The CPSR software comes with a range of options to configure the report and analysis.
Key settings
Below, we will outline some key settings that are important for the generation of variant reports with CPSR.
Virtual gene panel
The user can choose the set of cancer predisposition genes for which input variants should be subject to classification and reporting. The input VCF with germline variants may thus come from any sequencing assay (whole-genome, whole-exome or targeted assay), yet the results shown in the report will always be restricted by the choice of a virtual gene panel:
--panel_id <panel_number>
Custom-made virtual gene panels
CPSR allows users to create custom virtual gene panels for reporting. Any set of genes found in the CPSR superpanel (panel 0) can be used to design a custom virtual gene panel. Technically, the users need to create a simple one-column text file with Ensembl gene identifiers, and provide a name for the custom panel:
--custom_list <custom_list_tsv>--custom_list_name <custom_list_name
The examples folder contains an example file for a custom-made virtual gene panel.
ACMG variant classification
The ACMG classification criteria rely heavily upon variant population frequencies from gnomAD. The user can specify the preferred population when it comes to variant allele frequencies used by CPSR:
--pop_gnomad <pop_code>
There is also an option to set an upper minor allele frequency limit (gnomAD - global population) for variants to be included in the report. This is basically a means to exclude the potentially large number of common and benign variants that can be found in the input VCF:
--maf_upper_threshold <maf_threshold>
By default, CPSR provides two types of variant classifications:
- Existing variant classifications - as provided by ClinVar
- CPSR-derived classifications of novel variants (not found in ClinVar)
The user may opt to show CPSR’s classification of the variants which are already attributed with classifications in ClinVar (i.e. set 1.):
--classify_all
In the final classification, CPSR’s classification will not overrule the classification reported by ClinVar, which takes precedence for these variants.
Some cancer predisposition genes may be associated with diseases or syndromes that are not directly cancer relevant. The user may choose to ignore/exclude any ClinVar variant associated with non-cancer phenotypes through a dedicated option:
--clinvar_ignore_noncancer
Optional report contents
CPSR allows users to report recommended incidental findings, and also the genotypes of reported cancer risk loci from genome-wide association studies (GWAS):
--secondary_findings--gwas_findings-
--gwas_p_value <p_value>(Required GWAS p-value for variants listed as hits)
All options
A cancer predisposition report is generated by running the cpsr command, which takes the following arguments and options:
usage:
cpsr -h [options]
--input_vcf <INPUT_VCF>
--pcgr_dir <PCGR_DIR>
--output_dir <OUTPUT_DIR>
--genome_assembly <GENOME_ASSEMBLY>
--sample_id <SAMPLE_ID>
Cancer Predisposition Sequencing Reporter - report of clinically significant cancer-predisposing germline variants
Required arguments:
--input_vcf INPUT_VCF
VCF input file with germline query variants (SNVs/InDels).
--pcgr_dir PCGR_DIR Directory that contains the PCGR data bundle directory, e.g. ~/pcgr_db
--output_dir OUTPUT_DIR
Output directory
--genome_assembly {grch37,grch38}
Genome assembly build: grch37 or grch38
--sample_id SAMPLE_ID
Sample identifier - prefix for output files
Panel options:
--panel_id VIRTUAL_PANEL_ID
Comma-separated string with identifier(s) of predefined virtual cancer predisposition gene panels,
choose any combination of the following identifiers:
0 = CPSR exploratory cancer predisposition panel
(n = 433, Genomics England PanelApp / TCGA Germline Study / Cancer Gene Census / Other)
1 = Adult solid tumours cancer susceptibility (Genomics England PanelApp)
2 = Adult solid tumours for rare disease (Genomics England PanelApp)
3 = Bladder cancer pertinent cancer susceptibility (Genomics England PanelApp)
4 = Brain cancer pertinent cancer susceptibility (Genomics England PanelApp)
