The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package for functional annotation and translation of individual cancer genomes for precision cancer medicine. Currently, it interprets both somatic SNVs/InDels and copy number aberrations. The software extends basic gene and variant annotations from the Ensembl’s Variant Effect Predictor (VEP) with oncology-relevant, up-to-date annotations retrieved flexibly through vcfanno, and produces interactive HTML reports intended for clinical interpretation. NOTE: If you want to generate a personal report with respect to cancer predisposition for germline variants, try the accompanying tool Cancer Predisposition Sequencing Reporter (CPSR).
A few screenshots of the dashboard-type HTML output (new in 0.9.0) is shown below.
- June 30th 2021: 0.9.2 release
- Data bundle updates (CIViC, ClinVar, CancerMine, UniProt KB, PFAM)
- Software upgrades: VEP (104), R v4.1/BioConductor 3.13
- NEW: TOML configuration removed - all options to PCGR are now command-line based
- NEW: Feed PCGR with a CPSR report to view key germline findings in the tumor report
- CHANGELOG
- Planned for next release: Support for analysis of RNA fusions
- November 30th 2020: 0.9.1 release
- Data bundle updates (CIViC, ClinVar, CancerMine, UniProt KB)
- CHANGELOG
IMPORTANT: If you use PCGR, please cite the publication:
Sigve Nakken, Ghislain Fournous, Daniel Vodák, Lars Birger Aaasheim, Ola Myklebost, and Eivind Hovig. Personal Cancer Genome Reporter: variant interpretation report for precision oncology (2017). Bioinformatics. 34(10):1778–1780. doi:10.1093/bioinformatics/btx817
- VEP - Variant Effect Predictor v104 (GENCODE v38/v19 as the gene reference dataset)
- CIViC - Clinical interpretations of variants in cancer (June 15th 2021)
- ClinVar - Database of variants with clinical significance (June 2021)
- DoCM - Database of curated mutations (v3.2, Apr 2016)
- CGI - Cancer Biomarkers database (Jan 17th 2018)
- Cancer Hotspots - Resource for statistically significant mutations in cancer (v2 - 2017)
- dBNSFP - Database of non-synonymous functional predictions (v4.2, March 2021)
- TCGA - somatic mutations discovered across 33 tumor type cohorts (The Cancer Genome Atlas (TCGA), release 29, March 2021)
- CHASMplus - predicted driver mutations across 33 tumor type cohorts in TCGA
- UniProt/SwissProt KnowledgeBase - Resource on protein sequence and functional information (2021_03, June 2021)
- Pfam - Database of protein families and domains (v34, March 2021)
- Open Targets Platform - Target-disease and target-drug associations (2021_04, April 2021)
- ChEMBL - Manually curated database of bioactive molecules (v28, February 2021)
- CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (v36, June 2021)
An installation of Python (version 3.6) is required to run PCGR. Check that Python is installed by typing python --version in your terminal window.
