diff --git a/README.md b/README.md
index 69786c4c..2c60ff33 100755
--- a/README.md
+++ b/README.md
@@ -8,14 +8,16 @@ The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package for
 
 ### News
 
-* _November 14th 2017_: **0.5.0 release**
+* _November 14th 2017_: **0.5.1 release**
   * Updated version of VEP (v90)
   * Updated versions of ClinVar, Uniprot KB, CIViC, CBMDB
   * Removal of ExAC (replaced by gnomAD), removal of COSMIC due to licensing restrictions
   * Users can analyze samples run without matching control (i.e. tumor-only)
   * PCGR pipeline is now configured through a [TOML-based configuration file](https://github.com/toml-lang/toml)
   * Bug fixes / general speed improvements
-  * _Work in progress (0.5.1 release): Export of report data through JSON_
+  * _Work in progress: Export of report data through JSON_
+* _November 14th 2017_: **0.5.1 release**
+  * Bug fixing (VCF validation)
 
 ### Example reports
 * [Report for a breast tumor sample (TCGA)](http://folk.uio.no/sigven/tumor_sample.BRCA.0.5.0.pcgr.html)
@@ -30,7 +32,7 @@ If you use PCGR, please cite our preprint paper:
 
 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). bioRxiv. doi:[10.1101/122366](https://doi.org/10.1101/122366)
 
-### Annotation resources included in PCGR (v0.5.0)
+### Annotation resources included in PCGR (v0.5.1)
 
 * [VEP v90](http://www.ensembl.org/info/docs/tools/vep/index.html) - Variant Effect Predictor release 90 (GENCODE v27 as the gene reference dataset)
 * [dBNSFP v3.4](https://sites.google.com/site/jpopgen/dbNSFP) - Database of non-synonymous functional predictions (March 2017)
@@ -71,14 +73,14 @@ A local installation of Python (it has been tested with [version 2.7.13](https:/
 
 #### STEP 2: Download PCGR
 
-1. Download and unpack the [latest software release (0.5.0)](https://github.com/sigven/pcgr/releases/latest)
+1. Download and unpack the [latest software release (0.5.1)](https://github.com/sigven/pcgr/releases/latest)
 2. Download and unpack the data bundle (approx. 16Gb) in the PCGR directory
-   * Download [the accompanying data bundle](https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g `~/pcgr-0.5.0`)
+   * Download [the accompanying data bundle](https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g `~/pcgr-0.5.1`)
    * 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 (0.5.0)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (approx 4.2Gb):
-   * `docker pull sigven/pcgr:0.5.0` (PCGR annotation engine)
+3. Pull the [PCGR Docker image (0.5.1)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (approx 4.2Gb):
+   * `docker pull sigven/pcgr:0.5.1` (PCGR annotation engine)
 
 #### STEP 3: Input preprocessing
 
@@ -124,7 +126,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__, whi
 
 	positional arguments:
 	pcgr_dir              PCGR base directory with accompanying data directory,
-					e.g. ~/pcgr-0.5.0
+					e.g. ~/pcgr-0.5.1
 	output_dir            Output directory
 	configuration_file    PCGR configuration file (TOML format)
 	sample_id             Tumor sample/cancer genome identifier - prefix for
@@ -158,9 +160,9 @@ The configuration file, formatted using [TOML](https://github.com/toml-lang/toml
 
 The _examples_ folder contain input files from two tumor samples sequenced within TCGA. It also contains a PCGR configuration file. A report for a colorectal tumor case can be generated by running the following command in your terminal window:
 
-`python pcgr.py --input_vcf ~/pcgr-0.5.0/examples/tumor_sample.COAD.vcf.gz`
-`--input_cna ~/pcgr-0.5.0/examples/tumor_sample.COAD.cna.tsv`
-` ~/pcgr-0.5.0/examples ~/pcgr-0.5.0/examples/pcgr_configuration.toml tumor_sample.COAD`
+`python pcgr.py --input_vcf ~/pcgr-0.5.1/examples/tumor_sample.COAD.vcf.gz`
+`--input_cna ~/pcgr-0.5.1/examples/tumor_sample.COAD.cna.tsv`
+` ~/pcgr-0.5.1/examples ~/pcgr-0.5.1/examples/pcgr_configuration.toml tumor_sample.COAD`
 
 This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder:
 
diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle
index 63bc3538..ede4fe0f 100644
Binary files a/docs/_build/doctrees/environment.pickle and b/docs/_build/doctrees/environment.pickle differ
diff --git a/docs/_build/doctrees/getting_started.doctree b/docs/_build/doctrees/getting_started.doctree
index 8ac4480c..aadf8c6d 100644
Binary files a/docs/_build/doctrees/getting_started.doctree and b/docs/_build/doctrees/getting_started.doctree differ
diff --git a/docs/_build/doctrees/output.doctree b/docs/_build/doctrees/output.doctree
index 5e28c661..50a2e8ce 100644
Binary files a/docs/_build/doctrees/output.doctree and b/docs/_build/doctrees/output.doctree differ
diff --git a/docs/_build/html/_sources/getting_started.rst.txt b/docs/_build/html/_sources/getting_started.rst.txt
index 979d70a9..c54caa5a 100644
--- a/docs/_build/html/_sources/getting_started.rst.txt
+++ b/docs/_build/html/_sources/getting_started.rst.txt
@@ -55,7 +55,7 @@ Download PCGR
 ^^^^^^^^^^^^^
 
 -  Download and unpack the `latest software release
-   (0.5.0) <https://github.com/sigven/pcgr/releases/latest>`__
+   (0.5.1) <https://github.com/sigven/pcgr/releases/latest>`__
 
 -  Download and unpack the data bundle (approx. 16Gb) in the PCGR
    directory
@@ -63,7 +63,7 @@ Download PCGR
    -  Download `the accompanying data
       bundle <https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/>`__
       from Google Drive to ``~/pcgr-X.X`` (replace *X.X* with the
-      version number, e.g. ``~/pcgr-0.5.0``)
+      version number, e.g. ``~/pcgr-0.5.1``)
    -  Decompress and untar the bundle, e.g. through the following Unix
       command:
       ``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -``
@@ -72,10 +72,10 @@ Download PCGR
    have been produced
 
 -  Pull the `PCGR Docker image -
-   0.5.0 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
+   0.5.1 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
    (approx 4.2Gb) :
 
