The paper describing duphold is available here
SV callers like lumpy look at split-reads and pair distances to find structural variants. This tool is a fast way to add depth information to those calls. This can be used as additional information for filtering variants; for example we will be skeptical of deletion calls that do not have lower than average coverage compared to regions with similar gc-content.
In addition, duphold will annotate the SV vcf with information from a SNP/Indel VCF. For example, we will not
believe a large deletion that has many heterozygote SNP calls.
duphold takes a bam/cram, a VCF/BCF of SV calls, and a fasta reference and it updates the FORMAT field for a
single sample with:
- DHFC: fold-change for the variant depth relative to the rest of the chromosome the variant was found on
- DHBFC: fold-change for the variant depth relative to bins in the genome with similar GC-content.
- DHFFC: fold-change for the variant depth relative to Flanking regions.
It also adds GCF to the INFO field indicating the fraction of G or C bases in the variant.
After annotating with duphold, a sensible way to filter to high-quality variants is:
bcftools view -i '(SVTYPE = "DEL" & FMT/DHFFC[0] < 0.7) | (SVTYPE = "DUP" & FMT/DHBFC[0] > 1.3)' $svvcf
In our evaluations, DHFFC works best for deletions and DHBFC works slightly better for duplications.
For genomes/samples with more variable coverage, DHFFC should be the most reliable.
NOTE it is strongly recommended to use BCF for the --snp argument as otherwise VCF parsing will be a bottleneck.
- A DEL call with many HETs is unlikely to be valid.
When the user specifies a --snp VCF, duphold finds the appropriate sample in that file and extracts high (> 20) quality, bi-allelic
SNP calls and for each SV, it reports the number of hom-refs, heterozygote, hom-alt, unknown, and low-quality snp calls
in the region of the event. This information is stored in 5 integers in DHGT.
When a SNP/Indel VCF/BCF is given, duphold will annotate each DEL/DUP call with:
- DHGT: counts of [0] Hom-ref, [1] Het, [2] Homalt, [3] Unknown, [4] low-quality variants in the event. A heterozygous deletion may have more hom-alt SNP calls. A homozygous deletion may have only unknown or low-quality SNP calls.
In practice, this has had limited benefit for us. The depth changes are more informative.
duphold runtime depends almost entirely on how long it takes to parse the BAM/CRAM files; it is relatively independent of the number of variants evaluated. It will also run quite a bit faster on CRAM than on BAM. It can be < 20 minutes of CPU time for a 30X CRAM.
Evaluting on the genome in a bottle truthset for DEL calls larger than 300 bp:
| method | FDR | FN | FP | TP-call | precision | recall | recall-% | FP-% |
|---|---|---|---|---|---|---|---|---|
| unfiltered | 0.054 | 276 | 86 | 1496 | 0.946 | 0.844 | 100.000 | 100.000 |
| DHBFC < 0.7 | 0.018 | 298 | 27 | 1474 | 0.982 | 0.832 | 98.529 | 31.395 |
| DHFFC < 0.7 | 0.021 | 289 | 32 | 1483 | 0.979 | 0.837 | 99.131 | 37.209 |
Note that filtering on DHFFC < 0.7 retains 99.1% of true positives and removes 62.8% (100 - 37.2) of false positives
This was generated using truvari.py with the command:
truvari.py --sizemax 15000000 -s 300 -S 270 -b HG002_SVs_Tier1_v0.6.DEL.vcf.gz -c $dupholded_vcf -o $out \
--passonly --pctsim=0 -r 20 --giabreport -f $fasta --no-ref --includebed HG002_SVs_Tier1_v0.6.bed -O 0.6
For deletions >= 1KB, duphold does even better:
| method | FDR | FN | FP | TP-call | precision | recall | recall-% | FP-% |
|---|---|---|---|---|---|---|---|---|
| unfiltered | 0.073 | 46 | 38 | 486 | 0.927 | 0.914 | 100.000 | 100.000 |
| DHBFC < 0.7 | 0.012 | 54 | 6 | 478 | 0.988 | 0.898 | 98.354 | 15.789 |
| DHFFC < 0.7 | 0.012 | 53 | 6 | 479 | 0.988 | 0.900 | 98.560 | 15.789 |
Note that filtering on DHFFC < 0.7 retains 98.5% of DEL calls that are also in the truth-set (TPs) and
removes 84.2% (100 - 15.8) of calls not in the truth-set (FPs)
The truvari.py command used for this is the same as above except for: -s 1000 -S 970
duphold is distributed as a static binary here.
duphold -s $gatk_vcf -t 4 -v $svvcf -b $cram -f $fasta -o $output.bcf
duphold --snp $gatk_bcf --threads 4 --vcf $svvcf --bam $cram --fasta $fasta --output $output.bcf
--snp can be a multi-sample VCF/BCF. duphold will be much faster with a BCF, especially if
the snp/indel file contains many (>20 or so) samples.
the threads are decompression threads so increasing up to about 4 works.
Full usage is available with duphold -h
duphold runs on a single-sample, but you can install smoove and run smoove duphold
to parallelize across many samples.
Here is a duplication with clear change in depth (DHBFC)
duphold annotated this with
- DHBFC: 1.79
where together these indicate rapid (DUP-like) change in depth at the break-points and a coverage that 1.79 times higher than the mean for the genome--again indicative of a DUP. Together, these recapitulate (or anticipate) what we see on visual inspection.
A clear deletion will have rapid drop in depth at the left and increase in depth at the right and a lower mean coverage.
duphold annotated this with:
- DHBFC: 0.6
These indicate that both break-points are consistent with a deletion and that the coverage is ~60% of expected. So this is a clear deletion.
when lumpy decides that a cluster of evidence does not match a DUP or DEL or INV, it creates a BND with 2 lines in the VCF. Sometimes these are actual deletions. For example:
shows where a deletion is bounded by 2 BND calls. duphold annotates this with:
- DHBFC: 0.01
indicating a homozygous deletion with clear break-points.
The default flank is 1000 bases. If the environment variable DUPHOLD_FLANK is set to an integer, that
can be used instead. In our experiments, this value should be large enough that duphold can get a good estimate
of depth, but small enough that it is unlikely to extend into an unmapped region or another event.
This may be lowered for genomes with poor assemblies.
If the sample name in your bam does not match the one in the VCF (tisk, tisk). You can use DUPHOLD_SAMPLE_NAME
environment variable to set the name to use.
I stole the idea of annotating SVs with depth-change from Ira Hall.