Delly is an integrated structural variant (SV) prediction method that can discover, genotype and visualize deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends, split-reads and read-depth to sensitively and accurately delineate genomic rearrangements throughout the genome. Structural variants can be annotated using Delly-sansa and visualized using Delly-maze or Delly-suave.
The easiest way to get Delly is to download a statically linked binary or the singularity container (SIF file) from the Delly github release page. Alternatively, you can download Delly from Bioconda. You can also build Delly from source using a recursive clone and make.
git clone --recursive https://github.com/dellytools/delly.git
Delly multi-threading mode
Delly supports parallel computing using the OpenMP API (www.openmp.org).
make PARALLEL=1 -B src/delly
You can set the number of threads using the environment variable OMP_NUM_THREADS.
Delly primarily parallelizes on the sample level. Hence, OMP_NUM_THREADS should be always smaller or equal to the number of input samples.
Delly needs a sorted, indexed and duplicate marked bam file for every input sample. An indexed reference genome is required to identify split-reads. The output is in BCF format with a csi index. Delly supports germline and somatic SV discovery, genotyping and filtering. Because of that, Delly has been modularized and common workflows for germline and somatic SV calling are outlined below. If you do need VCF output you need a recent version of BCFtools for file conversion .
delly call -x hg19.excl -o delly.bcf -g hg19.fa input.bam
bcftools view delly.bcf > delly.vcf
Somatic SV calling
- At least one tumor sample and a matched control sample are required for SV discovery
delly call -x hg19.excl -o t1.bcf -g hg19.fa tumor1.bam control1.bam
- Somatic pre-filtering requires a tab-delimited sample description file where the first column is the sample id (as in the VCF/BCF file) and the second column is either tumor or control.
delly filter -f somatic -o t1.pre.bcf -s samples.tsv t1.bcf
- Genotype pre-filtered somatic sites across a larger panel of control samples to efficiently filter false postives and germline SVs. For performance reasons, this can be run in parallel for each sample of the control panel and you may want to combine multiple pre-filtered somatic site lists from multiple tumor samples.
delly call -g hg19.fa -v t1.pre.bcf -o geno.bcf -x hg19.excl tumor1.bam control1.bam ... controlN.bam
- Post-filter for somatic SVs using all control samples.
delly filter -f somatic -o t1.somatic.bcf -s samples.tsv geno.bcf
Germline SV calling
- SV calling is done by sample for high-coverage genomes or in small batches for low-coverage genomes
delly call -g hg19.fa -o s1.bcf -x hg19.excl sample1.bam
- Merge SV sites into a unified site list
delly merge -o sites.bcf s1.bcf s2.bcf ... sN.bcf
- Genotype this merged SV site list across all samples. This can be run in parallel for each sample.
delly call -g hg19.fa -v sites.bcf -o s1.geno.bcf -x hg19.excl s1.bam
delly call -g hg19.fa -v sites.bcf -o sN.geno.bcf -x hg19.excl sN.bam
- Merge all genotyped samples to get a single VCF/BCF using bcftools merge
bcftools merge -m id -O b -o merged.bcf s1.geno.bcf s2.geno.bcf ... sN.geno.bcf
- Apply the germline SV filter which requires at least 20 unrelated samples
delly filter -f germline -o germline.bcf merged.bcf
Delly for long reads from PacBio or ONT
Delly also has a long-read (lr) SV discovery mode.
delly lr -y ont -g hg19.fa -x hg19.excl input.bam
delly lr -y pb -g hg19.fa -x hg19.excl input.bam
You can generate read-depth profiles with delly. This requires a mappability map which can be downloaded here:
The command to count reads in 10kbp mappable windows and normalize the coverage is:
delly cnv -a -g hg19.fa -m hg19.map input.bam
The output file
out.cov.gz can be plotted using R to generate normalized copy-number profiles:
Rscript R/rd.R out.cov.gz
Read-depth profiles can also be segmented at the same time.
delly cnv -a -u -g hg19.fa -m hg19.map input.bam
The segmentation is in VCF format but you can extract a BED-like file using bcftools.
bcftools query -f "%CHROM\t%POS\t%INFO/END\t%ID\t[%RDCN]\n" cnv.bcf > segmentation.bed
Rscript R/rd.R out.cov.gz segmentation.bed
Germline CNV calling
Delly uses GC and mappability fragment correction to call CNVs. This requires a mappability map.