-   -  ``docker pull sigven/pcgr:0.5.0`` (PCGR annotation engine)
+   -  ``docker pull sigven/pcgr:0.5.1`` (PCGR annotation engine)
 
 Run test - generation of clinical report for a cancer genome
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -94,7 +94,7 @@ 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-0.5.0
+                    e.g. ~/pcgr-0.5.1
     output_dir            Output directory
     configuration_file    PCGR configuration file (TOML format)
     sample_id             Tumor sample/cancer genome identifier - prefix for
@@ -135,9 +135,9 @@ sequenced within TCGA. It also contains a PCGR configuration file. A
 report for a colorectal tumor case can be generated by running the
 following command in your terminal window:
 
-``python pcgr.py --input_vcf ~/pcgr-0.5.0/examples/tumor_sample.COAD.vcf.gz``
-``--input_cna ~/pcgr-0.5.0/examples/tumor_sample.COAD.cna.tsv``
-``~/pcgr-0.5.0/examples ~/pcgr-0.5.0/examples/pcgr_configuration.toml tumor_sample.COAD``
+``python pcgr.py --input_vcf ~/pcgr-0.5.1/examples/tumor_sample.COAD.vcf.gz``
+``--input_cna ~/pcgr-0.5.1/examples/tumor_sample.COAD.cna.tsv``
+``~/pcgr-0.5.1/examples ~/pcgr-0.5.1/examples/pcgr_configuration.toml tumor_sample.COAD``
 
 This command will run the Docker-based PCGR workflow and produce the
 following output files in the *examples* folder:
diff --git a/docs/_build/html/_sources/output.rst.txt b/docs/_build/html/_sources/output.rst.txt
index 19ac6e74..92badf98 100644
--- a/docs/_build/html/_sources/output.rst.txt
+++ b/docs/_build/html/_sources/output.rst.txt
@@ -482,29 +482,29 @@ processing with the PCGR annotation pipeline:
    mutations in
    cancer <http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/>`__
    database, as provided by VEP
--  *COSMIC\_CODON\_FRAC\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CODON\_FRAC\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CODON\_COUNT\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CODON\_COUNT\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CANCER\_TYPE\_ALL - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CANCER\_TYPE\_ALL - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_SAMPLE\_SOURCE - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_SAMPLE\_SOURCE - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
--  *COSMIC\_DRUG\_RESISTANCE - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_DRUG\_RESISTANCE - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_FATHMM\_PRED - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_FATHMM\_PRED - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
--  *COSMIC\_VARTYPE - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_VARTYPE - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
--  *COSMIC\_CONSEQUENCE - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_CONSEQUENCE - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
 
 .. raw:: html
diff --git a/docs/_build/html/getting_started.html b/docs/_build/html/getting_started.html
index 34778815..18616cfb 100644
--- a/docs/_build/html/getting_started.html
+++ b/docs/_build/html/getting_started.html
@@ -202,7 +202,7 @@ <h3>Python<a class="headerlink" href="#python" title="Permalink to this headline
 <h3>Download PCGR<a class="headerlink" href="#download-pcgr" title="Permalink to this headline">¶</a></h3>
 <ul>
 <li><p class="first">Download and unpack the <a class="reference external" href="https://github.com/sigven/pcgr/releases/latest">latest software release
-(0.5.0)</a></p>
+(0.5.1)</a></p>
 </li>
 <li><p class="first">Download and unpack the data bundle (approx. 16Gb) in the PCGR
 directory</p>
@@ -210,7 +210,7 @@ <h3>Download PCGR<a class="headerlink" href="#download-pcgr" title="Permalink to
 <li>Download <a class="reference external" href="https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/">the accompanying 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-0.5.0</span></code>)</li>
+version number, e.g. <code class="docutils literal"><span class="pre">~/pcgr-0.5.1</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>
@@ -219,10 +219,10 @@ <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 -
-0.5.0</a> from DockerHub
+0.5.1</a> from DockerHub
 (approx 4.2Gb) :</p>
 <ul class="simple">
-<li><code class="docutils literal"><span class="pre">docker</span> <span class="pre">pull</span> <span class="pre">sigven/pcgr:0.5.0</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:0.5.1</span></code> (PCGR annotation engine)</li>
 </ul>
 </li>
 </ul>
@@ -241,7 +241,7 @@ <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">0.5</span><span class="o">.</span><span class="mi">0</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">0.5</span><span class="o">.</span><span class="mi">1</span>
 <span class="n">output_dir</span>            <span class="n">Output</span> <span class="n">directory</span>
 <span class="n">configuration_file</span>    <span class="n">PCGR</span> <span class="n">configuration</span> <span class="n">file</span> <span class="p">(</span><span class="n">TOML</span> <span class="nb">format</span><span class="p">)</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>
@@ -282,9 +282,9 @@ <h2>Run test - generation of clinical report for a cancer genome<a class="header
 sequenced within TCGA. It also contains a PCGR configuration file. 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">~/pcgr-0.5.0/examples/tumor_sample.COAD.vcf.gz</span></code>
-<code class="docutils literal"><span class="pre">--input_cna</span> <span class="pre">~/pcgr-0.5.0/examples/tumor_sample.COAD.cna.tsv</span></code>
-<code class="docutils literal"><span class="pre">~/pcgr-0.5.0/examples</span> <span class="pre">~/pcgr-0.5.0/examples/pcgr_configuration.toml</span> <span class="pre">tumor_sample.COAD</span></code></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">~/pcgr-0.5.1/examples/tumor_sample.COAD.vcf.gz</span></code>
+<code class="docutils literal"><span class="pre">--input_cna</span> <span class="pre">~/pcgr-0.5.1/examples/tumor_sample.COAD.cna.tsv</span></code>
+<code class="docutils literal"><span class="pre">~/pcgr-0.5.1/examples</span> <span class="pre">~/pcgr-0.5.1/examples/pcgr_configuration.toml</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">
diff --git a/docs/_build/html/output.html b/docs/_build/html/output.html
index f67d035f..72caea86 100644
--- a/docs/_build/html/output.html
+++ b/docs/_build/html/output.html
@@ -667,29 +667,29 @@ <h4><em>Variant frequencies/annotations in germline/somatic databases</em><a cla
 mutations in
 cancer</a>
 database, as provided by VEP</li>
-<li><em>COSMIC_CODON_FRAC_GW - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_CODON_FRAC_GW - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_CODON_COUNT_GW - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_CODON_COUNT_GW - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_SITE_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_SITE_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_CANCER_TYPE_GW - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_CANCER_TYPE_GW - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_CANCER_TYPE_ALL - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_CANCER_TYPE_ALL - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_SITE_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_SITE_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_CANCER_TYPE_GW - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_CANCER_TYPE_GW - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_SAMPLE_SOURCE - Deprecated in 0.5.0 due to COSMIC licensing
+<li><em>COSMIC_SAMPLE_SOURCE - Deprecated in 0.5.1 due to COSMIC licensing
 restrictions</em></li>
-<li><em>COSMIC_DRUG_RESISTANCE - Deprecated in 0.5.0 due to COSMIC
+<li><em>COSMIC_DRUG_RESISTANCE - Deprecated in 0.5.1 due to COSMIC
 licensing restrictions</em></li>
-<li><em>COSMIC_FATHMM_PRED - Deprecated in 0.5.0 due to COSMIC licensing
+<li><em>COSMIC_FATHMM_PRED - Deprecated in 0.5.1 due to COSMIC licensing
 restrictions</em></li>
-<li><em>COSMIC_VARTYPE - Deprecated in 0.5.0 due to COSMIC licensing
+<li><em>COSMIC_VARTYPE - Deprecated in 0.5.1 due to COSMIC licensing
 restrictions</em></li>
-<li><em>COSMIC_CONSEQUENCE - Deprecated in 0.5.0 due to COSMIC licensing
+<li><em>COSMIC_CONSEQUENCE - Deprecated in 0.5.1 due to COSMIC licensing
 restrictions</em></li>
 </ul>
 <!--- COSMIC\_MUTATION_ID - Mutation identifier in [Catalog of somatic mutations in cancer](http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/) database
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 82767640..3277ee38 100755
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -27,16 +27,16 @@ An installation of Python (version 2.7.13) is required to run PCGR. Check that P
 
