diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..c787740
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,5 @@
+{
+    "python.analysis.extraPaths": [
+        "./vcf_ops/src"
+    ]
+}
\ No newline at end of file
diff --git a/src/main.py b/main.py
similarity index 100%
rename from src/main.py
rename to main.py
diff --git a/oncoliner_assesment/.gitignore b/oncoliner_assesment/.gitignore
new file mode 100644
index 0000000..b6e4761
--- /dev/null
+++ b/oncoliner_assesment/.gitignore
@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/oncoliner_assesment/CODE_OF_CONDUCT.md b/oncoliner_assesment/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..f92a867
--- /dev/null
+++ b/oncoliner_assesment/CODE_OF_CONDUCT.md
@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+rodrigo.martin@bsc.es.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.
diff --git a/oncoliner_assesment/README.md b/oncoliner_assesment/README.md
new file mode 100644
index 0000000..cc8d800
--- /dev/null
+++ b/oncoliner_assesment/README.md
@@ -0,0 +1,149 @@
+# Oncoliner: Assesment module<!-- omit in toc -->
+
+Evaluator of variants (SNVs, indels and SVs) from test VCFs against truth VCFs. It is provided as a standalone command line tool to allow for the comparison of a series of (VCF/BCF/VCF.GZ) files generated by any variant callers against a series of (VCF/BCF/VCF.GZ) truth files. If provided in the truth files the module will also provide information about the genes affected by the variants. Check the [Functional analysis](#functional-analysis) section for more information.
+
+It uses [VariantExtractor](https://github.com/EUCANCan/variant-extractor) under-the-hood for extracting SNVs, indels and structural variants (SVs) from VCF files in a deterministic and standard way. Different variant callers may provice slightly different formatted VCF files, that is why VariantExtractor adds a preprocessing layer to homogenize the variants extracted from the file. For more information about the preprocessing process check [VariantExtractor's repository](https://github.com/EUCANCan/variant-extractor#table-of-contents).
+
+It is written in Python 3 (**requires Python version 3.6 or higher**).
+
+## Table of contents<!-- omit in toc -->
+- [Dependencies](#dependencies)
+- [Functional analysis](#functional-analysis)
+- [Usage](#usage)
+  - [`assesment_main.py`](#assesment_mainpy)
+  - [`assesment_bulk.py`](#assesment_bulkpy)
+    - [Configuration file](#configuration-file)
+
+## Dependencies
+Oncoliner's assesment module makes use of the following Python modules:
+* [`pandas`](https://pandas.pydata.org/)
+* [`pysam`](https://github.com/pysam-developers/pysam)
+
+You may install them using pip:
+```
+pip3 install pandas pysam
+```
+
+## Functional analysis
+
+The module will try to obtain the genes affected by the variants from the `INFO` field in the truth files. **WARNING: Oncoliner does not compute genes linked to false positives.** Oncoliner's assesment module is compatible with the following functional analysis tools annotations:
+* Oncoliner.
+* [**VEP**](https://www.ensembl.org/info/docs/tools/vep/index.html): Variant Effect Predictor from Ensembl.
+
+
+## Usage
+
+**WARNING**: It is recommended to normalize indels and SNVs before executing the assesment. For this purpose, we recommend using pre.py from [Illumina's Haplotype Comparison Tools (hap.py)](https://github.com/Illumina/hap.py). We provide an standalone and containerized **[EUCANCan's pre.py wrapper](https://github.com/EUCANCan/prepy-wrapper)** for this purpose.
+
+The main executable code is in the [`src/`](/src/) folder. There are two executable files: [`assesment_main.py`](#assesment_mainpy) and [`assesment_bulk.py`](/src/assesment_bulk.py). The first one is the main executable file and the second one is a wrapper for the first one that allows to execute the assesment in a bulk way.
+
+There is an example of usage in the [`examples/`](/examples/) folder for each executable file: [`examples/test_main.sh`](/examples/test_main.sh) and [`examples/test_bulk.sh`](/examples/test_bulk.sh).
+
+
+### `assesment_main.py`
+
+Main executable file. It allows to compare a series of (VCF/BCF/VCF.GZ) files generated by any variant callers against a series of (VCF/BCF/VCF.GZ) truth files for **only one sample**. It is provided as a standalone command line tool. Example of usage:
+
+```
+python3 -O src/assesment_main.py -t truth.vcf -v test.vcf -o output_
+```
+
+Check the example of usage in [`examples/test_main.sh`](/examples/test_main.sh) for more information.
+
+#### Interface<!-- omit in toc -->
+```
+usage: Oncoliner Assesment [-h] -t TRUTHS [TRUTHS ...] -v TESTS [TESTS ...] -o OUTPUT_PREFIX -f FASTA_REF [-it INDEL_THRESHOLD] [-wr WINDOW_RADIUS] [--sv-size-bins SV_SIZE_BINS [SV_SIZE_BINS ...]]
+                           [--contigs CONTIGS [CONTIGS ...]] [--keep-intermediates] [--no-gzip]
+
+options:
+  -h, --help            show this help message and exit
+  -t TRUTHS [TRUTHS ...], --truths TRUTHS [TRUTHS ...]
+                        Path to the VCF truth files
+  -v TESTS [TESTS ...], --tests TESTS [TESTS ...]
+                        Path to the VCF test files
+  -o OUTPUT_PREFIX, --output_prefix OUTPUT_PREFIX
+                        Prefix path for the output_prefix VCF files
+  -f FASTA_REF, --fasta-ref FASTA_REF
+                        Path to reference FASTA file
+  -it INDEL_THRESHOLD, --indel-threshold INDEL_THRESHOLD
+                        Indel threshold, inclusive (default=100)
+  -wr WINDOW_RADIUS, --window-radius WINDOW_RADIUS
+                        Window ratio (default=100)
+  --sv-size-bins SV_SIZE_BINS [SV_SIZE_BINS ...]
+                        SV size bins for the output_prefix metrics (default=[500])
+  --contigs CONTIGS [CONTIGS ...]
+                        Contigs to process (default=['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y'])
+  --keep-intermediates  Keep intermediate CSV/VCF files from input VCF files
+  --no-gzip             Do not gzip output_prefix VCF files
+```
+
+#### Output<!-- omit in toc -->
+
+`assesment_main.py` outputs a series of files:
+ * `{OUTPUT_PREFIX}tp.[snv|indel|sv].vcf.gz`: VCF files with the true positives (TP) variants. One file per variant type (SNV, indel and SV).
+ * `{OUTPUT_PREFIX}fp.[snv|indel|sv].vcf.gz`: VCF files with the false positives (FP) variants. One file per variant type (SNV, indel and SV).
+ * `{OUTPUT_PREFIX}fn.[snv|indel|sv].vcf.gz`: VCF files with the false negatives (FN) variants. One file per variant type (SNV, indel and SV).
+ * `{OUTPUT_PREFIX}metrics.csv`: CSV file containing the metrics for the comparison of the test and truth VCF files. It contains the following columns:
+   * `variant_type`: variant type (SNV, indel or SV), as outputted by [VariantExtractor](https://github.com/EUCANCan/variant-extractor).
+   * `variant_size`: range of variant sizes for that particular row.
+   * `window_radius`: window radius used for the assessment.
+   * `recall`: Recall. TP / (TP + FN).
+   * `precision`: Precision. TP / (TP + FP).
+   * `f1_score`: F1 score. 2 * (precision * recall) / (precision + recall).
+   * `tp`: Number of true positives.
+   * `fp`: Number of false positives.
+   * `fn`: Number of false negatives.
+   * `protein_affected_genes_count`: Number of genes affected by the variants.
+   * `protein_affected_driver_genes_count`: Number of cancer driver genes affected by the variants.
