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‎README.md‎

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@@ -15,7 +15,7 @@ The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package int
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[![Documentation Status](https://readthedocs.org/projects/pcgr/badge/?version=latest)](http://pcgr.readthedocs.io/en/latest/?badge=latest)
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### Annotation resources included in PCGR (v0.2)
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### Annotation resources included in PCGR (v0.3)
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* [VEP v85](http://www.ensembl.org/info/docs/tools/vep/index.html) - Variant Effect Predictor release 85 (GENCODE v19 as the gene reference dataset)
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* [COSMIC v80](http://cancer.sanger.ac.uk/cosmic/) - Catalogue of somatic mutations in cancer (February 2017)
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#### STEP 2: Download PCGR
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1. Download and unpack the [latest release](https://github.com/sigven/pcgr/releases/latest)
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<font color="red"><b>April 12th 2017</b>: New release (v0.3)</font>
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1. Download and unpack the [latest release (v0.3)](https://github.com/sigven/pcgr/releases/latest)
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2. Download and unpack the data bundle (approx. 17Gb) in the PCGR directory
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* Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number)
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* Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g `~/pcgr-0.3`)
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* Unpack the data bundle, e.g. through the following Unix command: `gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -`
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A _data/_ folder within the _pcgr-X.X_ software folder should now have been produced
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3. Pull the [PCGR Docker image](https://hub.docker.com/r/sigven/pcgr/) from DockerHub:
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* `docker pull sigven/pcgr` (PCGR annotation engine)
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3. Pull the [PCGR Docker image (v0.3)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb):
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* `docker pull sigven/pcgr:0.3` (PCGR annotation engine)
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#### STEP 3: Input preprocessing
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#### STEP 4: Run example
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A tumor sample report is generated by calling the Python script __run_pcgr.py__ in the PCGR software folder, which takes the following arguments and options:
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usage: run_pcgr.py [-h] [--input_vcf INPUT_VCF]
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[--input_cna_segments INPUT_CNA_SEGMENTS]
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[--logR_threshold_amplification LOGR_THRESHOLD_AMPLIFICATION]
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[--logR_threshold_homozygous_deletion LOGR_THRESHOLD_HOMOZYGOUS_DELETION]
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[--num_vcfanno_processes NUM_VCFANNO_PROCESSES]
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[--num_vep_forks NUM_VEP_FORKS] [--force_overwrite]
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pcgr_directory working_directory sample_id
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Personal Cancer Genome Reporter (PCGR) workflow for clinical interpretation of
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somatic nucleotide variants and copy number aberration segments
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positional arguments:
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pcgr_directory PCGR base directory
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working_directory Working directory - directory with input/output files
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sample_id Tumor sample/cancer genome identifier - prefix for
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output files
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optional arguments:
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-h, --help show this help message and exit
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--input_vcf INPUT_VCF
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VCF input file with somatic query variants
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(SNVs/InDels) (default: None)
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--input_cna_segments INPUT_CNA_SEGMENTS
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Somatic copy number alteration segments (tab-separated
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values) (default: None)
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--logR_threshold_amplification LOGR_THRESHOLD_AMPLIFICATION
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Log(2) ratio treshold for calling copy number
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amplifications in HTML report (default: 0.8)
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--logR_threshold_homozygous_deletion LOGR_THRESHOLD_HOMOZYGOUS_DELETION
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Log(2) ratio treshold for calling homozygous deletions
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in HTML report (default: -0.8)
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--num_vcfanno_processes NUM_VCFANNO_PROCESSES
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Number of processes used during vcfanno annotation
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(default: 4)
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--num_vep_forks NUM_VEP_FORKS
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Number of forks (--forks option in VEP) used during
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VEP annotation (default: 4)
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--force_overwrite By default, the script will fail with an error if any
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output file already exists. You can force the
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overwrite of existing result files by using this flag
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(default: False)
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A tumor sample report is generated by calling the Python script __pcgr.py__ in the PCGR software folder, which takes the following arguments and options:
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usage: pcgr.py [-h] [--input_vcf INPUT_VCF]
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[--input_cna_segments INPUT_CNA_SEGMENTS]
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[--logR_threshold_amplification LOGR_THRESHOLD_AMPLIFICATION]
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[--logR_threshold_homozygous_deletion LOGR_THRESHOLD_HOMOZYGOUS_DELETION]
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[--num_vcfanno_processes NUM_VCFANNO_PROCESSES]
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[--num_vep_forks NUM_VEP_FORKS] [--force_overwrite]
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[--version]
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pcgr_dir output_dir sample_id
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Personal Cancer Genome Reporter (PCGR) workflow for clinical interpretation of
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somatic nucleotide variants and copy number aberration segments
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positional arguments:
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pcgr_dir PCGR base directory with accompanying data directory,
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e.g. ~/pcgr-0.3
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output_dir Output directory
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sample_id Tumor sample/cancer genome identifier - prefix for
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output files
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optional arguments:
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-h, --help show this help message and exit
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--input_vcf INPUT_VCF
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VCF input file with somatic query variants
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(SNVs/InDels) (default: None)
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--input_cna_segments INPUT_CNA_SEGMENTS
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Somatic copy number alteration segments (tab-separated
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values) (default: None)
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--logR_threshold_amplification LOGR_THRESHOLD_AMPLIFICATION
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Log(2) ratio treshold for calling copy number
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amplifications in HTML report (default: 0.8)
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--logR_threshold_homozygous_deletion LOGR_THRESHOLD_HOMOZYGOUS_DELETION
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Log(2) ratio treshold for calling homozygous deletions
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in HTML report (default: -0.8)
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--num_vcfanno_processes NUM_VCFANNO_PROCESSES
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Number of processes used during vcfanno annotation
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(default: 4)
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--num_vep_forks NUM_VEP_FORKS
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Number of forks (--forks option in VEP) used during
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VEP annotation (default: 4)
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--force_overwrite By default, the script will fail with an error if any
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output file already exists. You can force the
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overwrite of existing result files by using this flag
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(default: False)
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--version show program's version number and exit
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The _examples_ folder contain sample files from TCGA. A report for a colorectal tumor case can be generated through the following command:
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`python run_pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-X.X ~/pcgr-X.X/examples tumor_sample.COAD`
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`python pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD`
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This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder:
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‎docs/_build/doctrees/annotation_resources.doctree‎