IMPORTANT NOTE: STEP 1 & 2 below outline installation guidelines for running PCGR with Docker. If you want to install and run PCGR without the use of Docker (i.e. through Conda), follow these instructions
- Install the Docker engine on your preferred platform
- installing Docker on Linux
- installing Docker on Mac OS
- NOTE: We have not yet been able to perform enough testing on the Windows platform, and we have received feedback that particular versions of Docker/Windows do not work with PCGR (an example being mounting of data volumes)
- Test that Docker is running, e.g. by typing
docker psordocker imagesin the terminal window - Adjust the computing resources dedicated to the Docker, i.e.:
- Memory: minimum 5GB
- CPUs: minimum 4
- How to - Mac OS X
a. Download and unpack the latest software release (0.9.2)
b. Download and unpack the assembly-specific data bundle in the PCGR directory
-
grch37 data bundle - 20210627 (approx 20Gb)
-
grch38 data bundle - 20210627 (approx 21Gb)
- Unpacking:
gzip -dc pcgr.databundle.grch37.YYYYMMDD.tgz | tar xvf -
A data/ folder within the pcgr-X.X software folder should now have been produced
- Unpacking:
c. Pull the PCGR Docker image (0.9.2) from DockerHub (approx 5.0Gb):
docker pull sigven/pcgr:0.9.2(PCGR annotation engine)
a. Clone the PCGR GitHub repository (includes run script and default configuration file): git clone https://github.com/sigven/pcgr.git
b. Download and unpack the latest data bundles in the PCGR directory
- grch37 data bundle - 20210627 (approx 20Gb)
- grch38 data bundle - 20210627 (approx 21Gb)
- If our local server cannot be reached or is non-responding, try grch37 - Google Drive / grch38 - Google Drive
- Unpacking:
gzip -dc pcgr.databundle.grch37.YYYYMMDD.tgz | tar xvf -
c. Pull the PCGR Docker image (dev) from DockerHub (approx 5.0Gb):
docker pull sigven/pcgr:dev(PCGR annotation engine)
The PCGR workflow accepts two types of input files:
-
An unannotated, single-sample VCF file (>= v4.2) with called somatic variants (SNVs/InDels) - required
-
A copy number segment file - optional
-
We strongly recommend that the input VCF is compressed and indexed using bgzip and tabix
-
If the input VCF contains multi-allelic sites, these will be subject to decomposition. Optimally, try to decompose multi-allelic variants in the input VCF prior to analysis.
-
Variants used for reporting should be designated as 'PASS' in the VCF FILTER column
The tab-separated values file with copy number aberrations MUST contain the following four columns:
- Chromosome
- Start
- End
- Segment_Mean
Here, Chromosome, Start, and End denote the chromosomal segment, and Segment_Mean denotes the log(2) ratio for a particular segment, which is a common output of somatic copy number alteration callers. Note that coordinates must be one-based (i.e. chromosomes start at 1, not 0). Below shows the initial part of a copy number segment file that is formatted correctly according to PCGR's requirements:
Chromosome Start End Segment_Mean
1 3218329 3550598 0.0024
1 3552451 4593614 0.1995
1 4593663 6433129 -1.0277
The PCGR software comes with a range of options to configure the report and analysis, depending on the type of sequencing assay (tumor-control vs. tumor_only), types of data available (SNVs/InDels, copy number aberration) etc.
- IMPORTANT: Proper designation of VCF INFO tags that denote variant depth/allelic fraction is critical to make the report as comprehensive as possible (options
--tumor_dp_tag,tumor_af_tag,--control_dp_tag,--control_af_tag, and--call_conf_tag)- If this is not available/properly set, the report contents will be less informative AND it will not be possible to preset thresholds for variant depth/allelic fraction
Furthermore, to make the report as complete as possible, take a note of the following arguments which can be used in the report generation:
- Tumor purity estimate (
--tumor_ploidy, for display only) - Tumor ploidy estimate (
--tumor_purity, for display only) - Type of sequencing assay (
--assay) - Cell line sample (
