Robust detection of clinically relevant structural and copy number variation from whole genome sequencing data
Microarrays have been the mainstay for detecting clinically relevant copy number variants (CNV) in patients. Whole genome sequencing (WGS) has the potential to provide far higher resolution of CNV detection and to resolve structural variation (SV) invisible to current microarrays. Current WGS-based approaches however have high error rates, poor reproducibility, and difficulties in annotating, visualizing, and prioritizing rare variants.
We developed ClinSV to overcome these challenges, enabling the use of WGS to identify short, and large CNV and balanced SV, with high analytical sensitivity, reproducibility, and low false positive rates. ClinSV is designed to be easily integrated into production WGS analysis pipelines, and generate output which is easily interpreted by researchers and clinicians. We developed ClinSV mostly in the context of analysing WGS data from a single-lane of an Illumina HiSeq X sequencer, thus ~30-40x coverage. We focused mostly on the use of ClinSV to identify rare, gene-affecting variation in the context of rare genetic disease. We have used it to detect Mitochondrial SV, and somatic SV from tumour-normal paired WGS.
ClinSV has the following features:
- integration of three CNV signals: depth of coverage, split and spanning reads
- extensive quality attributes for CNV and SV
- CNV and copy-number neutral SV are assigned High, Pass, Low quality tranches
- variant segregation if a user-supplied PED file is supplied
- gene and phenotype annotation of each SV
- full, and focussed result tables for easy clinical interpretation
- Quality Control report
- Analytical validaiton report, if NA12878 is being analysed
- Multiple population allele frequency measures to help identify rare variants
- Visualisation framework via IGV and multiple supporting tracks
For 500 WGS samples of the Medical Genome Reference Bank population, allele frequencies were derived from split-reads, discordant pairs, depth of coverage changes and ClinSV calls. This allows to also filter out common low confident variant calls and sequencing artefacts.
Please refer to the manuscript for further details.
This repository contains the source code and Docker files required to run ClinSV version 1.0.0. This version only supports the GRCh38 reference genome. Please refer to release v0.9 to use ClinSV with reference genome GRCh37 decoy (hs37d5). A future version 1.1.0 will allow any reference genome to be used.
Download human genome reference data GRCh38 (37GB):
wget https://clinsv.s3.ccia.org.au/clinsv_b38/refdata-b38_v1.0.tar
tar xf refdata-b38_v1.0.tar
Download a sample bam to test ClinSV (71GB):
wget https://clinsv.s3.ccia.org.au/clinsv_b38/NA12878_b38.bam
wget https://clinsv.s3.ccia.org.au/clinsv_b38/NA12878_b38.bam.bai
or here is a smaller BAM file (4.7GB)
wget https://clinsv.s3.ccia.org.au/clinsv_b38/NA12878.grch38.subsampled.bam
wget https://clinsv.s3.ccia.org.au/clinsv_b38/NA12878.grch38.subsampled.bam.bai
The easiest way to run ClinSV is via Docker. If you really want to compile from source, then see INSTALL_b38.md.
In order to use the docker image your working directory needs to be set up as below:
Current working directory
| |contains your bam files *.bam
| |contains your bai files *.bai
└───clinsv
| |
| └───refdata-b38
| | |
| | └───refdata-b38
| | | | /all of refdata-b38's content/
|
└───test_run
| this is where clinsv generates all its output
Its important to note that the provided b38 ref data (after extracting) needs to be put within a parent folder of the same name as described above.
docker pull mrbradley2/clinsv:v1.0.0
refdata_path=$PWD/clinsv/refdata-b38
input_path=$PWD
project_folder=$PWD/test_run
docker run -v $refdata_path:/app/ref-data \
-v $project_folder:/app/project_folder \
-v $input_path:/app/input \
--entrypoint "perl" mrbradley2/clinsv:v1.0 /app/clinsv/bin/clinsv \
-r all \
-p /app/project_folder/ \
-i "/app/input/*.bam" \
-ref /app/ref-data/refdata-b38 \
Expect this to ~8 hours for a 30x WGS file.
-p Project folder [current_dir].
-r Analysis steps to run [all]. All is equivalent to bigwig,lumpy,cnvnator,annotate,prioritize,qc,igv
Multiple steps must be comma separated with no spaces in-between.
-i Path to input bams [./input/*.bam]. Requires bam index ending to be \"*.bam.bai.\".
Bam and index files can also be soft-links.
-s Sample information file [./sampleInfo.txt] If not set and if not already present,
such file gets generated from bam file names.
-f Force specified analysis step(s) and overwrite existing output.
-a Ask for confirmation before launching next analysis step.
-n Name stem for joint-called files (e.g joint vcf file) in case different sample grouping exists.
This is necessary if different sets of samples specified wtih -s are analysed within the same
project folder, E.g. a family trio and a set of single proband individuals.
-l Lumpy batch size. Number of sampels to be joint-called [15].
-ref Path to reference data dir [./refdata-b37].
-eval Create the NA12878 validation report section [no].
-h print this help
When providing a pedigree file, the output will contain additional columns showing e.g. how often a variant was observed among affected and unaffected individuals. The pedigree file has to be named "sampleInfo.ped" and it has to be placed into the project folder.
To mark variants affecting user defined candidate genes, a gene list list has to be placed into the project folder and named "testGene.ids". Gene names have to be as in ENSEMBL GRCh37.
