Provided by: libvcflib-tools_1.0.9+dfsg1-2_amd64 
NAME
vcfwave - reduces complex alleles by pairwise alignment with BiWFA
SYNOPSIS
vcfwave
DESCRIPTION
vcfwave reduces complex alleles into simpler primitive representation using pairwise
alignment with BiWFA.
Often variant callers are not perfect. vcfwave with its companion tool vcfcreatemulti can
take an existing VCF file that contains multiple complex overlapping and even nested
alleles and, like Humpty Dumpty, can take them apart and put them together again in a more
sane VCF output. Thereby getting rid of false positives and often greatly simplifying the
output. We created these tools for the output from long-read pangenome genotypers - with
10K base pair realignments - and is used in the Human Pangenome Reference Consortium
analyses (HPRC).
vcfwave realigns reference and alternate alleles with the recently introduced super fast
bi-wavefront aligner (WFA). vcfwave parses out the original `primitive’ alleles into
multiple VCF records and vcfcreatemulti puts them together again. These tools can handle
insertions, deletions, inversions and nested sequences. In both tools information is
tracked on original positions and genotypes are handled. New records have IDs that
reference the source record ID. Deletion alleles will result in haploid (missing allele)
genotypes overlapping the deleted region.
A typical workflow will call vcfwave to realign all ALT alleles against the reference and
spit out simplified VCF records. Next use a tool such as bcftools norm -m- to normalise
the VCF records and split out multiple ALT alleles into separate VCF records. Finally use
vcfcreatemulti to create multi-allele VCF records again.
PERFORMANCE:
Unlike traditional aligners that run in quadratic time, the recently introduced wavefront
aligner WFA runs in time O(ns+s^2), proportional to the sequence length n and the
alignment score s, using O(s^2) memory (or O(s) using the ultralow/BiWFA mode). Therefore
WFA does not choke on longer alignments.
Speed-wise vcfwave can still be faster. See also the performance docs for some metrics
and discussion.
READING:
See also the humpty dumpty companion tool vcfcreatemulti.
Options
-h, –help
shows help message and exits.
See more below.
EXIT VALUES
0 Success
not 0 Failure
EXAMPLES
Current command line options:
>>> head("vcfwave -h",26)
>
usage: vcfwave [options] [file]
>
Realign reference and alternate alleles with WFA, parsing out the
'primitive' alleles into multiple VCF records. New records have IDs that
reference the source record ID. Genotypes/samples are handled
correctly. Deletions generate haploid/missing genotypes at overlapping
sites.
>
options:
-p, --wf-params PARAMS use the given BiWFA params (default: 0,19,39,3,81,1)
format=match,mismatch,gap1-open,gap1-ext,gap2-open,gap2-ext
-f, --tag-parsed FLAG Annotate decomposed records with the source record position
(default: ORIGIN).
-L, --max-length LEN Do not manipulate records in which either the ALT or
REF is longer than LEN (default: unlimited).
-K, --inv-kmer K Length of k-mer to use for inversion detection sketching (default: 17).
-I, --inv-min LEN Minimum allele length to consider for inverted alignment (default: 64).
-t, --threads N Use this many threads for variant decomposition (default is 1).
For most datasets threading may actually slow vcfwave down.
--quiet Do not display progress bar.
-d, --debug Debug mode.
>
Note the -k,--keep-info switch is no longer in use and ignored.
>
Type: transformation
vcfwave picks complex regions and simplifies nested alignments. For example:
>>> sh("grep 10158243 ../samples/10158243.vcf")
