v0.3.2 - bug fix and input check

Author: sigven

README.md

@@ -15,7 +15,7 @@ The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package int
 [![Documentation Status](https://readthedocs.org/projects/pcgr/badge/?version=latest)](http://pcgr.readthedocs.io/en/latest/?badge=latest)
 
 
-### Annotation resources included in PCGR (v0.3)
+### Annotation resources included in PCGR (v0.3.2)
 
 * [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 +53,16 @@ A local installation of Python (it has been tested with [version 2.7.13](https:/
 
 #### STEP 2: Download PCGR
 
-<font color="red"><b>April 14th 2017</b>: New release (0.3.1)</font>
+<font color="red"><b>April 19th 2017</b>: New release (0.3.2)</font>
 
-1. Download and unpack the [latest release (0.3.1)](https://github.com/sigven/pcgr/releases/latest)
+1. Download and unpack the [latest release (0.3.2)](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-0.3.1`)
+   * 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`)
    * 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 (0.3.1)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb):
-   * `docker pull sigven/pcgr:0.3.1` (PCGR annotation engine)
+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)
 
 #### STEP 3: Input preprocessing
 
@@ -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-0.3.1
+                          e.g. ~/pcgr-0.3.2
       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-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD`
+`python pcgr.py --input_vcf tumor_sample.COAD.vcf.gz --input_cna_segments tumor_sample.COAD.cna.tsv ~/pcgr-0.3.2 ~/pcgr-0.3.2/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/annotation_resources.doctree

@@ -79,19 +79,19 @@ A requirement for all variant annotation datasets used in PCGR is that
 they have been mapped unambiguously to the human genome (GRCh37). For
 most datasets this is already the case (i.e. dbSNP, COSMIC, ClinVar
 etc.). A significant proportion of variants in the annotation datasets
-related to clinical interpretation, CIViC and CBMDB, are however not
+related to clinical interpretation, CIViC and CBMDB, is however not
 mapped to the genome. Whenever possible, we have utilized
 `TransVar <http://bioinformatics.mdanderson.org/transvarweb/>`__ to
 identify the actual genomic variants (e.g. *g.chr7:140453136A>T*) that
-corresponds to variants reported with other HGVS nomenclature (e.g.
+correspond to variants reported with other HGVS nomenclature (e.g.
 *p.V600E*).
 
 Other data quality concerns
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 **Clinical biomarkers**
 
-Clinical biomarkers included in PCGR is limited to the following:
+Clinical biomarkers included in PCGR are limited to the following:
 
 -  Markers reported at the variant level (e.g. **BRAF p.V600E**)
 -  Markers reported at the codon level (e.g. **KRAS p.G12**)

docs/_build/doctrees/environment.pickle

@@ -42,18 +42,18 @@ terminal window.
 Download PCGR
 ^^^^^^^^^^^^^
 
-**April 14th 2017**: New release (0.3.1)
+**April 19th 2017**: New release (0.3.2)
 
 -  Download and unpack the `latest release
-   (0.3.1) <https://github.com/sigven/pcgr/releases/latest>`__
+   (0.3.2) <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-0.3.1``)
+      version number, e.g. ``~/pcgr-0.3.2``)
    -  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 -
-   0.3.1 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
+   0.3.2 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
    (3.1Gb) :
 
-   -  ``docker pull sigven/pcgr:0.3.1`` (PCGR annotation engine)
+   -  ``docker pull sigven/pcgr:0.3.2`` (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
+                        e.g. ~/pcgr-0.3.2
     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-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD``
+``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.2 ~/pcgr-0.3.2/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

@@ -36,17 +36,24 @@ work properly:
       `tabix <http://www.htslib.org/doc/tabix.html>`__
    -  'chr' must be stripped from the chromosome names
 
