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automatic decomposition of multiallelics
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| ### Overview | ||
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| The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package intended for analysis and clinical interpretation of individual cancer genomes. 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)](http://www.ensembl.org/info/docs/tools/vep/index.html) with oncology-relevant, up-to-date annotations retrieved flexibly through [vcfanno](https://github.com/brentp/vcfanno), and produces HTML reports that can be navigated by clinical oncologists (Figure 1). | ||
| The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package for functional annotation and translation of individual cancer genomes for precision oncology. 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)](http://www.ensembl.org/info/docs/tools/vep/index.html) with oncology-relevant, up-to-date annotations retrieved flexibly through [vcfanno](https://github.com/brentp/vcfanno), and produces interactive HTML reports intended for clinical interpretation (Figure 1). | ||
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| ### Example reports | ||
| * <a href="http://folk.uio.no/sigven/tumor_sample.COAD.pcgr.html" target="_blank">View an example report for a colorectal tumor sample (TCGA)</a> | ||
| * <a href="http://folk.uio.no/sigven/tumor_sample.BRCA.pcgr.html" target="_blank">View an example report for a breast tumor sample (TCGA)</a> | ||
| * <a href="http://folk.uio.no/sigven/tumor_sample.COAD.pcgr.html" target="_blank">Report for a colorectal tumor sample (TCGA)</a> | ||
| * <a href="http://folk.uio.no/sigven/tumor_sample.BRCA.pcgr.html" target="_blank">Report for a breast tumor sample (TCGA)</a> | ||
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| ### PCGR documentation | ||
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| [](http://pcgr.readthedocs.io/en/latest/?badge=latest) | ||
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| If you use PCGR, please cite our paper: | ||
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| ### Annotation resources included in PCGR (v0.3.2) | ||
| Sigve Nakken, Ghislain Fournous, Daniel Vodák, Lars Birger Aaasheim, and Eivind Hovig. __Personal Cancer Genome Reporter: Variant Interpretation Report For Precision Oncology__ (2017). bioRxiv. doi:[10.1101/122366](https://doi.org/10.1101/122366) | ||
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| ### Annotation resources included in PCGR (v0.3.3) | ||
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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) | ||
| * [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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| <font color="red"><b>April 19th 2017</b>: New release (0.3.2)</font> | ||
| <font color="red"><b>April 20th 2017</b>: New release (0.3.3)</font> | ||
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| 1. Download and unpack the [latest release (0.3.2)](https://github.com/sigven/pcgr/releases/latest) | ||
| 1. Download and unpack the [latest release (0.3.3)](https://github.com/sigven/pcgr/releases/latest) | ||
| 2. Download and unpack the data bundle (approx. 17Gb) in the PCGR directory | ||
| * 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-0.3.2`) | ||
| * 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-0.3.3`) | ||
| * 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 | ||
| 3. Pull the [PCGR Docker image (0.3.2)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb): | ||
| * `docker pull sigven/pcgr:0.3.2` (PCGR annotation engine) | ||
| 3. Pull the [PCGR Docker image (0.3.3)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.2Gb): | ||
| * `docker pull sigven/pcgr:0.3.3` (PCGR annotation engine) | ||
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| #### STEP 3: Input preprocessing | ||
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| PCGR can be run with either or both of the two input files present. | ||
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| The following requirements __MUST__ be met by the input VCF for PCGR to work properly: | ||
| * We __strongly__ recommend that the input VCF is compressed and indexed using [bgzip](http://www.htslib.org/doc/tabix.html) and [tabix](http://www.htslib.org/doc/tabix.html) | ||
| * If the input VCF contains multi-allelic sites, these will be subject to [decomposition](http://genome.sph.umich.edu/wiki/Vt#Decompose) | ||
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| 1. Variants in the raw VCF that contain multiple alternative alleles (e.g. "multiple ALTs") must be split into variants with a single alternative allele. This can be done with the help of either [vt decompose](http://genome.sph.umich.edu/wiki/Vt#Decompose) or [vcflib's vcfbreakmulti](https://github.com/vcflib/vcflib#vcflib). We will add integrated support for this in an upcoming release | ||