grch38#chr4 10158243 >3655>3662 ACCCCCACCCCCACC ACC,AC,ACCCCCACCCCCAC,ACCCCCACC,ACA 60 . AC=64,3,2,3,1;AF=0.719101,0.0337079,0.0224719,0.0337079,0.011236;AN=89;AT=>3655>3656>3657>3658>3659>3660>3662,>3655>3656>3660>3662,>3655>3660>3662,>3655>3656>3657>3658>3660>3662,>3655>3656>3657>3660>3662,>3655>3656>3661>3662;NS=45;LV=0 GT 0|0 1|1 1|1 1|0 5|1 0|4 0|1 0|1 1|1 1|1 1|1 1|1 1|1 1|1 1|1 4|3 1|1 1|1 1|1 1|0 1|0 1|0 1|0 1|1 1|1 1|4 1|1 1|1 3|0 1|0 1|1 0|1 1|1 1|1 2|1 1|2 1|1 1|1 0|1 1|1 1|1 1|0 1|2 1|1 0
This aligns and adjusts the genotypes accordingly splitting into multiple records, one for
each unique allele found in the alignments:
>>> sh("../build/vcfwave -L 1000 ../samples/10158243.vcf|grep -v ^\#")
grch38#chr4 10158244 >3655>3662_1 CCCCCACCCCCAC C 60 . AC=1;AF=0.011236;AN=89;AT=>3655>3656>3657>3660>3662;NS=45;LV=0;ORIGIN=grch38#chr4:10158243;LEN=12;INV=0;TYPE=del GT 0|0 0|0 0|0 0|0 1|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0
grch38#chr4 10158244 >3655>3662_2 CCCCCACCCCCACC C 60 . AC=3;AF=0.033708;AN=89;AT=>3655>3656>3660>3662;NS=45;LV=0;ORIGIN=grch38#chr4:10158243;LEN=13;INV=0;TYPE=del GT 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 1|0 0|1 0|0 0|0 0|0 0|0 0|0 0|0 0|1 0|0 0
grch38#chr4 10158245 >3655>3662_3 CCCCACCCCCACC C 60 . AC=64;AF=0.719101;AN=89;AT=>3655>3656>3657>3658>3659>3660>3662;NS=45;LV=0;ORIGIN=grch38#chr4:10158243;LEN=12;INV=0;TYPE=del GT 0|0 1|1 1|1 1|0 0|1 0|0 0|1 0|1 1|1 1|1 1|1 1|1 1|1 1|1 1|1 0|0 1|1 1|1 1|1 1|0 1|0 1|0 1|0 1|1 1|1 1|0 1|1 1|1 0|0 1|0 1|1 0|1 1|1 1|1 0|1 1|0 1|1 1|1 0|1 1|1 1|1 1|0 1|0 1|1 0
grch38#chr4 10158251 >3655>3662_4 CCCCACC C 60 . AC=3;AF=0.033708;AN=89;AT=>3655>3656>3657>3658>3660>3662;NS=45;LV=0;ORIGIN=grch38#chr4:10158243;LEN=6;INV=0;TYPE=del GT 0|0 0|0 0|0 0|0 0|0 0|1 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 1|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|1 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0
grch38#chr4 10158256 >3655>3662_5 CC C 60 . AC=2;AF=0.022472;AN=89;AT=>3655>3660>3662;NS=45;LV=0;ORIGIN=grch38#chr4:10158243;LEN=1;INV=0;TYPE=del GT 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|1 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 1|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0
grch38#chr4 10158257 >3655>3662_6 C A 60 . AC=1;AF=0.011236;AN=89;AT=>3655>3656>3657>3660>3662;NS=45;LV=0;ORIGIN=grch38#chr4:10158243;LEN=1;INV=0;TYPE=snp GT 0|0 .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. .|. 0
./vcfwave -L 10000 ../samples/grch38#chr8_36353854-36453166.vcf >
../test/data/regression/vcfwave_4.vcf
run_stdout(“vcfwave -L 10000
../samples/grch38#chr8_36353854-36453166.vcf”, ext=“vcf”) output in
vcfwave_4.vcf
./vcfwave -L 10000 ../samples/grch38#chr4_10083863-10181258.vcf >
../test/data/regression/vcfwave_5.vcf
run_stdout(“vcfwave -L 10000
../samples/grch38#chr4_10083863-10181258.vcf”, ext=“vcf”) output in
vcfwave_5.vcf
## Inversions
We can also handle inversions.
This test case includes one that was introduced by building a variation graph with an inversion and then decomposing it into a VCF with `vg deconstruct` and finally "popping" the inversion variant with [`vcfbub`](https://github.com/pangenome/vcfbub).
From
a 281 >1>9 AGCCGGGGCAGAAAGTTCTTCCTTGAATGTGGTCATCTGCATTTCAGCTCAGGAATCCTGCAAAAGACAG
CTGTCTTTTGCAGGATTCCTGTGCTGAAATGCAGATGACCGCATTCAAGGAAGAACTATCTGCCCCGGCT 60 .
AC=1;AF=1;AN=1;AT=>1>2>3>4>5>6>7>8>9,>1<8>10<6>11<4>12<2>9;NS=1;LV=0 GT 1
To
```python
>>> sh("../build/vcfwave ../samples/inversion.vcf|grep -v ^\#|head -3")
a 293 >1>9_1 A T 60 . AC=1;AF=1.000000;AN=1;AT=>1>2>3>4>5>6>7>8>9;NS=1;LV=0;ORIGIN=a:281;LEN=1;INV=1;TYPE=snp GT 1
a 310 >1>9_2 T C 60 . AC=1;AF=1.000000;AN=1;AT=>1>2>3>4>5>6>7>8>9;NS=1;LV=0;ORIGIN=a:281;LEN=1;INV=1;TYPE=snp GT 1
a 329 >1>9_3 T A 60 . AC=1;AF=1.000000;AN=1;AT=>1>2>3>4>5>6>7>8>9;NS=1;LV=0;ORIGIN=a:281;LEN=1;INV=1;TYPE=snp GT 1
LICENSE
Copyright 2022-2023 (C) Erik Garrison, Pjotr Prins and vcflib contributors. MIT licensed.
AUTHORS
Erik Garrison, Pjotr Prins and other vcflib contributors.