--cell_line, for display only) - Coding target size of sequencing assay (
--target_size_mb)
A tumor sample report is generated by calling the Python script pcgr.py, which takes the following arguments and options:
usage:
pcgr.py -h [options]
--input_vcf <INPUT_VCF>
--pcgr_dir <PCGR_DIR>
--output_dir <OUTPUT_DIR>
--genome_assembly <GENOME_ASSEMBLY>
--sample_id <SAMPLE_ID>
Personal Cancer Genome Reporter (PCGR) workflow for clinical interpretation of somatic nucleotide variants and copy number aberration segments
Required arguments:
--input_vcf INPUT_VCF
VCF input file with somatic variants in tumor sample, SNVs/InDels
--pcgr_dir PCGR_DIR PCGR base directory with accompanying data directory, e.g. ~/pcgr-0.9.2
--output_dir OUTPUT_DIR
Output directory
--genome_assembly {grch37,grch38}
Human genome assembly build: grch37 or grch38
--sample_id SAMPLE_ID
Tumor sample/cancer genome identifier - prefix for output files
vcfanno options:
--vcfanno_n_proc VCFANNO_N_PROC
Number of vcfanno processes (option '-p' in vcfanno), default: 4
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: 100
--vep_pick_order VEP_PICK_ORDER
Comma-separated string of ordered transcript/variant properties for selection of primary variant consequence
( 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
--vep_regulatory Add VEP regulatory annotations (option '--regulatory' )or non-coding interpretation, default: False
Tumor mutational burden (TMB) and MSI options:
--target_size_mb TARGET_SIZE_MB
For mutational burden analysis - approximate protein-coding target size in Mb of sequencing assay (default: 34 (WES/WGS))
--estimate_tmb Estimate tumor mutational burden from the total number of somatic mutations and target region size, default: False
--estimate_msi_status
Predict microsatellite instability status from patterns of somatic mutations/indels, default: False
--tmb_algorithm {all_coding,nonsyn}
Method for calculation of TMB, all coding variants (Chalmers et al., Genome Medicine, 2017), or non-synonymous variants only, default: all_coding
Mutatonal signature options:
--estimate_signatures
Estimate relative contributions of reference mutational signatures in query sample and detect potential kataegis events, default: False
--min_mutations_signatures MIN_MUTATIONS_SIGNATURES
Minimum number of SNVs required for reconstruction of mutational signatures (SBS) by MutationalPatterns (default: 200, minimum n = 100)
--all_reference_signatures
Use all reference mutational signatures (SBS, n = 67) in signature reconstruction rather than only those already attributed to the tumor type (default: False)
--include_artefact_signatures
Include sequencing artefacts in the collection of reference signatures (default: False
Tumor-only options:
--tumor_only Input VCF comes from tumor-only sequencing, calls will be filtered for variants of germline origin, (default: False)
--cell_line Input VCF comes from tumor cell line sequencing (requires --tumor_only), calls will be filtered for variants of germline origin, (default: False)
--pon_vcf PON_VCF VCF file with germline calls from Panel of Normals (PON) - i.e. blacklisted variants, (default: None)
--maf_onekg_eur MAF_ONEKG_EUR
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold (1000 Genomes Project - European pop, default: 0.002)
--maf_onekg_amr MAF_ONEKG_AMR
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold (1000 Genomes Project - Ad Mixed American pop, default: 0.002)
--maf_onekg_afr MAF_ONEKG_AFR
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold (1000 Genomes Project - African pop, default: 0.002)
--maf_onekg_eas MAF_ONEKG_EAS
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold (1000 Genomes Project - East Asian pop, default: 0.002)
--maf_onekg_sas MAF_ONEKG_SAS
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold (1000 Genomes Project - South Asian pop, default: 0.002)
--maf_onekg_global MAF_ONEKG_GLOBAL
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold (1000 Genomes Project - global pop, default: 0.002)
--maf_gnomad_nfe MAF_GNOMAD_NFE