+   * `protein_affected_genes`: List of genes affected by the variants (separated by `;`).
+   * `protein_affected_driver_genes`: List of cancer driver genes affected by the variants (separated by `;`).
+
+### `assesment_bulk.py`
+
+Wrapper for `assesment_main.py`. It allows to compare a series of (VCF/BCF/VCF.GZ) files generated by any variant callers against a series of (VCF/BCF/VCF.GZ) truth files for **multiple samples**. It takes advantage of multiple processors. It is provided as a standalone command line tool. Example of usage:
+
+```
+python3 -O src/assesment_main.py -c config.tsv -o output_
+```
+
+Check the example of usage in [`examples/test_bulk.sh`](/examples/test_bulk.sh) for more information.
+
+#### Configuration file
+
+The configuration file is a TSV file with the following columns:
+* `sample_name`: sample name.
+* `sample_type`: sample types (recall or precision), separated by `,`.
+* `reference_fasta_path`: path to the reference FASTA file.
+* `truth_vcf_paths`: path(s) to the truth VCF files, separated by `,`. They can also be wildcard paths (e.g. `truths/*.vcf.gz`).
+* `test_vcf_paths`: path(s) to the test VCF files, separated by `,`. They can also be wildcard paths (e.g. `tests/*.vcf.gz`).
+
+#### Interface<!-- omit in toc -->
+```
+usage: Oncoliner Assesment Bulk [-h] -c CONFIG_FILE -o OUTPUT_FOLDER [-it INDEL_THRESHOLD] [-wr WINDOW_RADIUS] [--sv-size-bins SV_SIZE_BINS [SV_SIZE_BINS ...]] [--contigs CONTIGS [CONTIGS ...]]
+                                [--keep-intermediates] [--no-gzip] [-p MAX_PROCESSES]
+
+options:
+  -h, --help            show this help message and exit
+  -c CONFIG_FILE, --config-file CONFIG_FILE
+                        Path to the config TSV file
+  -o OUTPUT_FOLDER, --output-folder OUTPUT_FOLDER
+                        Path to the output folder
+  -it INDEL_THRESHOLD, --indel-threshold INDEL_THRESHOLD
+                        Indel threshold, inclusive (default=100)
+  -wr WINDOW_RADIUS, --window-radius WINDOW_RADIUS
+                        Window radius (default=100)
+  --sv-size-bins SV_SIZE_BINS [SV_SIZE_BINS ...]
+                        SV size bins for the output_prefix metrics (default=[500])
+  --contigs CONTIGS [CONTIGS ...]
+                        Contigs to process (default=['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y'])
+  --keep-intermediates  Keep intermediate CSV/VCF files from input VCF files
+  --no-gzip             Do not gzip output_prefix VCF files
+  -p MAX_PROCESSES, --max-processes MAX_PROCESSES
+                        Maximum number of processes to use (defaults to 1)
+```
+
+#### Output<!-- omit in toc -->
+
+`assesment_bulk.py` outputs the same files as `assesment_main.py` for each sample. The output files for each sample are stored in the `OUTPUT_FOLDER/samples` folder in a subfolder named after the sample name.
+
+`assesment_bulk.py` also outputs a `aggregated_metrics.csv` file, which aggregates the metrics for all the samples. It contains the same columns as `assesment_main.py`'s `metrics.csv` file. Recall related metrics are calculated using the recall samples and precision related metrics are calculated using the precision samples (as described in the configuration file).
diff --git a/oncoliner_assesment/examples/.gitignore b/oncoliner_assesment/examples/.gitignore
new file mode 100644
index 0000000..c70cd8e
--- /dev/null
+++ b/oncoliner_assesment/examples/.gitignore
@@ -0,0 +1 @@
+private_data
diff --git a/oncoliner_assesment/examples/fake_ref.fa b/oncoliner_assesment/examples/fake_ref.fa
new file mode 100644
index 0000000..b01bd36
--- /dev/null
+++ b/oncoliner_assesment/examples/fake_ref.fa
@@ -0,0 +1,60 @@
+>1
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT
+>2
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
+AGTGTGTTGGGGGGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTAGTAGTAGAT
\ No newline at end of file
diff --git a/oncoliner_assesment/examples/fake_ref.fa.fai b/oncoliner_assesment/examples/fake_ref.fa.fai
new file mode 100644
index 0000000..6eefdb0
--- /dev/null
+++ b/oncoliner_assesment/examples/fake_ref.fa.fai
@@ -0,0 +1,2 @@
+1	1650	3	55	56
+2	1540	1686	55	56
diff --git a/oncoliner_assesment/examples/input_test/test/sample_1/test_small.vcf b/oncoliner_assesment/examples/input_test/test/sample_1/test_small.vcf
new file mode 100644
index 0000000..f2c8ad2
--- /dev/null
+++ b/oncoliner_assesment/examples/input_test/test/sample_1/test_small.vcf
@@ -0,0 +1,193 @@
+##fileformat=VCFv4.2
+##FILTER=<ID=PASS,Description="All filters passed">
+##FILTER=<ID=FAIL,Description="Fail the site if all alleles fail but for different reasons.">
+##FILTER=<ID=base_qual,Description="alt median base quality">
+##FILTER=<ID=clustered_events,Description="Clustered events observed in the tumor">
+##FILTER=<ID=contamination,Description="contamination">
+##FILTER=<ID=duplicate,Description="evidence for alt allele is overrepresented by apparent duplicates">
+##FILTER=<ID=fragment,Description="abs(ref - alt) median fragment length">
+##FILTER=<ID=germline,Description="Evidence indicates this site is germline, not somatic">
+##FILTER=<ID=haplotype,Description="Variant near filtered variant on same haplotype.">
+##FILTER=<ID=low_allele_frac,Description="Allele fraction is below specified threshold">
+##FILTER=<ID=map_qual,Description="ref - alt median mapping quality">
+##FILTER=<ID=multiallelic,Description="Site filtered because too many alt alleles pass tumor LOD">
+##FILTER=<ID=n_ratio,Description="Ratio of N to alt exceeds specified ratio">
+##FILTER=<ID=normal_artifact,Description="artifact_in_normal">
+##FILTER=<ID=orientation,Description="orientation bias detected by the orientation bias mixture model">
+##FILTER=<ID=panel_of_normals,Description="Blacklisted site in panel of normals">
+##FILTER=<ID=position,Description="median distance of alt variants from end of reads">
+##FILTER=<ID=possible_numt,Description="Allele depth is below expected coverage of NuMT in autosome">
+##FILTER=<ID=slippage,Description="Site filtered due to contraction of short tandem repeat region">
+##FILTER=<ID=strand_bias,Description="Evidence for alt allele comes from one read direction only">
+##FILTER=<ID=strict_strand,Description="Evidence for alt allele is not represented in both directions">
+##FILTER=<ID=weak_evidence,Description="Mutation does not meet likelihood threshold">
+##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
+##FORMAT=<ID=AF,Number=A,Type=Float,Description="Allele fractions of alternate alleles in the tumor">
+##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
+##FORMAT=<ID=F1R2,Number=R,Type=Integer,Description="Count of reads in F1R2 pair orientation supporting each allele">
+##FORMAT=<ID=F2R1,Number=R,Type=Integer,Description="Count of reads in F2R1 pair orientation supporting each allele">
+##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
+##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
+##FORMAT=<ID=PGT,Number=1,Type=String,Description="Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles">
+##FORMAT=<ID=PID,Number=1,Type=String,Description="Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group">
+##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
+##FORMAT=<ID=PS,Number=1,Type=Integer,Description="Phasing set (typically the position of the first variant in the set)">
+##FORMAT=<ID=SB,Number=4,Type=Integer,Description="Per-sample component statistics which comprise the Fisher's Exact Test to detect strand bias.">