 #### Download PCGR
 
-* Download and unpack the [latest software release (0.5.0)](https://github.com/sigven/pcgr/releases/latest)
+* Download and unpack the [latest software release (0.5.1)](https://github.com/sigven/pcgr/releases/latest)
 
 * Download and unpack the data bundle (approx. 16Gb) in the PCGR directory
-    * Download [the accompanying data bundle](https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g. `~/pcgr-0.5.0`)
+    * Download [the accompanying data bundle](https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g. `~/pcgr-0.5.1`)
     * Decompress and untar the 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
 
-* Pull the [PCGR Docker image - 0.5.0](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (approx 4.2Gb) :
-    * `docker pull sigven/pcgr:0.5.0` (PCGR annotation engine)
+* Pull the [PCGR Docker image - 0.5.1](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (approx 4.2Gb) :
+    * `docker pull sigven/pcgr:0.5.1` (PCGR annotation engine)
 
 
 ### Run test - generation of clinical report for a cancer genome
@@ -52,7 +52,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__, whi
 
 	positional arguments:
 	pcgr_dir              PCGR base directory with accompanying data directory,
-					e.g. ~/pcgr-0.5.0
+					e.g. ~/pcgr-0.5.1
 	output_dir            Output directory
 	configuration_file    PCGR configuration file (TOML format)
 	sample_id             Tumor sample/cancer genome identifier - prefix for
@@ -86,9 +86,9 @@ The configuration file, formatted using [TOML](https://github.com/toml-lang/toml
 
 The _examples_ folder contain input files from two tumor samples sequenced within TCGA. It also contains a PCGR configuration file. A report for a colorectal tumor case can be generated by running the following command in your terminal window:
 
-`python pcgr.py --input_vcf ~/pcgr-0.5.0/examples/tumor_sample.COAD.vcf.gz`
-`--input_cna ~/pcgr-0.5.0/examples/tumor_sample.COAD.cna.tsv`
-` ~/pcgr-0.5.0/examples ~/pcgr-0.5.0/examples/pcgr_configuration.toml tumor_sample.COAD`
+`python pcgr.py --input_vcf ~/pcgr-0.5.1/examples/tumor_sample.COAD.vcf.gz`
+`--input_cna ~/pcgr-0.5.1/examples/tumor_sample.COAD.cna.tsv`
+` ~/pcgr-0.5.1/examples ~/pcgr-0.5.1/examples/pcgr_configuration.toml tumor_sample.COAD`
 
 This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder:
 
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 979d70a9..c54caa5a 100755
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -55,7 +55,7 @@ Download PCGR
 ^^^^^^^^^^^^^
 
 -  Download and unpack the `latest software release
-   (0.5.0) <https://github.com/sigven/pcgr/releases/latest>`__
+   (0.5.1) <https://github.com/sigven/pcgr/releases/latest>`__
 
 -  Download and unpack the data bundle (approx. 16Gb) in the PCGR
    directory
@@ -63,7 +63,7 @@ Download PCGR
    -  Download `the accompanying data
       bundle <https://drive.google.com/file/d/1ZKDef-dsRA4rMv5jXU8vrBj5PvwOZ3vb/>`__
       from Google Drive to ``~/pcgr-X.X`` (replace *X.X* with the
-      version number, e.g. ``~/pcgr-0.5.0``)
+      version number, e.g. ``~/pcgr-0.5.1``)
    -  Decompress and untar the bundle, e.g. through the following Unix
       command:
       ``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -``
@@ -72,10 +72,10 @@ Download PCGR
    have been produced
 
 -  Pull the `PCGR Docker image -
-   0.5.0 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
+   0.5.1 <https://hub.docker.com/r/sigven/pcgr/>`__ from DockerHub
    (approx 4.2Gb) :
 
-   -  ``docker pull sigven/pcgr:0.5.0`` (PCGR annotation engine)
+   -  ``docker pull sigven/pcgr:0.5.1`` (PCGR annotation engine)
 
 Run test - generation of clinical report for a cancer genome
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -94,7 +94,7 @@ 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-0.5.0
+                    e.g. ~/pcgr-0.5.1
     output_dir            Output directory
     configuration_file    PCGR configuration file (TOML format)
     sample_id             Tumor sample/cancer genome identifier - prefix for
@@ -135,9 +135,9 @@ sequenced within TCGA. It also contains a PCGR configuration file. A
 report for a colorectal tumor case can be generated by running the
 following command in your terminal window:
 