-**IMPORTANT NOTE**: Considering the VCF output for the `numerous somatic
-SNV/InDel callers <https://www.biostars.org/p/19104/>`__ that have been
-developed, we have a experienced a general lack of uniformity and
-robustness for the representation of somatic variant genotype data (e.g.
-variant allelic depths (tumor/normal), genotype quality etc.). In the
-output results provided within the current version of PCGR, we are
+**IMPORTANT NOTE 1**: Considering the VCF output for the `numerous
+somatic SNV/InDel callers <https://www.biostars.org/p/19104/>`__ that
+have been developed, we have a experienced a general lack of uniformity
+and robustness for the representation of somatic variant genotype data
+(e.g. variant allelic depths (tumor/normal), genotype quality etc.). In
+the output results provided within the current version of PCGR, we are
 considering PASSed variants only, and variant genotype data (i.e. as
 found in the VCF SAMPLE columns) are not handled or parsed. As improved
 standards for this matter may emerge, we will strive to include this
 information in the annotated output files.
 
+**IMPORTANT NOTE 2**: PCGR generates a number of VCF INFO annotation
+tags that is appended to the query VCF. We will therefore encourage the
+users to submit query VCF files that have not been subject to
+annotations by other means, but rather a VCF file that comes directly
+from variant calling. If not, there are likely to be INFO tags in the
+query VCF file that coincide with those produced by PCGR.
+
 Copy number segments
 ^^^^^^^^^^^^^^^^^^^^
 

docs/_build/doctrees/output.doctree

@@ -234,17 +234,17 @@ <h2>Genome mapping<a class="headerlink" href="#genome-mapping" title="Permalink
 they have been mapped unambiguously to the human genome (GRCh37). For
 most datasets this is already the case (i.e. dbSNP, COSMIC, ClinVar
 etc.). A significant proportion of variants in the annotation datasets
-related to clinical interpretation, CIViC and CBMDB, are however not
+related to clinical interpretation, CIViC and CBMDB, is however not
 mapped to the genome. Whenever possible, we have utilized
 <a class="reference external" href="http://bioinformatics.mdanderson.org/transvarweb/">TransVar</a> to
 identify the actual genomic variants (e.g. <em>g.chr7:140453136A&gt;T</em>) that
-corresponds to variants reported with other HGVS nomenclature (e.g.
+correspond to variants reported with other HGVS nomenclature (e.g.
 <em>p.V600E</em>).</p>
 </div>
 <div class="section" id="other-data-quality-concerns">
 <h2>Other data quality concerns<a class="headerlink" href="#other-data-quality-concerns" title="Permalink to this headline">¶</a></h2>
 <p><strong>Clinical biomarkers</strong></p>
-<p>Clinical biomarkers included in PCGR is limited to the following:</p>
+<p>Clinical biomarkers included in PCGR are limited to the following:</p>
 <ul class="simple">
 <li>Markers reported at the variant level (e.g. <strong>BRAF p.V600E</strong>)</li>
 <li>Markers reported at the codon level (e.g. <strong>KRAS p.G12</strong>)</li>