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - European (non-Finnish), default: 0.002)
--maf_gnomad_asj MAF_GNOMAD_ASJ
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - Ashkenazi Jewish, default: 0.002)
--maf_gnomad_fin MAF_GNOMAD_FIN
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - European (Finnish), default: 0.002)
--maf_gnomad_oth MAF_GNOMAD_OTH
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - Other, default: 0.002)
--maf_gnomad_amr MAF_GNOMAD_AMR
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - Latino/Admixed American, default: 0.002)
--maf_gnomad_afr MAF_GNOMAD_AFR
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - African/African-American, default: 0.002)
--maf_gnomad_eas MAF_GNOMAD_EAS
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - East Asian, default: 0.002)
--maf_gnomad_sas MAF_GNOMAD_SAS
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - South Asian, default: 0.002)
--maf_gnomad_global MAF_GNOMAD_GLOBAL
Exclude variants in tumor (SNVs/InDels, tumor-only mode) with MAF above the given percent threshold, (gnomAD - global population, default: 0.002)
--exclude_pon Exclude variants occurring in PoN (Panel of Normals, if provided as VCF (--pon_vcf), default: False)
--exclude_likely_hom_germline
Exclude likely homozygous germline variants (100 pct allelic fraction for alternate allele in tumor, very unlikely somatic event, default: False)
--exclude_likely_het_germline
Exclude likely heterozygous germline variants (40-60 pct allelic fraction, AND presence in dbSNP + gnomAD, AND not existing as somatic event in COSMIC/TCGA, default: False)
--exclude_dbsnp_nonsomatic
Exclude variants found in dbSNP (only those that are NOT found in ClinVar(somatic origin)/DoCM/TCGA/COSMIC, defult: False)
--exclude_nonexonic Exclude non-exonic variants, defult: False)
Allelic support options:
--tumor_dp_tag TUMOR_DP_TAG
Specify VCF INFO tag for sequencing depth (tumor, must be Type=Integer, default: _NA_
--tumor_af_tag TUMOR_AF_TAG
Specify VCF INFO tag for variant allelic fraction (tumor, must be Type=Float, default: _NA_
--control_dp_tag CONTROL_DP_TAG
Specify VCF INFO tag for sequencing depth (control, must be Type=Integer, default: _NA_
--control_af_tag CONTROL_AF_TAG
Specify VCF INFO tag for variant allelic fraction (control, must be Type=Float, default: _NA_
--call_conf_tag CALL_CONF_TAG
Specify VCF INFO tag for somatic variant call confidence (must be categorical, e.g. Type=String, default: _NA_
--tumor_dp_min TUMOR_DP_MIN
If VCF INFO tag for sequencing depth (tumor) is specified and found, set minimum required depth for inclusion in report (default: 0)
--tumor_af_min TUMOR_AF_MIN
If VCF INFO tag for variant allelic fraction (tumor) is specified and found, set minimum required AF for inclusion in report (default: 0)
--control_dp_min CONTROL_DP_MIN
If VCF INFO tag for sequencing depth (control) is specified and found, set minimum required depth for inclusion in report (default: 0)
--control_af_max CONTROL_AF_MAX
If VCF INFO tag for variant allelic fraction (control) is specified and found, set maximum tolerated AF for inclusion in report (default: 1)
Other options:
--input_cna INPUT_CNA
Somatic copy number alteration segments (tab-separated values)
--logr_gain LOGR_GAIN
Log ratio-threshold for regions containing copy number gains/amplifications (default: 0.8)
--logr_homdel LOGR_HOMDEL
Log ratio-threshold for regions containing homozygous deletions (default: -0.8)
--cna_overlap_pct CNA_OVERLAP_PCT
Mean percent overlap between copy number segment and gene transcripts for reporting of gains/losses in tumor suppressor genes/oncogenes, (default: 50)
--tumor_site TSITE Optional integer code to specify primary tumor type/site of query sample,
choose any of the following identifiers:
1 = Adrenal Gland
2 = Ampulla of Vater
3 = Biliary Tract
4 = Bladder/Urinary Tract
5 = Bone
6 = Breast
7 = Cervix
8 = CNS/Brain
9 = Colon/Rectum
10 = Esophagus/Stomach
11 = Eye
12 = Head and Neck
13 = Kidney
14 = Liver
15 = Lung
16 = Lymphoid
17 = Myeloid
18 = Ovary/Fallopian Tube