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‎docs/_build/doctrees/environment.pickle‎

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‎docs/_build/html/_sources/annotation_resources.rst.txt‎

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Other data quality concerns
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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**Clinical biomarkers** Clinical biomarkers included in PCGR is limited
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to the following: \* Markers reported at the variant level (e.g. **BRAF
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p.V600E**) \* Markers reported at the codon level (e.g. **KRAS p.G12**)
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\* Markers reported at the exon level (e.g. **KIT exon 11 mutation**) \*
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Within CBMDB, only markers collected from FDA/NCCN guidelines,
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scientific literature and clinical trials are included (markers
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collected from conference abstracts are not included)
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**COSMIC variants** The COSMIC dataset that is part of the PCGR
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annotation bundle is the subset of variants that satisfy the following
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criteria: \* **Mutation somatic status** is either
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'*confirmed\_somatic*' or
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'*reported\_in\_another\_cancer\_sample\_as\_somatic*'. \*
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**Site/histology** must be known and the sample must come from a
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malignant tumor (i.e. not polyps/adenomas, which are also found in
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COSMIC)
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**Clinical biomarkers**
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Clinical biomarkers included in PCGR is limited to the following:
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- Markers reported at the variant level (e.g. **BRAF p.V600E**)
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- Markers reported at the codon level (e.g. **KRAS p.G12**)
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- Markers reported at the exon level (e.g. **KIT exon 11 mutation**)
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- Within CBMDB, only markers collected from FDA/NCCN guidelines,
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scientific literature and clinical trials are included (markers
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collected from conference abstracts are not included)
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**COSMIC variants**
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The COSMIC dataset that is part of the PCGR annotation bundle is the
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subset of variants that satisfy the following criteria:
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- **Mutation somatic status** is either '*confirmed\_somatic*' or
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'*reported\_in\_another\_cancer\_sample\_as\_somatic*'.
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- **Site/histology** must be known and the sample must come from a
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malignant tumor (i.e. not polyps/adenomas, which are also found in
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COSMIC)