+##GATKCommandLine=<ID=FilterMutectCalls,CommandLine="FilterMutectCalls --output COLO829T_vs_COLO829N_Mutect2_filtered.vcf.gz --stats COLO829T_vs_COLO829N.vcf.gz.stats --contamination-table COLO829T_contamination.table.tsv --variant COLO829T_vs_COLO829N_Mutect2_unfiltered.vcf.gz --reference hg19.fa --threshold-strategy OPTIMAL_F_SCORE --f-score-beta 1.0 --false-discovery-rate 0.05 --initial-threshold 0.1 --mitochondria-mode false --max-events-in-region 2 --max-alt-allele-count 1 --unique-alt-read-count 0 --min-median-mapping-quality 30 --min-median-base-quality 20 --max-median-fragment-length-difference 10000 --min-median-read-position 1 --max-n-ratio Infinity --min-reads-per-strand 0 --min-allele-fraction 0.0 --contamination-estimate 0.0 --log-snv-prior -13.815510557964275 --log-indel-prior -16.11809565095832 --log-artifact-prior -2.302585092994046 --normal-p-value-threshold 0.001 --min-slippage-length 8 --pcr-slippage-rate 0.1 --distance-on-haplotype 100 --long-indel-length 5 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays  --disable-tool-default-read-filters false",Version="4.1.9.0",Date="8 juillet 2021 16:09:27 CEST">
+##GATKCommandLine=<ID=Mutect2,CommandLine="Mutect2 --tumor-sample COLO829T --normal-sample COLO829N --germline-resource /mnt/beegfs/home/tgutman/eucancan/colo829/work/c0/6c82bf4687b64df35ad12193f8e3cd/af-only-gnomad_modified.raw.sites.vcf.gz --dont-use-soft-clipped-bases true --min-base-quality-score 13 --output 10_1-135534747_COLO829T_vs_COLO829N.vcf --intervals 10_1-135534747.bed --input COLO829T_recal.bam --input COLO829N_recal.bam --reference hg19.fa --minimum-mapping-quality 20 --f1r2-median-mq 50 --f1r2-min-bq 20 --f1r2-max-depth 200 --genotype-pon-sites false --genotype-germline-sites false --af-of-alleles-not-in-resource -1.0 --mitochondria-mode false --tumor-lod-to-emit 3.0 --initial-tumor-lod 2.0 --pcr-snv-qual 40 --pcr-indel-qual 40 --max-population-af 0.01 --downsampling-stride 1 --callable-depth 10 --max-suspicious-reads-per-alignment-start 0 --normal-lod 2.2 --ignore-itr-artifacts false --gvcf-lod-band -2.5 --gvcf-lod-band -2.0 --gvcf-lod-band -1.5 --gvcf-lod-band -1.0 --gvcf-lod-band -0.5 --gvcf-lod-band 0.0 --gvcf-lod-band 0.5 --gvcf-lod-band 1.0 --minimum-allele-fraction 0.0 --independent-mates false --disable-adaptive-pruning false --kmer-size 10 --kmer-size 25 --dont-increase-kmer-sizes-for-cycles false --allow-non-unique-kmers-in-ref false --num-pruning-samples 1 --min-dangling-branch-length 4 --recover-all-dangling-branches false --max-num-haplotypes-in-population 128 --min-pruning 2 --adaptive-pruning-initial-error-rate 0.001 --pruning-lod-threshold 2.302585092994046 --pruning-seeding-lod-threshold 9.210340371976184 --max-unpruned-variants 100 --linked-de-bruijn-graph false --disable-artificial-haplotype-recovery false --debug-assembly false --debug-graph-transformations false --capture-assembly-failure-bam false --error-correction-log-odds -Infinity --error-correct-reads false --kmer-length-for-read-error-correction 25 --min-observations-for-kmer-to-be-solid 20 --base-quality-score-threshold 18 --pair-hmm-gap-continuation-penalty 10 --pair-hmm-implementation FASTEST_AVAILABLE --pcr-indel-model CONSERVATIVE --phred-scaled-global-read-mismapping-rate 45 --native-pair-hmm-threads 4 --native-pair-hmm-use-double-precision false --bam-writer-type CALLED_HAPLOTYPES --smith-waterman JAVA --emit-ref-confidence NONE --max-mnp-distance 1 --force-call-filtered-alleles false --allele-informative-reads-overlap-margin 2 --min-assembly-region-size 50 --max-assembly-region-size 300 --active-probability-threshold 0.002 --max-prob-propagation-distance 50 --force-active false --assembly-region-padding 100 --padding-around-indels 75 --padding-around-snps 20 --padding-around-strs 75 --max-reads-per-alignment-start 50 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays --disable-tool-default-read-filters false --max-read-length 2147483647 --min-read-length 30 --disable-tool-default-annotations false --enable-all-annotations false",Version="4.1.9.0",Date="8 juillet 2021 12:41:08 CEST">
+##INFO=<ID=AS_FilterStatus,Number=A,Type=String,Description="Filter status for each allele, as assessed by ApplyRecalibration. Note that the VCF filter field will reflect the most lenient/sensitive status across all alleles.">
+##INFO=<ID=AS_SB_TABLE,Number=1,Type=String,Description="Allele-specific forward/reverse read counts for strand bias tests. Includes the reference and alleles separated by |.">
+##INFO=<ID=AS_UNIQ_ALT_READ_COUNT,Number=A,Type=Integer,Description="Number of reads with unique start and mate end positions for each alt at a variant site">
+##INFO=<ID=CONTQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to contamination">
+##INFO=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth; some reads may have been filtered">
+##INFO=<ID=ECNT,Number=1,Type=Integer,Description="Number of events in this haplotype">
+##INFO=<ID=GERMQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not germline variants">
+##INFO=<ID=MBQ,Number=R,Type=Integer,Description="median base quality">
+##INFO=<ID=MFRL,Number=R,Type=Integer,Description="median fragment length">
+##INFO=<ID=MMQ,Number=R,Type=Integer,Description="median mapping quality">
+##INFO=<ID=MPOS,Number=A,Type=Integer,Description="median distance from end of read">
+##INFO=<ID=NALOD,Number=A,Type=Float,Description="Negative log 10 odds of artifact in normal with same allele fraction as tumor">
+##INFO=<ID=NCount,Number=1,Type=Integer,Description="Count of N bases in the pileup">
+##INFO=<ID=NLOD,Number=A,Type=Float,Description="Normal log 10 likelihood ratio of diploid het or hom alt genotypes">
+##INFO=<ID=OCM,Number=1,Type=Integer,Description="Number of alt reads whose original alignment doesn't match the current contig.">
+##INFO=<ID=PON,Number=0,Type=Flag,Description="site found in panel of normals">
+##INFO=<ID=POPAF,Number=A,Type=Float,Description="negative log 10 population allele frequencies of alt alleles">
+##INFO=<ID=ROQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to read orientation artifact">
+##INFO=<ID=RPA,Number=R,Type=Integer,Description="Number of times tandem repeat unit is repeated, for each allele (including reference)">
+##INFO=<ID=RU,Number=1,Type=String,Description="Tandem repeat unit (bases)">
+##INFO=<ID=SEQQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not sequencing errors">
+##INFO=<ID=STR,Number=0,Type=Flag,Description="Variant is a short tandem repeat">
+##INFO=<ID=STRANDQ,Number=1,Type=Integer,Description="Phred-scaled quality of strand bias artifact">
+##INFO=<ID=STRQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles in STRs are not polymerase slippage errors">
+##INFO=<ID=TLOD,Number=A,Type=Float,Description="Log 10 likelihood ratio score of variant existing versus not existing">
+##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
+##MutectVersion=2.2
+##contig=<ID=1,length=249250621>
+##contig=<ID=2,length=243199373>
+##contig=<ID=3,length=198022430>
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+##contig=<ID=Un_gl000249,length=38502>
+##contig=<ID=1_gl000191_random,length=106433>
+##contig=<ID=1_gl000192_random,length=547496>
+##contig=<ID=4_ctg9_hap1,length=590426>
+##contig=<ID=4_gl000193_random,length=189789>
+##contig=<ID=4_gl000194_random,length=191469>
+##contig=<ID=6_apd_hap1,length=4622290>
+##contig=<ID=6_cox_hap2,length=4795371>
+##contig=<ID=6_dbb_hap3,length=4610396>
+##contig=<ID=6_mann_hap4,length=4683263>
+##contig=<ID=6_mcf_hap5,length=4833398>
+##contig=<ID=6_qbl_hap6,length=4611984>
+##contig=<ID=6_ssto_hap7,length=4928567>
+##contig=<ID=7_gl000195_random,length=182896>
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+##contig=<ID=9_gl000201_random,length=36148>
+##contig=<ID=11_gl000202_random,length=40103>
+##contig=<ID=17_ctg5_hap1,length=1680828>
+##contig=<ID=17_gl000203_random,length=37498>
+##contig=<ID=17_gl000204_random,length=81310>
+##contig=<ID=17_gl000205_random,length=174588>
+##contig=<ID=17_gl000206_random,length=41001>
+##contig=<ID=18_gl000207_random,length=4262>
+##contig=<ID=19_gl000208_random,length=92689>
+##contig=<ID=19_gl000209_random,length=159169>
+##contig=<ID=21_gl000210_random,length=27682>
+##contig=<ID=X,length=155270560>
+##contig=<ID=Y,length=59373566>
+##contig=<ID=M,length=16571>
+##filtering_status=These calls have been filtered by FilterMutectCalls to label false positives with a list of failed filters and true positives with PASS.
