adding a new key in the INFO field
Using vcffilterjs :
the script:
var ArrayList = Java.type("java.util.ArrayList");
var VariantContextBuilder = Java.type("htsjdk.variant.variantcontext.VariantContextBuilder");
function addInfo(v)
{
var vcb = new VariantContextBuilder(v);
var atts = new ArrayList();
atts.add(v.getType().name()+ (variant.isFiltered()?"_FILTERED":"_UNFILTERED"));
atts.add(v.getType().name()+ (variant.hasID()?"_ID":"_NOID"));
vcb.attribute("MYKEY",atts);
return vcb.make();
}
addInfo(variant);
run the program, but first use awk to insert the new INFO definition for ‘MYKEY’
cat input.vcf |\
awk '/^#CHROM/ {printf("##INFO=<ID=MYKEY,Number=.,Type=String,Description=\"My key\">\n");} {print;}' |\
java -jar dist/vcffilterjs.jar -f script.js
Example
$ java -jar $< -o tmp --soterms SO:0001818 --soterms SO:0001819 -K ucsc/hg19/database/knownGene_noPrefix.txt.gz input.vcf
$ (cd tmp && make)
$ ls tmp/
ALL.affectedSamples.png ALL.countDepthBySample.tsv ALL.countDistances.png ALL.geneLoc.tsv ALL.predictionsBySample.png ALL.sample2gtype.tsv Makefile
ALL.affectedSamples.tsv ALL.countDepth.png ALL.countDistances.tsv ALL.maf.png ALL.predictionsBySample.tsv ALL.transvers.png
ALL.countAltAlleles.png ALL.countDepth.tsv ALL.countIndelSize.png ALL.maf.tsv ALL.predictions.png ALL.transvers.tsv
ALL.countAltAlleles.tsv ALL.countDistancesBySample.png ALL.countIndelSize.tsv ALL.mendel.png ALL.predictions.tsv ALL.variant2type.png
ALL.countDepthBySample.png ALL.countDistancesBySample.tsv ALL.geneLoc.png ALL.mendel.tsv ALL.sample2gtype.png ALL.variant2type.tsv
$ head -n3 tmp/*.tsv
==> tmp/ALL.affectedSamples.tsv <==
1/57 182265
2/57 98512
3/57 67449
==> tmp/ALL.countAltAlleles.tsv <==
2 11699
3 2406
4 679
==> tmp/ALL.countDepthBySample.tsv <==
Sample [-Inf/0[ [0/10[ [10/20[ [20/30[ [30/50[ [50/100[ [100/200[ [200/Inf[
10_T1245 0 305229 97739 61972 75791 72779 14008 686
11AG09 0 400147 62261 39411 55254 86883 86192 46749
==> tmp/ALL.countDepth.tsv <==
[0/10[ 19435
[10/20[ 95369
[20/30[ 92378
==> tmp/ALL.countDistancesBySample.tsv <==
Sample [-Inf/0[ 0 1 2 3 4 5 6 7 8 9 [10/20[ [20/100[ [100/200[ [200/300[ [300/400[ [400/500[ [500/1000[ [1000/Inf[
S1 0 0 2643 1755 1491 1430 1156 1106 893 858 867 6826 26696 18252 12411 9348 7270 20975 85769
S2 0 0 3632 2397 1967 1855 1516 1425 1246 1179 1109 8917 36006 25455 18652 14951 11912 37828 103341
==> tmp/ALL.countDistances.tsv <==
1 16275
2 11184
3 8505
==> tmp/ALL.countIndelSize.tsv <==
2 59420
3 22727
4 10216
==> tmp/ALL.geneLoc.tsv <==
first_exon 51180
internal_exon 70074
last_exon 75341
==> tmp/ALL.maf.tsv <==
0.029411764705882353 0.027777777777777776
0.5 0.43333333333333335
0.05263157894736842 0.0
==> tmp/ALL.mendel.tsv <==
Sample synonymous_variant protein_altering_variant
10_T1245 0 0
11AG09 170 298
==> tmp/ALL.predictionsBySample.tsv <==
Sample synonymous_variant protein_altering_variant
S1 13453 14527
S2 12820 14077
==> tmp/ALL.predictions.tsv <==
protein_altering_variant 59516
synonymous_variant 47454
==> tmp/ALL.sample2gtype.tsv <==
Sample NO_CALL HOM_REF HET HOM_VAR UNAVAILABLE MIXED
S1 345776 429002 98825 100945 0 0
S2 196925 504211 132576 140836 0 0
==> tmp/ALL.transvers.tsv <==
TYPE TRANSITION TRANSVERSION
ALL 581537 133472
CDS 533340 120720
==> tmp/ALL.variant2type.tsv <==
Type Count
INDEL 126219
SNP 846677