-``python pcgr.py --input_vcf ~/pcgr-0.5.0/examples/tumor_sample.COAD.vcf.gz``
-``--input_cna ~/pcgr-0.5.0/examples/tumor_sample.COAD.cna.tsv``
-``~/pcgr-0.5.0/examples ~/pcgr-0.5.0/examples/pcgr_configuration.toml tumor_sample.COAD``
+``python pcgr.py --input_vcf ~/pcgr-0.5.1/examples/tumor_sample.COAD.vcf.gz``
+``--input_cna ~/pcgr-0.5.1/examples/tumor_sample.COAD.cna.tsv``
+``~/pcgr-0.5.1/examples ~/pcgr-0.5.1/examples/pcgr_configuration.toml tumor_sample.COAD``
 
 This command will run the Docker-based PCGR workflow and produce the
 following output files in the *examples* folder:
diff --git a/docs/output.md b/docs/output.md
index d5a4ed58..aff7730f 100755
--- a/docs/output.md
+++ b/docs/output.md
@@ -234,18 +234,18 @@ Here, the __sample_id__ is provided as input by the user, and reflects a unique
   - GWAS\_CATALOG_PMID - Variant is linked to phenotype through the [GWAS Catalog](https://www.ebi.ac.uk/gwas/), literature in PMID list
   - GWAS\_CATALOG\_TRAIT_URI - List of trait URIs for GWAS-associated variant
   - COSMIC\_MUTATION\_ID - Mutation identifier in [Catalog of somatic mutations in cancer](http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/) database, as provided by VEP
-  - *COSMIC\_CODON_FRAC\_GW - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_CODON_COUNT\_GW - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_CANCER\_TYPE\_ALL - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_CANCER_TYPE\_GW - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC_SAMPLE_SOURCE - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC\_DRUG\_RESISTANCE - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC_FATHMM_PRED - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC_VARTYPE - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
-  - *COSMIC_CONSEQUENCE - Deprecated in 0.5.0 due to COSMIC licensing restrictions*
+  - *COSMIC\_CODON_FRAC\_GW - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_CODON_COUNT\_GW - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_CANCER\_TYPE\_ALL - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_CANCER_TYPE\_GW - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC_SAMPLE_SOURCE - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC\_DRUG\_RESISTANCE - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC_FATHMM_PRED - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC_VARTYPE - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
+  - *COSMIC_CONSEQUENCE - Deprecated in 0.5.1 due to COSMIC licensing restrictions*
 
 
 <!--- COSMIC\_MUTATION_ID - Mutation identifier in [Catalog of somatic mutations in cancer](http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/) database
diff --git a/docs/output.rst b/docs/output.rst
index 19ac6e74..92badf98 100755
--- a/docs/output.rst
+++ b/docs/output.rst
@@ -482,29 +482,29 @@ processing with the PCGR annotation pipeline:
    mutations in
    cancer <http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/>`__
    database, as provided by VEP
--  *COSMIC\_CODON\_FRAC\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CODON\_FRAC\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CODON\_COUNT\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CODON\_COUNT\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CANCER\_TYPE\_ALL - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CANCER\_TYPE\_ALL - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_SITE\_HISTOLOGY - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_CANCER\_TYPE\_GW - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_SAMPLE\_SOURCE - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_SAMPLE\_SOURCE - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
--  *COSMIC\_DRUG\_RESISTANCE - Deprecated in 0.5.0 due to COSMIC
+-  *COSMIC\_DRUG\_RESISTANCE - Deprecated in 0.5.1 due to COSMIC
    licensing restrictions*
--  *COSMIC\_FATHMM\_PRED - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_FATHMM\_PRED - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
--  *COSMIC\_VARTYPE - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_VARTYPE - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
--  *COSMIC\_CONSEQUENCE - Deprecated in 0.5.0 due to COSMIC licensing
+-  *COSMIC\_CONSEQUENCE - Deprecated in 0.5.1 due to COSMIC licensing
    restrictions*
 
 .. raw:: html
diff --git a/pcgr.py b/pcgr.py
index e753e57a..037c17e3 100755
--- a/pcgr.py
+++ b/pcgr.py
@@ -11,7 +11,7 @@
 import platform
 import toml
 
-version = '0.5.0'
+version = '0.5.1'
 
 def __main__():
    
@@ -317,6 +317,7 @@ def run_pcgr(host_directories, docker_image_version, config_options, sample_id,
    
    ## set basic Docker run commands
    output_vcf = 'None'
+   output_pass_vcf = 'None'
    uid = ''
    logger = getlogger('pcgr-get-OS')
    if platform.system() == 'Linux' or platform.system() == 'Darwin' or sys.platform == 'darwin' or sys.platform == 'linux2' or sys.platform == 'linux':
@@ -372,11 +373,13 @@ def run_pcgr(host_directories, docker_image_version, config_options, sample_id,
       
       ## Define input, output and temporary file names
       output_vcf = '/workdir/output/' + str(sample_id) + '.pcgr.vcf.gz'
+      output_pass_vcf = '/workdir/output/' + str(sample_id) + '.pcgr.pass.vcf.gz'
       input_vcf_pcgr_ready = '/workdir/output/' + re.sub(r'(\.vcf$|\.vcf\.gz$)','.pcgr_ready.vcf.gz',host_directories['input_vcf_basename_host'])
       vep_vcf = re.sub(r'(\.vcf$|\.vcf\.gz$)','.pcgr_vep.vcf',input_vcf_pcgr_ready)
       vep_vcfanno_vcf = re.sub(r'(\.vcf$|\.vcf\.gz$)','.pcgr_vep.vcfanno.vcf',input_vcf_pcgr_ready)
       vep_tmp_vcf = vep_vcf + '.tmp'
       vep_vcfanno_annotated_vcf = re.sub(r'\.vcfanno','.vcfanno.annotated',vep_vcfanno_vcf) + '.gz'
+      vep_vcfanno_annotated_pass_vcf = re.sub(r'\.vcfanno','.vcfanno.annotated.pass',vep_vcfanno_vcf) + '.gz'
       