+##normal_sample=COLO829N
+##source=FilterMutectCalls
+##source=Mutect2
+##tumor_sample=COLO829T
+##bcftools_normVersion=1.10.2+htslib-1.10.2
+##bcftools_normCommand=norm -Oz -m -both -f hg19.fa --threads 8 -o COLO829T_vs_COLO829N_Mutect2_filtered_pass_norm.vcf.gz COLO829T_vs_COLO829N_Mutect2_filtered_pass.vcf.gz; Date=Thu Jul  8 16:14:34 2021
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	COLO829N	COLO829T
+1	1	.	T	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	2	.	T	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	74	.	T	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	74	.	T	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	63	.	A	C	.	PASS	TEST_VARIANT;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	31	.	A	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	31	.	A	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	1	dup_large_indel	A	AGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTTAGTCAAAAAAAAAAAAAAAAAAAAAAAAAAGTTTTTTTTTTTTTTTTTGTTTTTT	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	1	del_base	AGTCAA	A	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	1	del_shorthand	A	<DEL>	.	PASS	END=6;TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	1	del_bracket	A	A[1:6[	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	4	dup_base	C	CAGT	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	1	dup_shorthand	A	<DUP>	.	PASS	END=4;TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	1	dup_bracket	A	]1:4]A	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	2	inv_bracket_1	A	A]1:5]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	7	inv_bracket_2	A	[1:9[A	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	100	.	T	T]1:200]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	90	.	T	T]1:700]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	90	.	T	T]1:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	90	.	T	T]2:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	85	.	T	T]2:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	87	.	T	T]2:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	199	.	T	T]1:400]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	200	.	T	T]2:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	230	.	T	T]2:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	250	.	T	T]2:300]	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	2102	.	T	T]2:2200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	2111	.	T	T]2:2200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
diff --git a/oncoliner_assesment/examples/input_test/test/sample_2/test_small_2.vcf b/oncoliner_assesment/examples/input_test/test/sample_2/test_small_2.vcf
new file mode 100644
index 0000000..39981c0
--- /dev/null
+++ b/oncoliner_assesment/examples/input_test/test/sample_2/test_small_2.vcf
@@ -0,0 +1,166 @@
+##fileformat=VCFv4.2
+##FILTER=<ID=PASS,Description="All filters passed">
+##FILTER=<ID=FAIL,Description="Fail the site if all alleles fail but for different reasons.">
+##FILTER=<ID=base_qual,Description="alt median base quality">
+##FILTER=<ID=clustered_events,Description="Clustered events observed in the tumor">
+##FILTER=<ID=contamination,Description="contamination">
+##FILTER=<ID=duplicate,Description="evidence for alt allele is overrepresented by apparent duplicates">
+##FILTER=<ID=fragment,Description="abs(ref - alt) median fragment length">
+##FILTER=<ID=germline,Description="Evidence indicates this site is germline, not somatic">
+##FILTER=<ID=haplotype,Description="Variant near filtered variant on same haplotype.">
+##FILTER=<ID=low_allele_frac,Description="Allele fraction is below specified threshold">
+##FILTER=<ID=map_qual,Description="ref - alt median mapping quality">
+##FILTER=<ID=multiallelic,Description="Site filtered because too many alt alleles pass tumor LOD">
+##FILTER=<ID=n_ratio,Description="Ratio of N to alt exceeds specified ratio">
+##FILTER=<ID=normal_artifact,Description="artifact_in_normal">
+##FILTER=<ID=orientation,Description="orientation bias detected by the orientation bias mixture model">
+##FILTER=<ID=panel_of_normals,Description="Blacklisted site in panel of normals">
+##FILTER=<ID=position,Description="median distance of alt variants from end of reads">
+##FILTER=<ID=possible_numt,Description="Allele depth is below expected coverage of NuMT in autosome">
+##FILTER=<ID=slippage,Description="Site filtered due to contraction of short tandem repeat region">
+##FILTER=<ID=strand_bias,Description="Evidence for alt allele comes from one read direction only">
+##FILTER=<ID=strict_strand,Description="Evidence for alt allele is not represented in both directions">
+##FILTER=<ID=weak_evidence,Description="Mutation does not meet likelihood threshold">
+##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
+##FORMAT=<ID=AF,Number=A,Type=Float,Description="Allele fractions of alternate alleles in the tumor">
+##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
+##FORMAT=<ID=F1R2,Number=R,Type=Integer,Description="Count of reads in F1R2 pair orientation supporting each allele">
+##FORMAT=<ID=F2R1,Number=R,Type=Integer,Description="Count of reads in F2R1 pair orientation supporting each allele">
+##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
+##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
+##FORMAT=<ID=PGT,Number=1,Type=String,Description="Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles">
+##FORMAT=<ID=PID,Number=1,Type=String,Description="Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group">
+##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
+##FORMAT=<ID=PS,Number=1,Type=Integer,Description="Phasing set (typically the position of the first variant in the set)">
+##FORMAT=<ID=SB,Number=4,Type=Integer,Description="Per-sample component statistics which comprise the Fisher's Exact Test to detect strand bias.">
+##GATKCommandLine=<ID=FilterMutectCalls,CommandLine="FilterMutectCalls --output COLO829T_vs_COLO829N_Mutect2_filtered.vcf.gz --stats COLO829T_vs_COLO829N.vcf.gz.stats --contamination-table COLO829T_contamination.table.tsv --variant COLO829T_vs_COLO829N_Mutect2_unfiltered.vcf.gz --reference hg19.fa --threshold-strategy OPTIMAL_F_SCORE --f-score-beta 1.0 --false-discovery-rate 0.05 --initial-threshold 0.1 --mitochondria-mode false --max-events-in-region 2 --max-alt-allele-count 1 --unique-alt-read-count 0 --min-median-mapping-quality 30 --min-median-base-quality 20 --max-median-fragment-length-difference 10000 --min-median-read-position 1 --max-n-ratio Infinity --min-reads-per-strand 0 --min-allele-fraction 0.0 --contamination-estimate 0.0 --log-snv-prior -13.815510557964275 --log-indel-prior -16.11809565095832 --log-artifact-prior -2.302585092994046 --normal-p-value-threshold 0.001 --min-slippage-length 8 --pcr-slippage-rate 0.1 --distance-on-haplotype 100 --long-indel-length 5 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays  --disable-tool-default-read-filters false",Version="4.1.9.0",Date="8 juillet 2021 16:09:27 CEST">
