automatic decomposition of multiallelics

Author: sigven

README.md

@@ -2,20 +2,23 @@
 
 ### Overview
 
-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).
 
 ![PCGR overview](PCGR_workflow.png)
 
 ### 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>
 
 ### PCGR documentation
 
 [![Documentation Status](https://readthedocs.org/projects/pcgr/badge/?version=latest)](http://pcgr.readthedocs.io/en/latest/?badge=latest)
 
+If you use PCGR, please cite our paper:
 
-### 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)
+
+### Annotation resources included in PCGR (v0.3.3)
 
 * [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)
@@ -53,16 +56,16 @@ A local installation of Python (it has been tested with [version 2.7.13](https:/
 
 #### STEP 2: Download PCGR
 
-<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>
 
-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 -`
 
     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)
 
 #### STEP 3: Input preprocessing
 
@@ -73,12 +76,9 @@ The PCGR workflow accepts two types of input files:
 
 PCGR can be run with either or both of the two input files present.
 
-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)
 
-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
 
 The tab-separated values file with copy number aberrations __MUST__ contain the following four columns:
 * Chromosome
@@ -112,7 +112,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__ in t
 
       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
@@ -146,7 +146,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__ in t
 
 The _examples_ folder contain sample files from TCGA. A report for a colorectal tumor case can be generated through the following command:
 
-`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`
 
 This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder:
 
@@ -157,3 +157,7 @@ This command will run the Docker-based PCGR workflow and produce the following o
 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
+
+## Contact
+
+[email protected]

docs/_build/doctrees/about.doctree

@@ -5,14 +5,15 @@ What is the Personal Cancer Genome Reporter (PCGR)?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 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).
 
 .. figure:: PCGR_workflow.png
    :alt: 
@@ -22,6 +23,12 @@ affiliated with the `Norwegian Cancer Genomics
 Consortium <http://cancergenomics.no>`__, at the `Institute for Cancer
 Research/Oslo University Hospital <http://radium.no>`__.
 