       vep_main_command = str(docker_command_run1) + "vep --input_file " + str(input_vcf_pcgr_ready) + " --output_file " + str(vep_tmp_vcf) + " --vcf --check_ref --flag_pick_allele --force_overwrite --species homo_sapiens --assembly GRCh37 --offline --fork " + str(config_options['other']['n_vep_forks']) + " --af --af_1kg --af_gnomad --variant_class --regulatory --domains --hgvs --hgvsg --symbol --protein --ccds --uniprot --appris --biotype --canonical --gencode_basic --cache --numbers --total_length --allele_number --no_escape --xref_refseq --dir /usr/local/share/vep/data --fasta /usr/local/share/vep/data/homo_sapiens/90_GRCh37/Homo_sapiens.GRCh37.75.dna.primary_assembly.fa.gz\""
       if config_options['other']['vep_skip_intergenic'] == 1:
@@ -413,17 +416,22 @@ def run_pcgr(host_directories, docker_image_version, config_options, sample_id,
       
       create_output_vcf_command1 = str(docker_command_run2) + 'mv ' + str(vep_vcfanno_annotated_vcf) + ' ' + str(output_vcf) + "\""
       create_output_vcf_command2 = str(docker_command_run2) + 'mv ' + str(vep_vcfanno_annotated_vcf) + '.tbi ' + str(output_vcf) + '.tbi' + "\""
-      clean_command = str(docker_command_run2) + 'rm -f ' + str(vep_vcf) + '* ' + str(vep_vcfanno_annotated_vcf) + ' ' + str(vep_vcfanno_vcf) + '* ' +  str(input_vcf_pcgr_ready) + "* "  + "\""
+      create_output_vcf_command3 = str(docker_command_run2) + 'mv ' + str(vep_vcfanno_annotated_pass_vcf) + ' ' + str(output_pass_vcf) + "\""
+      create_output_vcf_command4 = str(docker_command_run2) + 'mv ' + str(vep_vcfanno_annotated_pass_vcf) + '.tbi ' + str(output_pass_vcf) + '.tbi' + "\""
+      clean_command = str(docker_command_run2) + 'rm -f ' + str(vep_vcf) + '* ' + str(vep_vcfanno_annotated_vcf) + ' ' + str(vep_vcfanno_annotated_pass_vcf) + '* ' + str(vep_vcfanno_vcf) + '* ' +  str(input_vcf_pcgr_ready) + "* "  + "\""
       check_subprocess(create_output_vcf_command1)
       check_subprocess(create_output_vcf_command2)
+      check_subprocess(create_output_vcf_command3)
+      check_subprocess(create_output_vcf_command4)
       check_subprocess(clean_command)
+      #return
   
    print
    
    ## Generation of HTML reports for VEP/vcfanno-annotated VCF and copy number segment file
    logger = getlogger('pcgr-writer')
    logger.info("STEP 4: Generation of output files")
-   pcgr_report_command = str(docker_command_run1) + "/pcgr.R /workdir/output " + str(output_vcf) + " " + str(input_cna_docker) + " "  + str(sample_id)  + " " + str(input_conf_docker) + " " + str(version) + "\""
+   pcgr_report_command = str(docker_command_run1) + "/pcgr.R /workdir/output " + str(output_pass_vcf) + " " + str(input_cna_docker) + " "  + str(sample_id)  + " " + str(input_conf_docker) + " " + str(version) + "\""
    check_subprocess(pcgr_report_command)
    logger.info("Finished")
    
diff --git a/src/R/pcgrr/R/msi.R b/src/R/pcgrr/R/msi.R
index 6bf8aac5..ababd106 100644
--- a/src/R/pcgrr/R/msi.R
+++ b/src/R/pcgrr/R/msi.R
@@ -15,6 +15,7 @@ predict_msi_status <- function(vcf_data_df, simpleRepeats_gr, windowMasker_gr, m
   mutations_valid <- dplyr::select(mutations_valid, CHROM,POS,end,REF,ALT,CONSEQUENCE,SYMBOL,GENOMIC_CHANGE,VARIANT_CLASS,PROTEIN_DOMAIN,GENE_NAME,PROTEIN_CHANGE,PROTEIN_FEATURE,CANCER_MUTATION_HOTSPOT,OTHER_DISEASE_DOCM,OTHER_LITERATURE_DOCM,CLINVAR,TCGA_FREQUENCY,CANCER_ASSOCIATIONS,AF_TUMOR, DP_TUMOR,AF_NORMAL,DP_NORMAL,CALL_CONFIDENCE)
   mutations_valid$start_field <- mutations_valid$POS
   mutations_valid$end_field <- NA
+  mutations_valid <- dplyr::filter(mutations_valid, !is.na(VARIANT_CLASS))
   if(nrow(mutations_valid[mutations_valid$VARIANT_CLASS == 'deletion',]) > 0){
     mutations_valid[mutations_valid$VARIANT_CLASS == 'deletion',]$end_field <- mutations_valid[mutations_valid$VARIANT_CLASS == 'deletion',]$end
   }
diff --git a/src/R/pcgrr/R/utils.R b/src/R/pcgrr/R/utils.R
index 8dc43fee..59302673 100644
--- a/src/R/pcgrr/R/utils.R
+++ b/src/R/pcgrr/R/utils.R
@@ -307,74 +307,81 @@ generate_report <- function(project_directory, query_vcf, pcgr_data, sample_name
     }
   }
 