+##GATKCommandLine=<ID=Mutect2,CommandLine="Mutect2 --tumor-sample COLO829T --normal-sample COLO829N --germline-resource /mnt/beegfs/home/tgutman/eucancan/colo829/work/c0/6c82bf4687b64df35ad12193f8e3cd/af-only-gnomad_modified.raw.sites.vcf.gz --dont-use-soft-clipped-bases true --min-base-quality-score 13 --output 10_1-135534747_COLO829T_vs_COLO829N.vcf --intervals 10_1-135534747.bed --input COLO829T_recal.bam --input COLO829N_recal.bam --reference hg19.fa --minimum-mapping-quality 20 --f1r2-median-mq 50 --f1r2-min-bq 20 --f1r2-max-depth 200 --genotype-pon-sites false --genotype-germline-sites false --af-of-alleles-not-in-resource -1.0 --mitochondria-mode false --tumor-lod-to-emit 3.0 --initial-tumor-lod 2.0 --pcr-snv-qual 40 --pcr-indel-qual 40 --max-population-af 0.01 --downsampling-stride 1 --callable-depth 10 --max-suspicious-reads-per-alignment-start 0 --normal-lod 2.2 --ignore-itr-artifacts false --gvcf-lod-band -2.5 --gvcf-lod-band -2.0 --gvcf-lod-band -1.5 --gvcf-lod-band -1.0 --gvcf-lod-band -0.5 --gvcf-lod-band 0.0 --gvcf-lod-band 0.5 --gvcf-lod-band 1.0 --minimum-allele-fraction 0.0 --independent-mates false --disable-adaptive-pruning false --kmer-size 10 --kmer-size 25 --dont-increase-kmer-sizes-for-cycles false --allow-non-unique-kmers-in-ref false --num-pruning-samples 1 --min-dangling-branch-length 4 --recover-all-dangling-branches false --max-num-haplotypes-in-population 128 --min-pruning 2 --adaptive-pruning-initial-error-rate 0.001 --pruning-lod-threshold 2.302585092994046 --pruning-seeding-lod-threshold 9.210340371976184 --max-unpruned-variants 100 --linked-de-bruijn-graph false --disable-artificial-haplotype-recovery false --debug-assembly false --debug-graph-transformations false --capture-assembly-failure-bam false --error-correction-log-odds -Infinity --error-correct-reads false --kmer-length-for-read-error-correction 25 --min-observations-for-kmer-to-be-solid 20 --base-quality-score-threshold 18 --pair-hmm-gap-continuation-penalty 10 --pair-hmm-implementation FASTEST_AVAILABLE --pcr-indel-model CONSERVATIVE --phred-scaled-global-read-mismapping-rate 45 --native-pair-hmm-threads 4 --native-pair-hmm-use-double-precision false --bam-writer-type CALLED_HAPLOTYPES --smith-waterman JAVA --emit-ref-confidence NONE --max-mnp-distance 1 --force-call-filtered-alleles false --allele-informative-reads-overlap-margin 2 --min-assembly-region-size 50 --max-assembly-region-size 300 --active-probability-threshold 0.002 --max-prob-propagation-distance 50 --force-active false --assembly-region-padding 100 --padding-around-indels 75 --padding-around-snps 20 --padding-around-strs 75 --max-reads-per-alignment-start 50 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays --disable-tool-default-read-filters false --max-read-length 2147483647 --min-read-length 30 --disable-tool-default-annotations false --enable-all-annotations false",Version="4.1.9.0",Date="8 juillet 2021 12:41:08 CEST">
+##INFO=<ID=AS_FilterStatus,Number=A,Type=String,Description="Filter status for each allele, as assessed by ApplyRecalibration. Note that the VCF filter field will reflect the most lenient/sensitive status across all alleles.">
+##INFO=<ID=AS_SB_TABLE,Number=1,Type=String,Description="Allele-specific forward/reverse read counts for strand bias tests. Includes the reference and alleles separated by |.">
+##INFO=<ID=AS_UNIQ_ALT_READ_COUNT,Number=A,Type=Integer,Description="Number of reads with unique start and mate end positions for each alt at a variant site">
+##INFO=<ID=CONTQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to contamination">
+##INFO=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth; some reads may have been filtered">
+##INFO=<ID=ECNT,Number=1,Type=Integer,Description="Number of events in this haplotype">
+##INFO=<ID=GERMQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not germline variants">
+##INFO=<ID=MBQ,Number=R,Type=Integer,Description="median base quality">
+##INFO=<ID=MFRL,Number=R,Type=Integer,Description="median fragment length">
+##INFO=<ID=MMQ,Number=R,Type=Integer,Description="median mapping quality">
+##INFO=<ID=MPOS,Number=A,Type=Integer,Description="median distance from end of read">
+##INFO=<ID=NALOD,Number=A,Type=Float,Description="Negative log 10 odds of artifact in normal with same allele fraction as tumor">
+##INFO=<ID=NCount,Number=1,Type=Integer,Description="Count of N bases in the pileup">
+##INFO=<ID=NLOD,Number=A,Type=Float,Description="Normal log 10 likelihood ratio of diploid het or hom alt genotypes">
+##INFO=<ID=OCM,Number=1,Type=Integer,Description="Number of alt reads whose original alignment doesn't match the current contig.">
+##INFO=<ID=PON,Number=0,Type=Flag,Description="site found in panel of normals">
+##INFO=<ID=POPAF,Number=A,Type=Float,Description="negative log 10 population allele frequencies of alt alleles">
+##INFO=<ID=ROQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to read orientation artifact">
+##INFO=<ID=RPA,Number=R,Type=Integer,Description="Number of times tandem repeat unit is repeated, for each allele (including reference)">
+##INFO=<ID=RU,Number=1,Type=String,Description="Tandem repeat unit (bases)">
+##INFO=<ID=SEQQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not sequencing errors">
+##INFO=<ID=STR,Number=0,Type=Flag,Description="Variant is a short tandem repeat">
+##INFO=<ID=STRANDQ,Number=1,Type=Integer,Description="Phred-scaled quality of strand bias artifact">
+##INFO=<ID=STRQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles in STRs are not polymerase slippage errors">
+##INFO=<ID=TLOD,Number=A,Type=Float,Description="Log 10 likelihood ratio score of variant existing versus not existing">
+##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
+##MutectVersion=2.2
+##contig=<ID=1,length=249250621>
+##contig=<ID=2,length=243199373>
+##contig=<ID=3,length=198022430>
+##contig=<ID=4,length=191154276>
+##contig=<ID=5,length=180915260>
+##contig=<ID=6,length=171115067>
+##contig=<ID=7,length=159138663>
+##contig=<ID=8,length=146364022>
+##contig=<ID=9,length=141213431>
+##contig=<ID=10,length=135534747>
+##contig=<ID=11,length=135006516>
+##contig=<ID=12,length=133851895>
+##contig=<ID=13,length=115169878>
+##contig=<ID=14,length=107349540>
+##contig=<ID=15,length=102531392>
+##contig=<ID=16,length=90354753>
+##contig=<ID=17,length=81195210>
+##contig=<ID=18,length=78077248>
+##contig=<ID=19,length=59128983>
+##contig=<ID=20,length=63025520>
+##contig=<ID=21,length=48129895>
+##contig=<ID=22,length=51304566>
+##contig=<ID=Un_gl000211,length=166566>
+##contig=<ID=Un_gl000212,length=186858>
+##contig=<ID=Un_gl000213,length=164239>
+##contig=<ID=Un_gl000214,length=137718>
+##contig=<ID=Un_gl000215,length=172545>
+##contig=<ID=Un_gl000216,length=172294>
+##contig=<ID=Un_gl000217,length=172149>
+##contig=<ID=Un_gl000218,length=161147>