+Example reports
+^^^^^^^^^^^^^^^
+
+-  Report for a colorectal tumor sample (TCGA)
+-  Report for a breast tumor sample (TCGA)
+
 Why use PCGR?
 ~~~~~~~~~~~~~
 
@@ -37,6 +44,13 @@ and variant level. The application generates a tiered report that will
 aid the interpretation of individual cancer genomes in a clinical
 setting.
 
+If you use PCGR, please cite our paper:
+
+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>`__
+
 Docker-based technology
 ~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -50,3 +64,8 @@ for precision oncology <annotation_resources.html>`__.
 
 .. figure:: docker-logo50.png
    :alt: 
+
+Contact
+~~~~~~~
+
+[email protected]

docs/_build/doctrees/environment.pickle

@@ -42,18 +42,18 @@ terminal window.
 Download PCGR
 ^^^^^^^^^^^^^
 
-**April 19th 2017**: New release (0.3.2)
+**April 20th 2017**: New release (0.3.3)
 
 -  Download and unpack the `latest release
-   (0.3.2) <https://github.com/sigven/pcgr/releases/latest>`__
+   (0.3.3) <https://github.com/sigven/pcgr/releases/latest>`__
 
 -  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``)
+      version number, e.g. ``~/pcgr-0.3.3``)
    -  Decompress and untar the bundle, e.g. through the following Unix
       command:
       ``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -``
@@ -62,10 +62,10 @@ Download PCGR
    have been produced
 
 -  Pull the `PCGR Docker image -
-   0.3.2 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
-   (3.1Gb) :
+   0.3.3 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
+   (3.2Gb) :
 
-   -  ``docker pull sigven/pcgr:0.3.2`` (PCGR annotation engine)
+   -  ``docker pull sigven/pcgr:0.3.3`` (PCGR annotation engine)
 
 Run test - generation of clinical report for a cancer genome
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -89,7 +89,7 @@ A tumor sample report is generated by calling the Python script
 
     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
@@ -125,7 +125,7 @@ sequenced within TCGA. A report for a colorectal tumor case can be
 generated by running the following command in your terminal window:
 
 ``python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments``
-``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.2 ~/pcgr-0.3.2/examples tumor_sample.COAD``
+``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.3 ~/pcgr-0.3.3/examples tumor_sample.COAD``
 
 This command will run the Docker-based PCGR workflow and produce the
 following output files in the *examples* folder:

docs/_build/doctrees/getting_started.doctree

@@ -18,23 +18,11 @@ currently supported.
 VCF
 ^^^
 
-The following requirements **MUST** be met by the input VCF for PCGR to
-work properly:
-
-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
+-  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>`__
 
 **IMPORTANT NOTE 1**: Considering the VCF output for the `numerous
 somatic SNV/InDel callers <https://www.biostars.org/p/19104/>`__ that

docs/_build/doctrees/output.doctree

@@ -89,9 +89,13 @@
                 <p class="caption"><span class="caption-text">Table of Contents</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">About</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#what-is-the-personal-cancer-genome-reporter-pcgr">What is the Personal Cancer Genome Reporter (PCGR)?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#what-is-the-personal-cancer-genome-reporter-pcgr">What is the Personal Cancer Genome Reporter (PCGR)?</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#example-reports">Example reports</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference internal" href="#why-use-pcgr">Why use PCGR?</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#docker-based-technology">Docker-based technology</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#contact">Contact</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="getting_started.html">Getting started</a></li>
@@ -147,21 +151,29 @@ <h1>About<a class="headerlink" href="#about" title="Permalink to this headline">
 <div class="section" id="what-is-the-personal-cancer-genome-reporter-pcgr">
 <h2>What is the Personal Cancer Genome Reporter (PCGR)?<a class="headerlink" href="#what-is-the-personal-cancer-genome-reporter-pcgr" title="Permalink to this headline">¶</a></h2>
 <p>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 <a class="reference external" href="http://www.ensembl.org/info/docs/tools/vep/index.html">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 <a class="reference external" href="http://www.ensembl.org/info/docs/tools/vep/index.html">Ensembl’s Variant Effect Predictor
 (VEP)</a> with
 oncology-relevant, up-to-date annotations retrieved flexibly through
-<a class="reference external" href="https://github.com/brentp/vcfanno">vcfanno</a>, and produces HTML
-reports that can be navigated by clinical oncologists (Figure 1).</p>
+<a class="reference external" href="https://github.com/brentp/vcfanno">vcfanno</a>, and produces
+interactive HTML reports intended for clinical interpretation (Figure
+1).</p>
 <div class="figure">
 <img alt="" src="_images/PCGR_workflow.png" />
 </div>
 <p>The Personal Cancer Genome Reporter has been developed by scientists
 affiliated with the <a class="reference external" href="http://cancergenomics.no">Norwegian Cancer Genomics
 Consortium</a>, at the <a class="reference external" href="http://radium.no">Institute for Cancer
 Research/Oslo University Hospital</a>.</p>
+<div class="section" id="example-reports">
+<h3>Example reports<a class="headerlink" href="#example-reports" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Report for a colorectal tumor sample (TCGA)</li>
+<li>Report for a breast tumor sample (TCGA)</li>
+</ul>
+</div>
 </div>
 <div class="section" id="why-use-pcgr">
 <h2>Why use PCGR?<a class="headerlink" href="#why-use-pcgr" title="Permalink to this headline">¶</a></h2>
@@ -176,6 +188,11 @@ <h2>Why use PCGR?<a class="headerlink" href="#why-use-pcgr" title="Permalink to
 and variant level. The application generates a tiered report that will
 aid the interpretation of individual cancer genomes in a clinical
 setting.</p>
+<p>If you use PCGR, please cite our paper:</p>
+<p>Sigve Nakken, Ghislain Fournous, Daniel Vodák, Lars Birger Aaasheim, and
+Eivind Hovig. <strong>Personal Cancer Genome Reporter: Variant Interpretation
+Report For Precision Oncology</strong> (2017). bioRxiv.
+doi:<a class="reference external" href="https://doi.org/10.1101/122366">10.1101/122366</a></p>
 </div>
 <div class="section" id="docker-based-technology">
 <h2>Docker-based technology<a class="headerlink" href="#docker-based-technology" title="Permalink to this headline">¶</a></h2>
@@ -190,6 +207,10 @@ <h2>Docker-based technology<a class="headerlink" href="#docker-based-technology"
 <img alt="" src="_images/docker-logo50.png" />
 </div>
 </div>
+<div class="section" id="contact">
+<h2>Contact<a class="headerlink" href="#contact" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference external" href="mailto:sigven&#37;&#52;&#48;ifi&#46;uio&#46;no">sigven<span>&#64;</span>ifi<span>&#46;</span>uio<span>&#46;</span>no</a></p>
+</div>
 </div>