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

Download

Download human genome reference data GRCh37 decoy (hs37d5):

wget https://nci.space/clinsv/refdata-b37_v0.9.tar
tar xf refdata-b37_v0.9.tar
refdata_path=$PWD/clinsv/refdata-b37

Download a sample bam to test ClinSV:

wget https://nci.space/clinsv/NA12878_v0.9.bam
wget https://nci.space/clinsv/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.

Run ClinSV

Using Singularity

wget https://nci.space/clinsv/clinsv.sif 
singularity run clinsv.sif \
  -i "$input_path/*.bam" \
  -ref $refdata_path \
  -p $PWD/project_folder

Using Docker

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 \
  docker run clinsv -r all \
-i "/app/input/*.bam" \
-ref $refdata_path:/app/ref-data \
-p $project_folder:/app/project_folder

Linux Native

Download precompiled ClinSV bundle for CentOS 6.8 x86_64

wget https://nci.space/clinsv/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

Compile dependencies from source

see INSTALL.md

ClinSV options

-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

Advanced options

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.

Hardware requirements

• based on 30-40x WGS (80GB BAM file): 16 CPUs, 60GB RAM, 200 GB storage

Output

QC report

results/sample.QC_report.pdf

Qualtiy control metrics, including a detailed description.

Variant files

results/sample.RARE_PASS_GENE.xlsx

Rare gene affecting variants, one variant per line. Recommended to open in OpenOffice calc.

SVs/joined/SV-CNV.vcf, .txt or .xlsx

All variants

Results description

For instructions on how to interprete the results, see:

results/result_description.docx

and the manuscript (see section citation)

IGV session file

igv/sample.xml

This IGV genome browser session file contains paths to evidence data files necessary for manual inspection of variants.

If ClinSV was executed on a remote computer, it is recommended to mount the results folder on your desktop computer preserving the path.

When the IGV application is open, the hyperlinks in sample.RARE_PASS_GENE.xlsx facilitate to open session files and to navigate to variants, if pasted into a spreadsheet program.

For more information please see the publication.

Licence

ClinSV is free for research and education purposes. For clinical or commercial use, please see the ClinSV licence for more details.

Citation

Minoche et al. 2019 Manuscript under revision