+##contig=<ID=Un_gl000219,length=179198>
+##contig=<ID=Un_gl000220,length=161802>
+##contig=<ID=Un_gl000221,length=155397>
+##contig=<ID=Un_gl000222,length=186861>
+##contig=<ID=Un_gl000223,length=180455>
+##contig=<ID=Un_gl000224,length=179693>
+##contig=<ID=Un_gl000225,length=211173>
+##contig=<ID=Un_gl000226,length=15008>
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+##contig=<ID=Un_gl000230,length=43691>
+##contig=<ID=Un_gl000231,length=27386>
+##contig=<ID=Un_gl000232,length=40652>
+##contig=<ID=Un_gl000233,length=45941>
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+##contig=<ID=4_ctg9_hap1,length=590426>
+##contig=<ID=4_gl000193_random,length=189789>
+##contig=<ID=4_gl000194_random,length=191469>
+##contig=<ID=6_apd_hap1,length=4622290>
+##contig=<ID=6_cox_hap2,length=4795371>
+##contig=<ID=6_dbb_hap3,length=4610396>
+##contig=<ID=6_mann_hap4,length=4683263>
+##contig=<ID=6_mcf_hap5,length=4833398>
+##contig=<ID=6_qbl_hap6,length=4611984>
+##contig=<ID=6_ssto_hap7,length=4928567>
+##contig=<ID=7_gl000195_random,length=182896>
+##contig=<ID=8_gl000196_random,length=38914>
+##contig=<ID=8_gl000197_random,length=37175>
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+##contig=<ID=9_gl000201_random,length=36148>
+##contig=<ID=11_gl000202_random,length=40103>
+##contig=<ID=17_ctg5_hap1,length=1680828>
+##contig=<ID=17_gl000203_random,length=37498>
+##contig=<ID=17_gl000204_random,length=81310>
+##contig=<ID=17_gl000205_random,length=174588>
+##contig=<ID=17_gl000206_random,length=41001>
+##contig=<ID=18_gl000207_random,length=4262>
+##contig=<ID=19_gl000208_random,length=92689>
+##contig=<ID=19_gl000209_random,length=159169>
+##contig=<ID=21_gl000210_random,length=27682>
+##contig=<ID=X,length=155270560>
+##contig=<ID=Y,length=59373566>
+##contig=<ID=M,length=16571>
+##filtering_status=These calls have been filtered by FilterMutectCalls to label false positives with a list of failed filters and true positives with PASS.
+##normal_sample=COLO829N
+##source=FilterMutectCalls
+##source=Mutect2
+##tumor_sample=COLO829T
+##bcftools_normVersion=1.10.2+htslib-1.10.2
+##bcftools_normCommand=norm -Oz -m -both -f hg19.fa --threads 8 -o COLO829T_vs_COLO829N_Mutect2_filtered_pass_norm.vcf.gz COLO829T_vs_COLO829N_Mutect2_filtered_pass.vcf.gz; Date=Thu Jul  8 16:14:34 2021
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	NORMAL	TUMOR	ANOTHER
+1	34	.	T	G	.	PASS	TEST_VARIANT;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
diff --git a/oncoliner_assesment/examples/input_test/truth/sample_1/test_small_truth.vcf b/oncoliner_assesment/examples/input_test/truth/sample_1/test_small_truth.vcf
new file mode 100644
index 0000000..0a60e8b
--- /dev/null
+++ b/oncoliner_assesment/examples/input_test/truth/sample_1/test_small_truth.vcf
@@ -0,0 +1,183 @@
+##fileformat=VCFv4.2
+##FILTER=<ID=PASS,Description="All filters passed">
+##FILTER=<ID=FAIL,Description="Fail the site if all alleles fail but for different reasons.">
+##FILTER=<ID=base_qual,Description="alt median base quality">
+##FILTER=<ID=clustered_events,Description="Clustered events observed in the tumor">
+##FILTER=<ID=contamination,Description="contamination">
+##FILTER=<ID=duplicate,Description="evidence for alt allele is overrepresented by apparent duplicates">
+##FILTER=<ID=fragment,Description="abs(ref - alt) median fragment length">
+##FILTER=<ID=germline,Description="Evidence indicates this site is germline, not somatic">
+##FILTER=<ID=haplotype,Description="Variant near filtered variant on same haplotype.">
+##FILTER=<ID=low_allele_frac,Description="Allele fraction is below specified threshold">
+##FILTER=<ID=map_qual,Description="ref - alt median mapping quality">
+##FILTER=<ID=multiallelic,Description="Site filtered because too many alt alleles pass tumor LOD">
+##FILTER=<ID=n_ratio,Description="Ratio of N to alt exceeds specified ratio">
+##FILTER=<ID=normal_artifact,Description="artifact_in_normal">
+##FILTER=<ID=orientation,Description="orientation bias detected by the orientation bias mixture model">
+##FILTER=<ID=panel_of_normals,Description="Blacklisted site in panel of normals">
+##FILTER=<ID=position,Description="median distance of alt variants from end of reads">
+##FILTER=<ID=possible_numt,Description="Allele depth is below expected coverage of NuMT in autosome">
+##FILTER=<ID=slippage,Description="Site filtered due to contraction of short tandem repeat region">
+##FILTER=<ID=strand_bias,Description="Evidence for alt allele comes from one read direction only">
+##FILTER=<ID=strict_strand,Description="Evidence for alt allele is not represented in both directions">
+##FILTER=<ID=weak_evidence,Description="Mutation does not meet likelihood threshold">
+##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
+##FORMAT=<ID=AF,Number=A,Type=Float,Description="Allele fractions of alternate alleles in the tumor">
+##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
+##FORMAT=<ID=F1R2,Number=R,Type=Integer,Description="Count of reads in F1R2 pair orientation supporting each allele">
+##FORMAT=<ID=F2R1,Number=R,Type=Integer,Description="Count of reads in F2R1 pair orientation supporting each allele">
+##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
+##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
+##FORMAT=<ID=PGT,Number=1,Type=String,Description="Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles">
+##FORMAT=<ID=PID,Number=1,Type=String,Description="Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group">
+##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
+##FORMAT=<ID=PS,Number=1,Type=Integer,Description="Phasing set (typically the position of the first variant in the set)">
+##FORMAT=<ID=SB,Number=4,Type=Integer,Description="Per-sample component statistics which comprise the Fisher's Exact Test to detect strand bias.">
+##GATKCommandLine=<ID=FilterMutectCalls,CommandLine="FilterMutectCalls --output COLO829T_vs_COLO829N_Mutect2_filtered.vcf.gz --stats COLO829T_vs_COLO829N.vcf.gz.stats --contamination-table COLO829T_contamination.table.tsv --variant COLO829T_vs_COLO829N_Mutect2_unfiltered.vcf.gz --reference hg19.fa --threshold-strategy OPTIMAL_F_SCORE --f-score-beta 1.0 --false-discovery-rate 0.05 --initial-threshold 0.1 --mitochondria-mode false --max-events-in-region 2 --max-alt-allele-count 1 --unique-alt-read-count 0 --min-median-mapping-quality 30 --min-median-base-quality 20 --max-median-fragment-length-difference 10000 --min-median-read-position 1 --max-n-ratio Infinity --min-reads-per-strand 0 --min-allele-fraction 0.0 --contamination-estimate 0.0 --log-snv-prior -13.815510557964275 --log-indel-prior -16.11809565095832 --log-artifact-prior -2.302585092994046 --normal-p-value-threshold 0.001 --min-slippage-length 8 --pcr-slippage-rate 0.1 --distance-on-haplotype 100 --long-indel-length 5 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays  --disable-tool-default-read-filters false",Version="4.1.9.0",Date="8 juillet 2021 16:09:27 CEST">