5 = Breast cancer pertinent cancer susceptibility (Genomics England PanelApp)
6 = Childhood solid tumours cancer susceptibility (Genomics England PanelApp)
7 = Colorectal cancer pertinent cancer susceptibility (Genomics England PanelApp)
8 = Endometrial cancer pertinent cancer susceptibility (Genomics England PanelApp)
9 = Familial Tumours Syndromes of the central & peripheral Nervous system (Genomics England PanelApp)
10 = Familial breast cancer (Genomics England PanelApp)
11 = Familial melanoma (Genomics England PanelApp)
12 = Familial prostate cancer (Genomics England PanelApp)
13 = Familial rhabdomyosarcoma (Genomics England PanelApp)
14 = GI tract tumours (Genomics England PanelApp)
15 = Genodermatoses with malignancies (Genomics England PanelApp)
16 = Haematological malignancies cancer susceptibility (Genomics England PanelApp)
17 = Haematological malignancies for rare disease (Genomics England PanelApp)
18 = Head and neck cancer pertinent cancer susceptibility (Genomics England PanelApp)
19 = Inherited MMR deficiency (Lynch syndrome) - Genomics England PanelApp
20 = Inherited non-medullary thyroid cancer (Genomics England PanelApp)
21 = Inherited ovarian cancer (without breast cancer) (Genomics England PanelApp)
22 = Inherited pancreatic cancer (Genomics England PanelApp)
23 = Inherited polyposis (Genomics England PanelApp)
24 = Inherited predisposition to acute myeloid leukaemia (AML) - Genomics England PanelApp
25 = Inherited predisposition to GIST (Genomics England PanelApp)
26 = Inherited renal cancer (Genomics England PanelApp)
27 = Inherited phaeochromocytoma and paraganglioma (Genomics England PanelApp)
28 = Melanoma pertinent cancer susceptibility (Genomics England PanelApp)
29 = Multiple endocrine tumours (Genomics England PanelApp)
30 = Multiple monogenic benign skin tumours (Genomics England PanelApp)
31 = Neuroendocrine cancer pertinent cancer susceptibility (Genomics England PanelApp)
32 = Neurofibromatosis Type 1 (Genomics England PanelApp)
33 = Ovarian cancer pertinent cancer susceptibility (Genomics England PanelApp)
34 = Parathyroid Cancer (Genomics England PanelApp)
35 = Prostate cancer pertinent cancer susceptibility (Genomics England PanelApp)
36 = Renal cancer pertinent cancer susceptibility (Genomics England PanelApp)
37 = Rhabdoid tumour predisposition (Genomics England PanelApp)
38 = Sarcoma cancer susceptibility (Genomics England PanelApp)
39 = Sarcoma susceptibility (Genomics England PanelApp)
40 = Thyroid cancer pertinent cancer susceptibility (Genomics England PanelApp)
41 = Tumour predisposition - childhood onset (Genomics England PanelApp)
42 = Upper gastrointestinal cancer pertinent cancer susceptibility (Genomics England PanelApp)
--custom_list CUSTOM_LIST
Provide custom list of genes from virtual panel 0 (single-column txt file with Ensembl gene identifiers),
alternative to predefined panels provided with --panel_id)
--custom_list_name CUSTOM_LIST_NAME
Set name for custom made panel/list (single word - no whitespace), will be displayed in the report
--diagnostic_grade_only
For panel_id's 1-42 (Genomics England PanelApp) - consider genes with a GREEN status only, default: False
VEP options:
--vep_n_forks VEP_N_FORKS
Number of forks (option '--fork' in VEP), default: 4
--vep_buffer_size VEP_BUFFER_SIZE
Variant buffer size (variants read into memory simultaneously, option '--buffer_size' in VEP)
- set lower to reduce memory usage, default: 500
--vep_gencode_all Consider all GENCODE transcripts with Variant Effect Predictor (VEP) (option '--gencode_basic' in VEP is used by default).
--vep_pick_order VEP_PICK_ORDER
Comma-separated string of ordered transcript properties for primary variant pick
( option '--pick_order' in VEP), default: canonical,appris,biotype,ccds,rank,tsl,length,mane
--vep_no_intergenic Skip intergenic variants during processing (option '--no_intergenic' in VEP), default: False
vcfanno options:
--vcfanno_n_proc VCFANNO_N_PROC
Number of vcfanno processes (option '-p' in vcfanno), default: 4
Other options:
--force_overwrite By default, the script will fail with an error if any output file already exists.