| 2. The contents of the VCF must be sorted correctly (i.e. according to chromosomal order and chromosomal position). This can be obtained by [vcftools](https://vcftools.github.io/perl_module.html#vcf-sort). | ||
| * We strongly recommend that the input VCF is compressed and indexed using [bgzip](http://www.htslib.org/doc/tabix.html) and [tabix](http://www.htslib.org/doc/tabix.html) | ||
| * 'chr' must be stripped from the chromosome names | ||
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| The tab-separated values file with copy number aberrations __MUST__ contain the following four columns: | ||
| * Chromosome | ||
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| positional arguments: | ||
| pcgr_dir PCGR base directory with accompanying data directory, | ||
| e.g. ~/pcgr-0.3.2 | ||
| e.g. ~/pcgr-0.3.3 | ||
| output_dir Output directory | ||
| sample_id Tumor sample/cancer genome identifier - prefix for | ||
| output files | ||
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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 pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-0.3.2 ~/pcgr-0.3.2/examples tumor_sample.COAD` | ||
| `python pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-0.3.3 ~/pcgr-0.3.3/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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| 5. __tumor_sample.COAD.pcgr.mutational_signatures.tsv__ - Tab-separated values file with estimated contributions by known mutational signatures and associated underlying etiologies | ||
| 6. __tumor_sample.COAD.pcgr.snvs_indels.biomarkers.tsv__ - Tab-separated values file with clinical evidence items associated with biomarkers for diagnosis, prognosis or drug sensitivity/resistance | ||
| 7. __tumor_sample.COAD.pcgr.cna_segments.tsv.gz__ - Tab-separated values file with annotations of gene transcripts that overlap with somatic copy number aberrations | ||
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| ## Contact | ||
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| [email protected] | ||
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| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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| The Personal Cancer Genome Reporter (PCGR) is a stand-alone software | ||
| package intended for analysis and clinical interpretation of individual | ||
| cancer genomes. 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 | ||
| package for functional annotation and translation of individual cancer | ||
| genomes for precision oncology. 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) <http://www.ensembl.org/info/docs/tools/vep/index.html>`__ with | ||
| oncology-relevant, up-to-date annotations retrieved flexibly through | ||
| `vcfanno <https://github.com/brentp/vcfanno>`__, and produces HTML | ||
| reports that can be navigated by clinical oncologists (Figure 1). | ||
| `vcfanno <https://github.com/brentp/vcfanno>`__, and produces | ||
| interactive HTML reports intended for clinical interpretation (Figure | ||
| 1). | ||
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| .. figure:: PCGR_workflow.png | ||
| :alt: | ||
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| Consortium <http://cancergenomics.no>`__, at the `Institute for Cancer | ||
| Research/Oslo University Hospital <http://radium.no>`__. | ||
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| Example reports | ||
| ^^^^^^^^^^^^^^^ | ||
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| - Report for a colorectal tumor sample (TCGA) | ||
| - Report for a breast tumor sample (TCGA) | ||
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| Why use PCGR? | ||
| ~~~~~~~~~~~~~ | ||
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| aid the interpretation of individual cancer genomes in a clinical | ||
| setting. | ||
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| If you use PCGR, please cite our paper: | ||
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| Sigve Nakken, Ghislain Fournous, Daniel Vodák, Lars Birger Aaasheim, and | ||
| Eivind Hovig. **Personal Cancer Genome Reporter: Variant Interpretation | ||
| Report For Precision Oncology** (2017). bioRxiv. | ||
| doi:\ `10.1101/122366 <https://doi.org/10.1101/122366>`__ | ||
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| Docker-based technology | ||
| ~~~~~~~~~~~~~~~~~~~~~~~ | ||
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| .. figure:: docker-logo50.png | ||
| :alt: | ||
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| Contact | ||
| ~~~~~~~ | ||
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| [email protected] | ||
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