+##GATKCommandLine=<ID=Mutect2,CommandLine="Mutect2 --tumor-sample COLO829T --normal-sample COLO829N --germline-resource /mnt/beegfs/home/tgutman/eucancan/colo829/work/c0/6c82bf4687b64df35ad12193f8e3cd/af-only-gnomad_modified.raw.sites.vcf.gz --dont-use-soft-clipped-bases true --min-base-quality-score 13 --output 10_1-135534747_COLO829T_vs_COLO829N.vcf --intervals 10_1-135534747.bed --input COLO829T_recal.bam --input COLO829N_recal.bam --reference hg19.fa --minimum-mapping-quality 20 --f1r2-median-mq 50 --f1r2-min-bq 20 --f1r2-max-depth 200 --genotype-pon-sites false --genotype-germline-sites false --af-of-alleles-not-in-resource -1.0 --mitochondria-mode false --tumor-lod-to-emit 3.0 --initial-tumor-lod 2.0 --pcr-snv-qual 40 --pcr-indel-qual 40 --max-population-af 0.01 --downsampling-stride 1 --callable-depth 10 --max-suspicious-reads-per-alignment-start 0 --normal-lod 2.2 --ignore-itr-artifacts false --gvcf-lod-band -2.5 --gvcf-lod-band -2.0 --gvcf-lod-band -1.5 --gvcf-lod-band -1.0 --gvcf-lod-band -0.5 --gvcf-lod-band 0.0 --gvcf-lod-band 0.5 --gvcf-lod-band 1.0 --minimum-allele-fraction 0.0 --independent-mates false --disable-adaptive-pruning false --kmer-size 10 --kmer-size 25 --dont-increase-kmer-sizes-for-cycles false --allow-non-unique-kmers-in-ref false --num-pruning-samples 1 --min-dangling-branch-length 4 --recover-all-dangling-branches false --max-num-haplotypes-in-population 128 --min-pruning 2 --adaptive-pruning-initial-error-rate 0.001 --pruning-lod-threshold 2.302585092994046 --pruning-seeding-lod-threshold 9.210340371976184 --max-unpruned-variants 100 --linked-de-bruijn-graph false --disable-artificial-haplotype-recovery false --debug-assembly false --debug-graph-transformations false --capture-assembly-failure-bam false --error-correction-log-odds -Infinity --error-correct-reads false --kmer-length-for-read-error-correction 25 --min-observations-for-kmer-to-be-solid 20 --base-quality-score-threshold 18 --pair-hmm-gap-continuation-penalty 10 --pair-hmm-implementation FASTEST_AVAILABLE --pcr-indel-model CONSERVATIVE --phred-scaled-global-read-mismapping-rate 45 --native-pair-hmm-threads 4 --native-pair-hmm-use-double-precision false --bam-writer-type CALLED_HAPLOTYPES --smith-waterman JAVA --emit-ref-confidence NONE --max-mnp-distance 1 --force-call-filtered-alleles false --allele-informative-reads-overlap-margin 2 --min-assembly-region-size 50 --max-assembly-region-size 300 --active-probability-threshold 0.002 --max-prob-propagation-distance 50 --force-active false --assembly-region-padding 100 --padding-around-indels 75 --padding-around-snps 20 --padding-around-strs 75 --max-reads-per-alignment-start 50 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays --disable-tool-default-read-filters false --max-read-length 2147483647 --min-read-length 30 --disable-tool-default-annotations false --enable-all-annotations false",Version="4.1.9.0",Date="8 juillet 2021 12:41:08 CEST">
+##INFO=<ID=AS_FilterStatus,Number=A,Type=String,Description="Filter status for each allele, as assessed by ApplyRecalibration. Note that the VCF filter field will reflect the most lenient/sensitive status across all alleles.">
+##INFO=<ID=AS_SB_TABLE,Number=1,Type=String,Description="Allele-specific forward/reverse read counts for strand bias tests. Includes the reference and alleles separated by |.">
+##INFO=<ID=AS_UNIQ_ALT_READ_COUNT,Number=A,Type=Integer,Description="Number of reads with unique start and mate end positions for each alt at a variant site">
+##INFO=<ID=CONTQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to contamination">
+##INFO=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth; some reads may have been filtered">
+##INFO=<ID=ECNT,Number=1,Type=Integer,Description="Number of events in this haplotype">
+##INFO=<ID=GERMQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not germline variants">
+##INFO=<ID=MBQ,Number=R,Type=Integer,Description="median base quality">
+##INFO=<ID=MFRL,Number=R,Type=Integer,Description="median fragment length">
+##INFO=<ID=MMQ,Number=R,Type=Integer,Description="median mapping quality">
+##INFO=<ID=MPOS,Number=A,Type=Integer,Description="median distance from end of read">
+##INFO=<ID=NALOD,Number=A,Type=Float,Description="Negative log 10 odds of artifact in normal with same allele fraction as tumor">
+##INFO=<ID=NCount,Number=1,Type=Integer,Description="Count of N bases in the pileup">
+##INFO=<ID=NLOD,Number=A,Type=Float,Description="Normal log 10 likelihood ratio of diploid het or hom alt genotypes">
+##INFO=<ID=OCM,Number=1,Type=Integer,Description="Number of alt reads whose original alignment doesn't match the current contig.">
+##INFO=<ID=PON,Number=0,Type=Flag,Description="site found in panel of normals">
+##INFO=<ID=POPAF,Number=A,Type=Float,Description="negative log 10 population allele frequencies of alt alleles">
+##INFO=<ID=ROQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to read orientation artifact">
+##INFO=<ID=RPA,Number=R,Type=Integer,Description="Number of times tandem repeat unit is repeated, for each allele (including reference)">
+##INFO=<ID=RU,Number=1,Type=String,Description="Tandem repeat unit (bases)">
+##INFO=<ID=SEQQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not sequencing errors">
+##INFO=<ID=STR,Number=0,Type=Flag,Description="Variant is a short tandem repeat">
+##INFO=<ID=STRANDQ,Number=1,Type=Integer,Description="Phred-scaled quality of strand bias artifact">
+##INFO=<ID=STRQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles in STRs are not polymerase slippage errors">
+##INFO=<ID=TLOD,Number=A,Type=Float,Description="Log 10 likelihood ratio score of variant existing versus not existing">
+##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
+##MutectVersion=2.2
+##contig=<ID=1,length=249250621>
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+##contig=<ID=6_dbb_hap3,length=4610396>
+##contig=<ID=6_mann_hap4,length=4683263>
+##contig=<ID=6_mcf_hap5,length=4833398>
+##contig=<ID=6_qbl_hap6,length=4611984>
+##contig=<ID=6_ssto_hap7,length=4928567>
+##contig=<ID=7_gl000195_random,length=182896>
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+##contig=<ID=17_ctg5_hap1,length=1680828>
+##contig=<ID=17_gl000203_random,length=37498>
+##contig=<ID=17_gl000204_random,length=81310>
+##contig=<ID=17_gl000205_random,length=174588>
+##contig=<ID=17_gl000206_random,length=41001>
+##contig=<ID=18_gl000207_random,length=4262>
+##contig=<ID=19_gl000208_random,length=92689>
+##contig=<ID=19_gl000209_random,length=159169>
+##contig=<ID=21_gl000210_random,length=27682>
+##contig=<ID=X,length=155270560>
+##contig=<ID=Y,length=59373566>
+##contig=<ID=M,length=16571>
+##filtering_status=These calls have been filtered by FilterMutectCalls to label false positives with a list of failed filters and true positives with PASS.