You can force the overwrite of existing result files by using this flag, default: False
--version show program's version number and exit
--basic Run functional variant annotation on VCF through VEP/vcfanno, omit Tier assignment/report generation (STEP 4), default: False
--no_vcf_validate Skip validation of input VCF with Ensembl's vcf-validator, default: False
--docker_uid DOCKER_USER_ID
Docker user ID. Default is the host system user ID. If you are experiencing permission errors,
try setting this up to root (`--docker_uid root`), default: None
--no_docker Run the CPSR workflow in a non-Docker mode, default: False
--preserved_info_tags PRESERVED_INFO_TAGS
Comma-separated string of VCF INFO tags from query VCF that should be kept in CPSR output TSV
--report_theme {default,cerulean,journal,flatly,readable,spacelab,united,cosmo,lumen,paper,sandstone,simplex,yeti}
Visual report theme (rmarkdown), default: default
--report_nonfloating_toc
Do not float the table of contents (TOC) in output HTML report, default: False
--report_table_display {full,light}
Set the level of detail/comprehensiveness in interactive datables of HTML report, very comprehensive (option 'full') or slim/focused ('light')
--ignore_noncoding Do not list non-coding variants in HTML report, default: False
--secondary_findings Include variants found in ACMG-recommended list for secondary findings (v3.0), default: False
--gwas_findings Report overlap with low to moderate cancer risk variants (tag SNPs) identified from genome-wide association studies, default: False
--gwas_p_value GWAS_P_VALUE
Required p-value for variants listed as hits from genome-wide association studies, default: 5e-06
--pop_gnomad {afr,amr,eas,sas,asj,nfe,fin,global}
Population source in gnomAD used for variant frequency assessment (ACMG classification), default: nfe
--maf_upper_threshold MAF_UPPER_THRESHOLD
Upper MAF limit (gnomAD global population frequency) for variants to be included in the report, default: 0.9
--classify_all Provide CPSR variant classifications (TIER 1-5) also for variants with exising ClinVar classifications in output TSV, default: False
--clinvar_ignore_noncancer
Ignore (exclude from report) ClinVar-classified variants reported only for phenotypes/conditions NOT related to cancer, default: False
--debug Print full docker commands to log, default: FalseExample run
The cpsr software bundle contains an example VCF file.
Report generation with the example VCF, using the Adult solid tumours cancer susceptibility as the virtual gene panel, can be performed through the following command:
$ (base) conda activate pcgr
$ (pcgr)
cpsr \
--input_vcf ~/cpsr-0.7.4/example.vcf.gz \
--pcgr_dir ~/pcgr_db \
--output_dir ~/cpsr-0.7.4 \
--genome_assembly grch37 \
--panel_id 1 \
--sample_id example \
--secondary_findings \
--classify_all \
--no_docker \
--maf_upper_threshold 0.2 \
--force_overwriteNote that the example command also refers to the PCGR data bundle directory (pcgr_db), which contains the data bundle that are necessary for both PCGR and CPSR.
This command will produce the following output files in the output folder:
- example.cpsr.grch37.vcf.gz (.tbi) - Bgzipped VCF file with relevant annotations appended by CPSR
- example.cpsr.grch37.pass.vcf.gz (.tbi) - Bgzipped VCF file with relevant annotations appended by CPSR (PASS variants only)
- example.cpsr_config.rds - CPSR configuration object (RDS format), mostly for debugging purposes
- example.cpsr.grch37.pass.tsv.gz - Compressed TSV file (generated with vcf2tsv) of VCF content with relevant annotations appended by CPSR
- example.cpsr.grch37.html - Interactive HTML report with clinically relevant variants in cancer predisposition genes organized into tiers
- example.cpsr.grch37.json.gz - Compressed JSON dump of HTML report content
- example.cpsr.grch37.snvs_indels.tiers.tsv - TSV file with key annotations of tier-structured SNVs/InDels