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

@@ -189,18 +189,18 @@ <h3>Python<a class="headerlink" href="#python" title="Permalink to this headline
 </div>
 <div class="section" id="download-pcgr">
 <h3>Download PCGR<a class="headerlink" href="#download-pcgr" title="Permalink to this headline">¶</a></h3>
-<p><strong>April 14th 2017</strong>: New release (0.3.1)</p>
+<p><strong>April 19th 2017</strong>: New release (0.3.2)</p>
 <ul>
 <li><p class="first">Download and unpack the <a class="reference external" href="https://github.com/sigven/pcgr/releases/latest">latest release
-(0.3.1)</a></p>
+(0.3.2)</a></p>
 </li>
 <li><p class="first">Download and unpack the data bundle (approx. 17Gb) in the PCGR
 directory</p>
 <ul class="simple">
 <li>Download <a class="reference external" href="https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/">the latest data
 bundle</a>
 from Google Drive to <code class="docutils literal"><span class="pre">~/pcgr-X.X</span></code> (replace <em>X.X</em> with the
-version number, e.g. <code class="docutils literal"><span class="pre">~/pcgr-0.3.1</span></code>)</li>
+version number, e.g. <code class="docutils literal"><span class="pre">~/pcgr-0.3.2</span></code>)</li>
 <li>Decompress and untar the bundle, e.g. through the following Unix
 command:
 <code class="docutils literal"><span class="pre">gzip</span> <span class="pre">-dc</span> <span class="pre">pcgr.databundle.GRCh37.YYYYMMDD.tgz</span> <span class="pre">|</span> <span class="pre">tar</span> <span class="pre">xvf</span> <span class="pre">-</span></code></li>
@@ -209,10 +209,10 @@ <h3>Download PCGR<a class="headerlink" href="#download-pcgr" title="Permalink to
 have been produced</p>
 </li>
 <li><p class="first">Pull the <a class="reference external" href="https://hub.docker.com/r/sigven/pcgr/">PCGR Docker image -
-0.3.1</a> from DockerHub
+0.3.2</a> from DockerHub
 (3.1Gb) :</p>
 <ul class="simple">
-<li><code class="docutils literal"><span class="pre">docker</span> <span class="pre">pull</span> <span class="pre">sigven/pcgr:0.3.1</span></code> (PCGR annotation engine)</li>
+<li><code class="docutils literal"><span class="pre">docker</span> <span class="pre">pull</span> <span class="pre">sigven/pcgr:0.3.2</span></code> (PCGR annotation engine)</li>
 </ul>
 </li>
 </ul>
@@ -236,7 +236,7 @@ <h2>Run test - generation of clinical report for a cancer genome<a class="header
 
 <span class="n">positional</span> <span class="n">arguments</span><span class="p">:</span>
 <span class="n">pcgr_dir</span>              <span class="n">PCGR</span> <span class="n">base</span> <span class="n">directory</span> <span class="k">with</span> <span class="n">accompanying</span> <span class="n">data</span> <span class="n">directory</span><span class="p">,</span>
-                    <span class="n">e</span><span class="o">.</span><span class="n">g</span><span class="o">.</span> <span class="o">~/</span><span class="n">pcgr</span><span class="o">-</span><span class="mf">0.3</span>
+                    <span class="n">e</span><span class="o">.</span><span class="n">g</span><span class="o">.</span> <span class="o">~/</span><span class="n">pcgr</span><span class="o">-</span><span class="mf">0.3</span><span class="o">.</span><span class="mi">2</span>
 <span class="n">output_dir</span>            <span class="n">Output</span> <span class="n">directory</span>
 <span class="n">sample_id</span>             <span class="n">Tumor</span> <span class="n">sample</span><span class="o">/</span><span class="n">cancer</span> <span class="n">genome</span> <span class="n">identifier</span> <span class="o">-</span> <span class="n">prefix</span> <span class="k">for</span>
                     <span class="n">output</span> <span class="n">files</span>
@@ -272,7 +272,7 @@ <h2>Run test - generation of clinical report for a cancer genome<a class="header
 sequenced within TCGA. A report for a colorectal tumor case can be
 generated by running the following command in your terminal window:</p>
 <p><code class="docutils literal"><span class="pre">python</span> <span class="pre">pcgr.py</span> <span class="pre">--input_vcf</span> <span class="pre">examples/tumor_sample.COAD.vcf.gz</span> <span class="pre">--input_cna_segments</span></code>
-<code class="docutils literal"><span class="pre">examples/tumor_sample.COAD.cna.tsv</span> <span class="pre">~/pcgr-0.3.1</span> <span class="pre">~/pcgr-0.3.1/examples</span> <span class="pre">tumor_sample.COAD</span></code></p>
+<code class="docutils literal"><span class="pre">examples/tumor_sample.COAD.cna.tsv</span> <span class="pre">~/pcgr-0.3.2</span> <span class="pre">~/pcgr-0.3.2/examples</span> <span class="pre">tumor_sample.COAD</span></code></p>
 <p>This command will run the Docker-based PCGR workflow and produce the
 following output files in the <em>examples</em> folder:</p>
 <ol class="arabic simple">