ClinSV version 1.1.0 is currently under development and aims to make major usability improvements such as:
- Ability to use both GRCh37 and GRCh38 reference genomes
- Support for hg19 style chromosome names for v37 or v38 reference genomes
- XML bug fixes to correct publicly hosted resource files
Its development is kept track in this issue with most usability improvements implemented, however its is still buggy. Its docker container can be pulled from:
docker pull containerregistrypubliccb.azurecr.io/clinsv:v1.1-dev.
Usage information for new features is currently shown in linked enhancement issues in the main development issues described previously.
Install and usage instructions for ClinSV v0.9
Download human genome reference data GRCh37 decoy (hs37d5):
wget https://clinsv.s3.ccia.org.au/clinsv_b37/refdata-b37_v0.9.tar
# check md5sum: 921ecb9b9649563a16e3a47f25954951
tar xf refdata-b37_v0.9.tar
refdata_path=$PWD/clinsv/refdata-b37
Download a sample bam to test ClinSV:
wget https://clinsv.s3.ccia.org.au/clinsv_b37/NA12878_v0.9.bam
wget https://clinsv.s3.ccia.org.au/clinsv_b37/NA12878_v0.9.bam.bai
input_path=$PWD
The ClinSV software can be downloaded precompiled, as a Singularity image or through Docker. Please refer to the section below.
wget https://clinsv.s3.ccia.org.au/clinsv_b37/clinsv.sif
singularity run clinsv.sif \
-i "$input_path/*.bam" \
-ref $refdata_path \
-p $PWD/project_folder
docker pull kccg/clinsv
project_folder=$PWD/test_run
docker run \
-v $refdata_path:/app/ref-data \
-v $project_folder:/app/project_folder \
-v $input_path:/app/input \
kccg/clinsv -r all \
-i "/app/input/*.bam" \
-ref $refdata_path:/app/ref-data \
-p $project_folder:/app/project_folder
Download precompiled ClinSV bundle for CentOS 6.8 x86_64
wget https://clinsv.s3.ccia.org.au/clinsv_b37/ClinSV_x86_64_v0.9.tar.gz
tar zxf ClinSV_x86_64_v0.9.tar.gz
clinsv_path=$PWD/clinsv
export PATH=$clinsv_path/bin:$PATH
clinsv -r all -p $PWD/project_folder -i "$input_path/*.bam" -ref $refdata_path
see INSTALL.md
- based on 30-40x WGS (80GB BAM file): 16 CPUs, 60GB RAM, 200 GB storage
results/sample.QC_report.pdf
Quality control metrics, including a detailed description.
results/sample.RARE_PASS_GENE.xlsx
Rare gene affecting variants, one variant per line. Recommended to open in Excel or OpenOffice calc.
SVs/joined/SV-CNV.vcf, .txt or .xlsx
All variants
For instructions on how to interprete the results, see:
results/result_description.docx
and the manuscript (see section citation)
igv/sample.xml
This IGV genome browser session file contains paths to supporting data files necessary for manual inspection of variants. There are tracks from static annotation files and those from your sample(s) of interest.
If ClinSV was executed on a remote computer, like an HPC, or within a docker container then the file paths might not work on your Desktop. The default option of -p /app/project_folder/ creates resource paths like this:
You have several options to improve this:
1: The -p parameter accepts two arguments, the first is the desired path on your desktop/localhost, the second is the path on the execution host where the job needs to run. For example -p /path/on/desktop:/app/project_folder/. In this case the session.xml will have
<Resource path="/path/on/desktop/test_run/igv/alignments/Sample/bw/Sample.q0.bw"/>
Once you copy the results to /path/on/desktop, the session file will now work.
2: paths can be relative to the igv xml file, so -p ..:/app/project_folder/ will create:
<Resource path="../alignments/Sample/bw/Sample.q0.bw"/>
3: manually replace the paths in the XML file with a perl regex, eg perl -pi -e 's|/app/project_folder/|/path/on/desktop/|g' $xml
4: mount the remote folder on your desktop (eg sshfs) using the same folder structure
When the IGV application is open, the hyperlinks within the sample.RARE_PASS_GENE.xlsx file will open session files and to navigate to variants.
For more information please see the publication.
- Does ClinSV support long read data (Nanopore or PacBio)? No.
- Does ClinSV work on targeted short read NGS data (eg WES or panels)? No, it only works on WGS.
- Does ClinSV work on NovaSeq data? Yes it should be fine, but the control data was generated on HiSeq X & much of the strength of ClinSV is removing the noise that can happen when searching genome-wide.
- Why does my BAM not work? You must have one sample name 'SM' defined in the BAM header.
- Can I run hundreds of BAM files through ClinSV? We mostly tested ClinSV on trios or small numbers of WGS, so this probably won't work.
- Will you support CRAM? Yes, one day.
- Can I use hg19? No. v0.9 allows analysis against the hs37d5 ref genome (and the b37), where chrom names are 1, 2, ..., X, Y, MT. V1.0.x supports grch38, where chrom names are chr1,chr2,...,chrX,chrY.
- Do you support alt/no alts? ClinSV should accept any of the versions of GRCh38, but will only analyse CNV or SV on the autosomes, and allosomes (X and Y).
- Will ClinSV work on model organisms? We've never tried. The annotation files and control data are important features of ClinSV, so it probably isn't the best choice.
ClinSV is free for research and education purposes, please refer to ClinSV licence agreement for full terms. For clinical or commercial use, please contact bdi[at]garvan.org.au for additional information.
Minoche AE, Lundie B, Peters GB, Ohnesorg T, Pinese M, Thomas DM, et al. ClinSV: clinical grade structural and copy number variant detection from whole genome sequencing data. Genome Medicine. 2021;13:32.