-  if(!is.null(pcgr_config) & query_vcf != 'None'){
-    if(pcgr_config$tumor_only$vcf_tumor_only == TRUE){
-      sample_calls_unfiltered <- pcgrr::get_calls(query_vcf, pcgr_data, sample_id = sample_name, tumor_dp_tag = pcgr_config$allelic_support$tumor_dp_tag , tumor_af_tag = pcgr_config$allelic_support$tumor_af_tag, normal_dp_tag =  pcgr_config$allelic_support$normal_dp_tag, normal_af_tag = pcgr_config$allelic_support$normal_af_tag, call_conf_tag = pcgr_config$allelic_support$call_conf_tag)
-      filtered_callset <- pcgrr::filter_no_control_calls(sample_calls_unfiltered, sample_name = sample_name, pcgr_config = pcgr_config)
-
-      ## Override MSI/mutational signature analysis when running tumor only (set to FALSE)
-      pcgr_config$msi$msi <- FALSE
-      pcgr_config$mutational_signatures$mutsignatures <- FALSE
-
-      report_data_unfiltered <- pcgrr::generate_report_data(sample_calls_unfiltered, pcgr_data = pcgr_data, sample_name = sample_name, minimum_n_signature_analysis = pcgr_config$mutational_signatures$mutsignatures_mutation_limit, signature_normalization_method = pcgr_config$mutational_signatures$mutsignatures_normalization, signatures_limit = pcgr_config$mutational_signatures$mutsignatures_signature_limit, predict_MSI = pcgr_config$msi$msi, identify_msigs = pcgr_config$mutational_signatures$mutsignatures, callset = 'unfiltered callset')
-      report_data <- pcgrr::generate_report_data(filtered_callset$sample_calls, pcgr_data = pcgr_data, sample_name = sample_name, minimum_n_signature_analysis = pcgr_config$mutational_signatures$mutsignatures_mutation_limit, signature_normalization_method = pcgr_config$mutational_signatures$mutsignatures_normalization, signatures_limit = pcgr_config$mutational_signatures$mutsignatures_signature_limit, predict_MSI = pcgr_config$msi$msi, identify_msigs = pcgr_config$mutational_signatures$mutsignatures, callset = 'germline-filtered callset')
-      report_data$sample_name <- sample_name
-      report_data$vcf_run_mode <- 'tumor-only'
-      report_data$pcgr_config <- pcgr_config
-      report_data$unfiltered_snv_indels_n <- nrow(sample_calls_unfiltered)
-      for(filter_db in c('onekg','gnomad','dbsnp','noncoding')){
-        n_remain <- paste0(filter_db,'_filter_n_remain')
-        report_data[[n_remain]] <- filtered_callset[[n_remain]]
-        if(report_data[[n_remain]] > 0 & report_data$unfiltered_snv_indels_n > 0){
-          frac_remain <- paste0(filter_db,'_filter_frac')
-          report_data[[frac_remain]] <- round(as.numeric((report_data[[n_remain]] / report_data$unfiltered_snv_indels_n) * 100), digits = 2)
-        }
-      }
-      if(!is.null(report_data_unfiltered$tsv_variants)){
-        write.table(report_data_unfiltered$tsv_variants,file=tier_tsv_unfiltered_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
-      if(!is.null(report_data$tsv_variants)){
-        write.table(report_data$tsv_variants,file=tier_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
-      if(!is.null(report_data$tsv_biomarkers)){
-        write.table(report_data$tsv_biomarkers,file=biomarker_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
+  if(query_vcf != 'None'){
+    if(!file.exists(query_vcf) | file.size(query_vcf) == 0){
+      rlogging::warning(paste0("File ",query_vcf," does not exist or has zero size"))
+    }
+    else{
+      if(!is.null(pcgr_config) & query_vcf != 'None'){
+        if(pcgr_config$tumor_only$vcf_tumor_only == TRUE){
+          sample_calls_unfiltered <- pcgrr::get_calls(query_vcf, pcgr_data, sample_id = sample_name, tumor_dp_tag = pcgr_config$allelic_support$tumor_dp_tag , tumor_af_tag = pcgr_config$allelic_support$tumor_af_tag, normal_dp_tag =  pcgr_config$allelic_support$normal_dp_tag, normal_af_tag = pcgr_config$allelic_support$normal_af_tag, call_conf_tag = pcgr_config$allelic_support$call_conf_tag)
+          filtered_callset <- pcgrr::filter_no_control_calls(sample_calls_unfiltered, sample_name = sample_name, pcgr_config = pcgr_config)
+
+          ## Override MSI/mutational signature analysis when running tumor only (set to FALSE)
+          pcgr_config$msi$msi <- FALSE
+          pcgr_config$mutational_signatures$mutsignatures <- FALSE
+
+          report_data_unfiltered <- pcgrr::generate_report_data(sample_calls_unfiltered, pcgr_data = pcgr_data, sample_name = sample_name, minimum_n_signature_analysis = pcgr_config$mutational_signatures$mutsignatures_mutation_limit, signature_normalization_method = pcgr_config$mutational_signatures$mutsignatures_normalization, signatures_limit = pcgr_config$mutational_signatures$mutsignatures_signature_limit, predict_MSI = pcgr_config$msi$msi, identify_msigs = pcgr_config$mutational_signatures$mutsignatures, callset = 'unfiltered callset')
+          report_data <- pcgrr::generate_report_data(filtered_callset$sample_calls, pcgr_data = pcgr_data, sample_name = sample_name, minimum_n_signature_analysis = pcgr_config$mutational_signatures$mutsignatures_mutation_limit, signature_normalization_method = pcgr_config$mutational_signatures$mutsignatures_normalization, signatures_limit = pcgr_config$mutational_signatures$mutsignatures_signature_limit, predict_MSI = pcgr_config$msi$msi, identify_msigs = pcgr_config$mutational_signatures$mutsignatures, callset = 'germline-filtered callset')
+          report_data$sample_name <- sample_name
+          report_data$vcf_run_mode <- 'tumor-only'
+          report_data$pcgr_config <- pcgr_config
+          report_data$unfiltered_snv_indels_n <- nrow(sample_calls_unfiltered)
+          for(filter_db in c('onekg','gnomad','dbsnp','noncoding')){
+            n_remain <- paste0(filter_db,'_filter_n_remain')
+            report_data[[n_remain]] <- filtered_callset[[n_remain]]
+            if(report_data[[n_remain]] > 0 & report_data$unfiltered_snv_indels_n > 0){
+              frac_remain <- paste0(filter_db,'_filter_frac')
+              report_data[[frac_remain]] <- round(as.numeric((report_data[[n_remain]] / report_data$unfiltered_snv_indels_n) * 100), digits = 2)
+            }
+          }
+          if(!is.null(report_data_unfiltered$tsv_variants)){
+            write.table(report_data_unfiltered$tsv_variants,file=tier_tsv_unfiltered_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
+          if(!is.null(report_data$tsv_variants)){
+            write.table(report_data$tsv_variants,file=tier_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
+          if(!is.null(report_data$tsv_biomarkers)){
+            write.table(report_data$tsv_biomarkers,file=biomarker_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
 