19 = Pancreas
20 = Peripheral Nervous System
21 = Peritoneum
22 = Pleura
23 = Prostate
24 = Skin
25 = Soft Tissue
26 = Testis
27 = Thymus
28 = Thyroid
29 = Uterus
30 = Vulva/Vagina
(default: 0 - any tumor type)
--tumor_purity TUMOR_PURITY
Estimated tumor purity (between 0 and 1, (default: None)
--tumor_ploidy TUMOR_PLOIDY
Estimated tumor ploidy (default: None)
--cpsr_report CPSR_REPORT
CPSR report file (Gzipped JSON - file ending with 'cpsr.<genome_assembly>.json.gz' - germline report of patient's blood/control sample
--vcf2maf Generate a MAF file for input VCF using https://github.com/mskcc/vcf2maf (default: False)
--show_noncoding List non-coding (i.e. non protein-altering) variants in report, default: False
--assay {WES,WGS,TARGETED}
Type of DNA sequencing assay performed for input data (VCF) default: WES
--include_trials (Beta) Include relevant ongoing or future clinical trials, focusing on studies with molecularly targeted interventions
--preserved_info_tags PRESERVED_INFO_TAGS
Comma-separated string of VCF INFO tags from query VCF that should be kept in PCGR output TSV file
--report_theme {default,cerulean,journal,flatly,readable,spacelab,united,cosmo,lumen,paper,sandstone,simplex,yeti}
Visual report theme (rmarkdown)
--report_nonfloating_toc
Do not float the table of contents (TOC) in output report (rmarkdown), default: False
--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 other analyses (i.e. Tier assignment/MSI/TMB/Signatures etc. and 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`)
--no_docker Run the PCGR workflow in a non-Docker mode (see install_no_docker/ folder for instructions)
--debug Print full Docker commands to log, default: False
The examples folder contain input VCF files from two tumor samples sequenced within TCGA (GRCh37 only). It also contains a PCGR configuration file customized for these VCFs. A report for a colorectal tumor case can be generated by running the following command in your terminal window:
python ~/pcgr-0.9.2/pcgr.py
--pcgr_dir ~/pcgr-0.9.2
--output_dir ~/pcgr-0.9.2
--sample_id tumor_sample.COAD
--tumor_dp_tag TDP
--tumor_af_tag TVAF
--call_conf_tag TAL
--genome_assembly grch37
--input_vcf ~/pcgr-0.9.2/examples/tumor_sample.COAD.vcf.gz
--tumor_site 9
--input_cna ~/pcgr-0.9.2/examples/tumor_sample.COAD.cna.tsv
--tumor_purity 0.9
--tumor_ploidy 2.0
--include_trials
--assay WES
--estimate_signatures
--estimate_msi_status
--estimate_tmb
--no_vcf_validate
This command will run the Docker-based PCGR workflow and produce the following output files in the examples folder:
- tumor_sample.COAD.pcgr_acmg.grch37.html - An interactive HTML report for clinical interpretation (rmarkdown)
- tumor_sample.COAD.pcgr_acmg.grch37.flexdb.html - An interactive HTML report for clinical interpretation (flexdashboard)
- tumor_sample.COAD.pcgr_acmg.grch37.vcf.gz (.tbi) - Bgzipped VCF file with rich set of annotations for precision oncology
- tumor_sample.COAD.pcgr_acmg.grch37.pass.vcf.gz (.tbi) - Bgzipped VCF file with rich set of annotations for precision oncology (PASS variants only)
- tumor_sample.COAD.pcgr_acmg.grch37.pass.tsv.gz - Compressed vcf2tsv-converted file with rich set of annotations for precision oncology
- tumor_sample.COAD.pcgr_acmg.grch37.snvs_indels.tiers.tsv - Tab-separated values file with variants organized according to tiers of functional relevance
- tumor_sample.COAD.pcgr_acmg.grch37.mutational_signatures.tsv - Tab-separated values file with information on contribution of mutational signatures
- tumor_sample.COAD.pcgr_acmg.grch37.json.gz - Compressed JSON dump of HTML report content
- tumor_sample.COAD.pcgr_acmg.grch37.cna_segments.tsv.gz - Compressed tab-separated values file with annotations of gene transcripts that overlap with somatic copy number aberrations
Frequently asked questions regarding PCGR usage and functionality:
1. Why do I get a long list of lines with “ERROR: Line ..” during “STEP 0: validate input data”?
Answer: Your query VCF does not pass the VCF validation check by EBI's vcf-validator.