- Call CNVs for each sample and optionally refine breakpoints using delly SV calls
delly cnv -o c1.bcf -g hg19.fa -m hg19.map -l delly.sv.bcf input.bam
- Merge CNVs into a unified site list
delly merge -e -p -o sites.bcf -m 1000 -n 100000 c1.bcf c2.bcf ... cN.bcf
- Genotype CNVs for each sample
delly cnv -u -v sites.bcf -g hg19.fa -m hg19.map -o geno1.bcf input.bam
- Merge genotypes using bcftools
bcftools merge -m id -O b -o merged.bcf geno1.bcf ... genoN.bcf
- Filter for germline CNVs
delly classify -f germline -o filtered.bcf merged.bcf
- Optional: Plot copy-number distribution for large number of samples (>>100)
bcftools query -f "%ID[\t%RDCN]\n" filtered.bcf > plot.tsv
Rscript R/cnv.R plot.tsv
Somatic copy-number alterations (SCNAs)
- For somatic copy-number alterations, delly first segments the tumor genome (
-uis required). Depending on the coverage, tumor purity and heterogeneity you can adapt parameters
-xwhich control the sensitivity of SCNA detection.
delly cnv -u -z 10000 -o tumor.bcf -c tumor.cov.gz -g hg19.fa -m hg19.map tumor.bam
- Then these tumor SCNAs are genotyped in the control sample (
delly cnv -u -v tumor.bcf -o control.bcf -g hg19.fa -m hg19.map control.bam
- The VCF IDs are matched between tumor and control. Thus, you can merge both files using bcftools.
bcftools merge -m id -O b -o tumor_control.bcf tumor.bcf control.bcf
- Somatic filtering requires a tab-delimited sample description file where the first column is the sample id (as in the VCF/BCF file) and the second column is either tumor or control.
delly classify -p -f somatic -o somatic.bcf -s samples.tsv tumor_control.bcf
- Optional: Plot the SCNAs using bcftools and R.
bcftools query -s tumor -f "%CHROM\t%POS\t%INFO/END\t%ID\t[%RDCN]\n" somatic.bcf > segmentation.bed
Rscript R/rd.R tumor.cov.gz segmentation.bed
What is the smallest SV size Delly can call?
This depends on the sharpness of the insert size distribution. For an insert size of 200-300bp with a 20-30bp standard deviation, Delly starts to call reliable SVs >=300bp. Delly also supports calling of small InDels using soft-clipped reads only, the smallest SV size called is 15bp.
Can Delly be used on a non-diploid genome?
Yes and no. The SV site discovery works for any ploidy. However, Delly's genotyping model assumes diploidy (hom. reference, het. and hom. alternative). The CNV calling allows to set the baseline ploidy on the command-line.
Delly is running too slowly what can I do?
You should exclude telomere and centromere regions and also all unplaced contigs. Delly ships with such an exclude list for human and mouse samples. In addition, you can filter input reads more stringently using -q 20 and -s 15. Lastly,
-zcan be set to 5 for high-coverage data.
Are non-unique alignments, multi-mappings and/or multiple split-read alignments allowed?
Delly expects two alignment records in the bam file for every paired-end, one for the first and one for the second read. Multiple split-read alignment records of a given read are allowed if and only if one of them is a primary alignment whereas all others are marked as secondary or supplementary (flag 0x0100 or flag 0x0800). This is the default for bwa mem.
What pre-processing of bam files is required?
Bam files need to be sorted, indexed and ideally duplicate marked.
Usage/discussion mailing list?
There is a delly discussion group delly-users.
dicey chop sacCer3.fa bwa index sacCer3.fa bwa mem sacCer3.fa read1.fq.gz read2.fq.gz | samtools sort -@ 8 -o srt.bam - samtools index srt.bam dicey mappability2 srt.bam gunzip map.fa.gz && bgzip map.fa && samtools faidx map.fa.gz
- Bioconda support?
Delly is available via bioconda.
Tobias Rausch, Thomas Zichner, Andreas Schlattl, Adrian M. Stuetz, Vladimir Benes, Jan O. Korbel.
DELLY: structural variant discovery by integrated paired-end and split-read analysis.
Bioinformatics. 2012 Sep 15;28(18):i333-i339.
Delly is distributed under the BSD 3-Clause license. Consult the accompanying LICENSE file for more details.