-      if(!is.null(report_data$signature_data)){
-        if(length(report_data$signature_data) > 1){
-          sample_mutational_signatures <- report_data$signature_data$signatures_cancertypes_aetiologies
-          sample_mutational_signatures$SampleID <- sample_name
-          sample_mutational_signatures <- dplyr::select(sample_mutational_signatures,-Comments)
-          write.table(sample_mutational_signatures,file=msig_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
-        }
-      }
-      if(!is.null(report_data$maf_df)){
-        write.table(report_data$maf_df,file=maf_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
-    }else{
-      sample_calls <- pcgrr::get_calls(query_vcf, pcgr_data, sample_id = sample_name, tumor_dp_tag = pcgr_config$allelic_support$tumor_dp_tag, tumor_af_tag = pcgr_config$allelic_support$tumor_af_tag, normal_dp_tag =  pcgr_config$allelic_support$normal_dp_tag, normal_af_tag = pcgr_config$allelic_support$normal_af_tag, call_conf_tag = pcgr_config$allelic_support$call_conf_tag)
-      report_data <- pcgrr::generate_report_data(sample_calls, pcgr_data = pcgr_data, sample_name = sample_name, minimum_n_signature_analysis = pcgr_config$mutational_signatures$mutsignatures_mutation_limit, signature_normalization_method = pcgr_config$mutational_signatures$mutsignatures_normalization, signatures_limit = pcgr_config$mutational_signatures$mutsignatures_signature_limit, predict_MSI = pcgr_config$msi$msi, identify_msigs = pcgr_config$mutational_signatures$mutsignatures)
-      report_data$sample_name <- sample_name
-      report_data$vcf_run_mode <- 'tumor vs. control'
-
-      if(!is.null(report_data$tsv_variants)){
-        write.table(report_data$tsv_variants,file=tier_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
-      if(!is.null(report_data$tsv_biomarkers)){
-        write.table(report_data$tsv_biomarkers,file=biomarker_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
+          if(!is.null(report_data$signature_data)){
+            if(length(report_data$signature_data) > 1){
+              sample_mutational_signatures <- report_data$signature_data$signatures_cancertypes_aetiologies
+              sample_mutational_signatures$SampleID <- sample_name
+              sample_mutational_signatures <- dplyr::select(sample_mutational_signatures,-Comments)
+              write.table(sample_mutational_signatures,file=msig_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+            }
+          }
+          if(!is.null(report_data$maf_df)){
+            write.table(report_data$maf_df,file=maf_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
+        }else{
+          sample_calls <- pcgrr::get_calls(query_vcf, pcgr_data, sample_id = sample_name, tumor_dp_tag = pcgr_config$allelic_support$tumor_dp_tag, tumor_af_tag = pcgr_config$allelic_support$tumor_af_tag, normal_dp_tag =  pcgr_config$allelic_support$normal_dp_tag, normal_af_tag = pcgr_config$allelic_support$normal_af_tag, call_conf_tag = pcgr_config$allelic_support$call_conf_tag)
+          report_data <- pcgrr::generate_report_data(sample_calls, pcgr_data = pcgr_data, sample_name = sample_name, minimum_n_signature_analysis = pcgr_config$mutational_signatures$mutsignatures_mutation_limit, signature_normalization_method = pcgr_config$mutational_signatures$mutsignatures_normalization, signatures_limit = pcgr_config$mutational_signatures$mutsignatures_signature_limit, predict_MSI = pcgr_config$msi$msi, identify_msigs = pcgr_config$mutational_signatures$mutsignatures)
+          report_data$sample_name <- sample_name
+          report_data$vcf_run_mode <- 'tumor vs. control'
+
+          if(!is.null(report_data$tsv_variants)){
+            write.table(report_data$tsv_variants,file=tier_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
+          if(!is.null(report_data$tsv_biomarkers)){
+            write.table(report_data$tsv_biomarkers,file=biomarker_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
 
-      if(!is.null(report_data$signature_data)){
-        if(length(report_data$signature_data) > 1){
-          sample_mutational_signatures <- report_data$signature_data$signatures_cancertypes_aetiologies
-          sample_mutational_signatures$SampleID <- sample_name
-          sample_mutational_signatures <- dplyr::select(sample_mutational_signatures,-Comments)
-          write.table(sample_mutational_signatures,file=msig_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          if(!is.null(report_data$signature_data)){
+            if(length(report_data$signature_data) > 1){
+              sample_mutational_signatures <- report_data$signature_data$signatures_cancertypes_aetiologies
+              sample_mutational_signatures$SampleID <- sample_name
+              sample_mutational_signatures <- dplyr::select(sample_mutational_signatures,-Comments)
+              write.table(sample_mutational_signatures,file=msig_tsv_fname, sep="\t",col.names = T,row.names = F,quote = F)
+            }
+          }
+          if(!is.null(report_data$maf_df)){
+            write.table(report_data$maf_df,file=maf_fname, sep="\t",col.names = T,row.names = F,quote = F)
+          }
         }
       }
-      if(!is.null(report_data$maf_df)){
-        write.table(report_data$maf_df,file=maf_fname, sep="\t",col.names = T,row.names = F,quote = F)
-      }
     }
   }
 