‎docs/_build/html/_sources/getting_started.rst.txt‎

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Download PCGR
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^^^^^^^^^^^^^
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- Download and unpack the `latest
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release <https://github.com/sigven/pcgr/releases/latest>`__
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April 12th 2017: New release (v0.3) \* Download and unpack the `latest
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release (v0.3) <https://github.com/sigven/pcgr/releases/latest>`__
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- Download and unpack the data bundle (approx. 17Gb) in the PCGR
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directory
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- Download `the latest data
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bundle <https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/>`__
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from Google Drive to ``~/pcgr-X.X`` (replace *X.X* with the
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version number)
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version number, e.g. ``~/pcgr-0.3``)
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- Decompress and untar the bundle, e.g. through the following Unix
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command:
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``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -``
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A *data/* folder within the *pcgr-X.X* software folder should now
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have been produced
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- Pull the `PCGR Docker
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image <https://hub.docker.com/r/sigven/pcgr/>`__ (3.5Gb) from
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DockerHub):
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- Pull the `PCGR Docker image -
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v0.3 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
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(3.1Gb) :
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- ``docker pull sigven/pcgr`` (PCGR annotation engine)
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- ``docker pull sigven/pcgr:0.3`` (PCGR annotation engine)
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Run test - generation of clinical report for a cancer genome
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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A tumor sample report is generated by calling the Python script
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**run\_pcgr.py**, which takes the following arguments and options:
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**pcgr.py**, which takes the following arguments and options:
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::
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usage: run_pcgr.py [-h] [--input_vcf INPUT_VCF]
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usage: pcgr.py [-h] [--input_vcf INPUT_VCF]
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[--input_cna_segments INPUT_CNA_SEGMENTS]
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[--logR_threshold_amplification LOGR_THRESHOLD_AMPLIFICATION]
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[--logR_threshold_homozygous_deletion LOGR_THRESHOLD_HOMOZYGOUS_DELETION]
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[--num_vcfanno_processes NUM_VCFANNO_PROCESSES]
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[--num_vep_forks NUM_VEP_FORKS] [--force_overwrite]
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pcgr_directory working_directory sample_id
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[--version]
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pcgr_dir output_dir sample_id
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Personal Cancer Genome Reporter (PCGR) workflow for clinical interpretation of
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somatic nucleotide variants and copy number aberration segments
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positional arguments:
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pcgr_directory PCGR base directory
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working_directory Working directory - directory with input/output files
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pcgr_dir PCGR base directory with accompanying data directory,
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e.g. ~/pcgr-0.3
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output_dir Output directory
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sample_id Tumor sample/cancer genome identifier - prefix for
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output files
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output file already exists. You can force the
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overwrite of existing result files by using this flag
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(default: False)
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--version show program's version number and exit
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The *examples* folder contain input files from two tumor samples
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sequenced within TCGA. A report for a colorectal tumor case can be
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generated by running the following command in your terminal window:
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``python run_pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments``
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``tumor_sample.COAD.cna.tsv ~/pcgr-X.X ~/pcgr-X.X/examples tumor_sample.COAD``
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``python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments``
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``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD``
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This command will run the Docker-based PCGR workflow and produce the
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following output files in the *examples* folder:

‎docs/_build/html/annotation_resources.html‎

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</div>
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<div class="section" id="other-data-quality-concerns">
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<h2>Other data quality concerns<a class="headerlink" href="#other-data-quality-concerns" title="Permalink to this headline"></a></h2>
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<p><strong>Clinical biomarkers</strong> Clinical biomarkers included in PCGR is limited
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to the following: * Markers reported at the variant level (e.g. <strong>BRAF
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p.V600E</strong>) * Markers reported at the codon level (e.g. <strong>KRAS p.G12</strong>)
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* Markers reported at the exon level (e.g. <strong>KIT exon 11 mutation</strong>) *
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Within CBMDB, only markers collected from FDA/NCCN guidelines,
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<p><strong>Clinical biomarkers</strong></p>
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<p>Clinical biomarkers included in PCGR is limited to the following:</p>
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<ul class="simple">
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<li>Markers reported at the variant level (e.g. <strong>BRAF p.V600E</strong>)</li>
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<li>Markers reported at the codon level (e.g. <strong>KRAS p.G12</strong>)</li>
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<li>Markers reported at the exon level (e.g. <strong>KIT exon 11 mutation</strong>)</li>
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<li>Within CBMDB, only markers collected from FDA/NCCN guidelines,
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scientific literature and clinical trials are included (markers
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collected from conference abstracts are not included)</p>
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<p><strong>COSMIC variants</strong> The COSMIC dataset that is part of the PCGR
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annotation bundle is the subset of variants that satisfy the following
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criteria: * <strong>Mutation somatic status</strong> is either
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&#8216;<em>confirmed_somatic</em>&#8216; or
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&#8216;<em>reported_in_another_cancer_sample_as_somatic</em>&#8216;. *
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<strong>Site/histology</strong> must be known and the sample must come from a
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collected from conference abstracts are not included)</li>
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</ul>
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<p><strong>COSMIC variants</strong></p>
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<p>The COSMIC dataset that is part of the PCGR annotation bundle is the
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subset of variants that satisfy the following criteria:</p>
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<ul class="simple">
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<li><strong>Mutation somatic status</strong> is either &#8216;<em>confirmed_somatic</em>&#8216; or
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&#8216;<em>reported_in_another_cancer_sample_as_somatic</em>&#8216;.</li>
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<li><strong>Site/histology</strong> must be known and the sample must come from a
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malignant tumor (i.e. not polyps/adenomas, which are also found in
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COSMIC)</p>
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COSMIC)</li>
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</ul>
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</div>
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</div>
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