Solution: 1) Fix the VCF so that it adheres to the VCF standard, or 2) run PCGR with option --vcf_no_validate if you think the formatting problems is not critical to the contents of the VCF.
2. I am not sure how to specify depth/allelic fraction in my query VCF. Why cannot PCGR pull out this information automatically from my VCF file?
Answer: This is something that you as a user need to handle yourself. To our knowledge, there is currently no standard way that variant callers format these types of data (allelic fraction/depth, tumor/normal) in the VCF, and this makes it very challenging for PCGR to automatically grab this information from the variety of VCFs produced by different variant callers. Please take a careful look at the example VCF files (examples folder) that comes with PCGR for how PCGR expects this information to be formatted, and make sure your VCF is formatted accordingly.
3. Is it possible to utilize PCGR for analysis of multiple samples?
Answer: As the name of the tool implies, PCGR was developed for the detailed analysis of individual tumor samples. However, if you take advantage of the different outputs from PCGR, it can also be utilized for analysis of multiple samples. First, make sure your input files are organized per sample (i.e. one VCF file per sample, one CNA file per sample), so that they can be fed directly to PCGR. Now, once all samples have been processed with PCGR, note that all the tab-separated output files (i.e. tiers, mutational signatures, cna segments) contain the sample identifier, which enable them to be aggregated and suitable for a downstream multi-sample analysis.
Also note that the compressed JSON output pr. sample run contains ALL information presented in the report. Explore the JSON contents e.g. with the jsonlite package in R:
report_data <- jsonlite::fromJSON('<sample_id>.pcgr_acmg.grch37.json.gz')
E.g. tiered SNV/InDel output:
head(report_data$content$snv_indel$variant_set$tsv)
Or TMB estimate:
report_data$content$tmb$variant_statistic$tmb_estimate
4. I do not see the expected transcript-specific consequence for a particular variant. In what way is the primary variant consequence established?
Answer: PCGR relies upon VEP for consequence prioritization, in which a specific transcript-specific consequence is chosen as the primary variant consequence. In the PCGR configuration file, you may customise how this is chosen by changing the order of criteria applied when choosing a primary consequence block - parameter vep_pick_order
5. Is it possible to use RefSeq as the underlying gene transcript model in PCGR?
Answer: PCGR uses GENCODE as the primary gene transcript model, but we provide cross-references to corresponding RefSeq transcripts when this is available.
6. I have a VCF with structural variants detected in my tumor sample, can PCGR process those as well?
Answer: This is currently not supported as input for PCGR, but is something we want to incorporate in the future.
7. I am surprised to see a particular gene being located in TIER 3 for my sample, since I know that this gene is of potential clinical significance in the tumor type I am investigating?
Answer: PCGR classifies variants into tiers of significance through an implementation of published guidelines by ACMG/AMP. No manual efforts for individual tumor types are conducted beyond this rule-based scheme. The users need to keep this in mind when interpreting the tier contents of the report.
8. Is it possible to see all the invididual cancer subtypes that belong to each of the 30 different tumor sites?
Answer: Yes, see oncotree_ontology_xref.tsv
9. Is there any plans to incorporate data from OncoKB in PCGR?
Answer: No. PCGR relies upon publicly available open-source resources, and further that the PCGR data bundle can be distributed freely to the user community. It is our understanding that OncoKB's terms of use do not fit well with this strategy.
10. Is it possible for the users to update the data bundle to get the most recent versions of all underlying data sources?
Answer: As of now, the data bundle is updated only with each release of PCGR. The data harmonization pipeline of knowledge databases in PCGR contain numerous and complex procedures, with several quality control and re-formatting steps, and and cannot be fully automated in its present form. The version of all databases and key software elements are outlined in each PCGR report.
sigven AT ifi.uio.no