@@ -1203,20 +1210,35 @@ add_read_support <- function(vcf_data_df,tumor_dp_tag = '_na', tumor_af_tag = '_
   for(v in c('DP_TUMOR','AF_TUMOR','DP_NORMAL','AF_NORMAL')){
     vcf_data_df[v] <- NA
   }
-  if(tumor_dp_tag != '_na' & tumor_dp_tag %in% colnames(vcf_data_df)){
-    vcf_data_df[,'DP_TUMOR'] <- as.integer(vcf_data_df[,tumor_dp_tag])
+  if(tumor_dp_tag != '_na'){
+    tumor_dp_tag <- stringr::str_replace_all(tumor_dp_tag,"-",".")
+    if(tumor_dp_tag %in% colnames(vcf_data_df)){
+      vcf_data_df[,'DP_TUMOR'] <- as.integer(vcf_data_df[,tumor_dp_tag])
+    }
   }
-  if(tumor_af_tag != '_na' & tumor_af_tag %in% colnames(vcf_data_df)){
-    vcf_data_df[,'AF_TUMOR'] <- round(as.numeric(vcf_data_df[,tumor_af_tag]), digits=3)
+  if(tumor_af_tag != '_na'){
+    tumor_af_tag <- stringr::str_replace_all(tumor_af_tag,"-",".")
+    if(tumor_af_tag %in% colnames(vcf_data_df)){
+      vcf_data_df[,'AF_TUMOR'] <- as.integer(vcf_data_df[,tumor_af_tag])
+    }
   }
-  if(normal_dp_tag != '_na' & normal_dp_tag %in% colnames(vcf_data_df)){
-    vcf_data_df[,'DP_NORMAL'] <- as.integer(vcf_data_df[,normal_dp_tag])
+  if(normal_dp_tag != '_na'){
+    normal_dp_tag <- stringr::str_replace_all(normal_dp_tag,"-",".")
+    if(normal_dp_tag %in% colnames(vcf_data_df)){
+      vcf_data_df[,'DP_NORMAL'] <- as.integer(vcf_data_df[,normal_dp_tag])
+    }
   }
-  if(normal_af_tag != '_na' & normal_af_tag %in% colnames(vcf_data_df)){
-    vcf_data_df[,'AF_NORMAL'] <- round(as.numeric(vcf_data_df[,normal_af_tag]), digits=3)
+  if(normal_af_tag != '_na'){
+    normal_af_tag <- stringr::str_replace_all(normal_af_tag,"-",".")
+    if(normal_af_tag %in% colnames(vcf_data_df)){
+      vcf_data_df[,'AF_NORMAL'] <- as.integer(vcf_data_df[,normal_af_tag])
+    }
   }
-  if(call_conf_tag != '_na' & call_conf_tag %in% colnames(vcf_data_df)){
-    vcf_data_df[,'CALL_CONFIDENCE'] <- as.character(vcf_data_df[,call_conf_tag])
+  if(call_conf_tag != '_na'){
+    call_conf_tag <- stringr::str_replace_all(call_conf_tag,"-",".")
+    if(call_conf_tag %in% colnames(vcf_data_df)){
+      vcf_data_df[,'CALL_CONFIDENCE'] <- as.character(vcf_data_df[,call_conf_tag])
+    }
   }
   else{
     vcf_data_df$CALL_CONFIDENCE <- NA
diff --git a/src/pcgr.tgz b/src/pcgr.tgz
index 07d67a1c..3f2bb003 100644
Binary files a/src/pcgr.tgz and b/src/pcgr.tgz differ
diff --git a/src/pcgr/pcgr_msample_flatten.py b/src/pcgr/pcgr_msample_flatten.py
index 2502bd4d..0abfa773 100755
--- a/src/pcgr/pcgr_msample_flatten.py
+++ b/src/pcgr/pcgr_msample_flatten.py
@@ -70,7 +70,7 @@ def flatten_vcf(query_vcf, output_postfix):
             if line.startswith('#'):
                if printed == 0 and not line.startswith('##FORMAT'):
                   if line.startswith('#CHROM'):
-                     vcf_lines.append('#CHROM\tPOS\tID\tALT\tREF\tQUAL\tFILTER\tINFO')
+                     vcf_lines.append('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO')
                   else:
                      vcf_lines.append(line.rstrip())
             else:
diff --git a/src/pcgr/pcgr_summarise.py b/src/pcgr/pcgr_summarise.py
index a6d9a2ed..00fb5e58 100755
--- a/src/pcgr/pcgr_summarise.py
+++ b/src/pcgr/pcgr_summarise.py
@@ -108,7 +108,29 @@ def set_coding_change(rec):
             rec.INFO['CDS_CHANGE'] = key
 
    return
- 
+
+def write_pass_vcf(annotated_vcf):
+	
+	out_vcf = re.sub(r'\.annotated\.vcf\.gz$','.annotated.pass.vcf',annotated_vcf)
+	vcf = VCF(annotated_vcf)
+	w = Writer(out_vcf, vcf)
+
+	num_pass = 0
+	for rec in vcf:
+		if rec.FILTER is None:
+			w.write_record(rec)
+			num_pass += 1
+
+	if num_pass == 0:
+		logger.warning('There are zero variants with a \'PASS\' filter in the VCF file')
+	else:
+		os.system('bgzip -f ' + str(out_vcf))
+		os.system('tabix -f -p vcf ' + str(out_vcf) + '.gz')
+		
+	vcf.close()
+	w.close()
+			
+
 def extend_vcf_annotations(query_vcf, pcgr_directory):
    """
    Function that reads VEP/vcfanno-annotated VCF and extends the VCF INFO column with tags from
@@ -250,6 +272,9 @@ def extend_vcf_annotations(query_vcf, pcgr_directory):
    os.system('bgzip -f ' + str(out_vcf))
    os.system('tabix -f -p vcf ' + str(out_vcf) + '.gz')
 
+   annotated_vcf = out_vcf + '.gz'
+   write_pass_vcf(annotated_vcf)
+
 if __name__=="__main__": __main__()
 
 
diff --git a/src/pcgr/pcgr_validate_input.py b/src/pcgr/pcgr_validate_input.py
index f1112650..3506fb6b 100755
--- a/src/pcgr/pcgr_validate_input.py
+++ b/src/pcgr/pcgr_validate_input.py
@@ -85,10 +85,10 @@ def is_valid_vcf(input_vcf, logger):
       for line in f:
          if not re.search(r' \(warning\)$|^Reading from ',line.rstrip()): ## ignore warnings
             if line.startswith('Line '):
-               validation_results['error_messages'].append(line.rstrip())
-            if line.endswith('the input file is valid'): ## valid VCF
+               validation_results['error_messages'].append('ERROR: ' + line.rstrip())
+            if 'the input file is valid' in line.rstrip(): ## valid VCF
                validation_results['validation_status'] = 1
-            if line.endswith('the input file is not valid'):  ## non-valid VCF
+            if 'the input file is not valid' in line.rstrip():  ## non-valid VCF
                validation_results['validation_status'] = 0
       f.close()
       os.system('rm -f ' + str(vcf_validation_output_file))
@@ -96,13 +96,13 @@ def is_valid_vcf(input_vcf, logger):
       err_msg = str(vcf_validation_output_file) + ' does not exist'
       return pcgr_error_message(err_msg, logger)
    
-   if validation_results['validation_status'] == 1:
+   if validation_results['validation_status'] == 0:
       error_string_42 = '\n'.join(validation_results['error_messages'])
-      validation_status = 'VCF file is NOT valid according to v4.2 specification'
+      validation_status = 'According to the VCF specification, the VCF file is NOT valid'
       err_msg = validation_status + ':\n' + str(error_string_42)
       return pcgr_error_message(err_msg, logger)
    else:
-      validation_status = 'VCF file ' + str(input_vcf) + ' is valid according to v4.2 specification'
+      validation_status = 'According to the VCF specification, the VCF file ' + str(input_vcf) + ' is valid'
       logger.info(validation_status)
    return 0