+##normal_sample=COLO829N
+##source=FilterMutectCalls
+##source=Mutect2
+##tumor_sample=COLO829T
+##bcftools_normVersion=1.10.2+htslib-1.10.2
+##bcftools_normCommand=norm -Oz -m -both -f hg19.fa --threads 8 -o COLO829T_vs_COLO829N_Mutect2_filtered_pass_norm.vcf.gz COLO829T_vs_COLO829N_Mutect2_filtered_pass.vcf.gz; Date=Thu Jul  8 16:14:34 2021
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	COLO829N	COLO829T
+1	2	dup_bracket	G	]1:110]G	.	PASS	AS_FilterStatus=SITE;CSQ=ENSG00000142611|ZSWIM7|protein_altering_variant,ENSG00000142611|PRDM16|protein_altering_variant;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	63	.	A	C	.	PASS	AS_FilterStatus=SITE;CSQ=ENSG00000142611|ZSWIM7|protein_altering_variant,ENSG00000142611|PRDM16|protein_altering_variant;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	63	.	A	C	.	PASS	DUP;AS_FilterStatus=SITE;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	63	.	A	C	.	PASS	DUP;AS_FilterStatus=SITE;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	74	.	T	G	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	79	.	G	C	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	79	.	G	C	.	PASS	DUP;AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	110	.	T	T]1:210]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	90	.	T	T]1:300]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	100	.	T	T]1:200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	200	.	T	T]1:400]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	280	.	T	T]1:600]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	290	.	T	T]1:600]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	300	.	T	T]1:600]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	100	.	T	T]2:300]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	90	.	T	T]2:300]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	2100	.	T	T]2:2200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	2102	.	T	T]2:2200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
diff --git a/oncoliner_assesment/examples/input_test/truth/sample_2/test_small_truth_2.vcf b/oncoliner_assesment/examples/input_test/truth/sample_2/test_small_truth_2.vcf
new file mode 100644
index 0000000..0a60e8b
--- /dev/null
+++ b/oncoliner_assesment/examples/input_test/truth/sample_2/test_small_truth_2.vcf
@@ -0,0 +1,183 @@
+##fileformat=VCFv4.2
+##FILTER=<ID=PASS,Description="All filters passed">
+##FILTER=<ID=FAIL,Description="Fail the site if all alleles fail but for different reasons.">
+##FILTER=<ID=base_qual,Description="alt median base quality">
+##FILTER=<ID=clustered_events,Description="Clustered events observed in the tumor">
+##FILTER=<ID=contamination,Description="contamination">
+##FILTER=<ID=duplicate,Description="evidence for alt allele is overrepresented by apparent duplicates">
+##FILTER=<ID=fragment,Description="abs(ref - alt) median fragment length">
+##FILTER=<ID=germline,Description="Evidence indicates this site is germline, not somatic">
+##FILTER=<ID=haplotype,Description="Variant near filtered variant on same haplotype.">
+##FILTER=<ID=low_allele_frac,Description="Allele fraction is below specified threshold">
+##FILTER=<ID=map_qual,Description="ref - alt median mapping quality">
+##FILTER=<ID=multiallelic,Description="Site filtered because too many alt alleles pass tumor LOD">
+##FILTER=<ID=n_ratio,Description="Ratio of N to alt exceeds specified ratio">
+##FILTER=<ID=normal_artifact,Description="artifact_in_normal">
+##FILTER=<ID=orientation,Description="orientation bias detected by the orientation bias mixture model">
+##FILTER=<ID=panel_of_normals,Description="Blacklisted site in panel of normals">
+##FILTER=<ID=position,Description="median distance of alt variants from end of reads">
+##FILTER=<ID=possible_numt,Description="Allele depth is below expected coverage of NuMT in autosome">
+##FILTER=<ID=slippage,Description="Site filtered due to contraction of short tandem repeat region">
+##FILTER=<ID=strand_bias,Description="Evidence for alt allele comes from one read direction only">
+##FILTER=<ID=strict_strand,Description="Evidence for alt allele is not represented in both directions">
+##FILTER=<ID=weak_evidence,Description="Mutation does not meet likelihood threshold">
+##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
+##FORMAT=<ID=AF,Number=A,Type=Float,Description="Allele fractions of alternate alleles in the tumor">
+##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
+##FORMAT=<ID=F1R2,Number=R,Type=Integer,Description="Count of reads in F1R2 pair orientation supporting each allele">
+##FORMAT=<ID=F2R1,Number=R,Type=Integer,Description="Count of reads in F2R1 pair orientation supporting each allele">
+##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
+##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
+##FORMAT=<ID=PGT,Number=1,Type=String,Description="Physical phasing haplotype information, describing how the alternate alleles are phased in relation to one another; will always be heterozygous and is not intended to describe called alleles">
+##FORMAT=<ID=PID,Number=1,Type=String,Description="Physical phasing ID information, where each unique ID within a given sample (but not across samples) connects records within a phasing group">
+##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
+##FORMAT=<ID=PS,Number=1,Type=Integer,Description="Phasing set (typically the position of the first variant in the set)">
+##FORMAT=<ID=SB,Number=4,Type=Integer,Description="Per-sample component statistics which comprise the Fisher's Exact Test to detect strand bias.">
+##GATKCommandLine=<ID=FilterMutectCalls,CommandLine="FilterMutectCalls --output COLO829T_vs_COLO829N_Mutect2_filtered.vcf.gz --stats COLO829T_vs_COLO829N.vcf.gz.stats --contamination-table COLO829T_contamination.table.tsv --variant COLO829T_vs_COLO829N_Mutect2_unfiltered.vcf.gz --reference hg19.fa --threshold-strategy OPTIMAL_F_SCORE --f-score-beta 1.0 --false-discovery-rate 0.05 --initial-threshold 0.1 --mitochondria-mode false --max-events-in-region 2 --max-alt-allele-count 1 --unique-alt-read-count 0 --min-median-mapping-quality 30 --min-median-base-quality 20 --max-median-fragment-length-difference 10000 --min-median-read-position 1 --max-n-ratio Infinity --min-reads-per-strand 0 --min-allele-fraction 0.0 --contamination-estimate 0.0 --log-snv-prior -13.815510557964275 --log-indel-prior -16.11809565095832 --log-artifact-prior -2.302585092994046 --normal-p-value-threshold 0.001 --min-slippage-length 8 --pcr-slippage-rate 0.1 --distance-on-haplotype 100 --long-indel-length 5 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays  --disable-tool-default-read-filters false",Version="4.1.9.0",Date="8 juillet 2021 16:09:27 CEST">
+##GATKCommandLine=<ID=Mutect2,CommandLine="Mutect2 --tumor-sample COLO829T --normal-sample COLO829N --germline-resource /mnt/beegfs/home/tgutman/eucancan/colo829/work/c0/6c82bf4687b64df35ad12193f8e3cd/af-only-gnomad_modified.raw.sites.vcf.gz --dont-use-soft-clipped-bases true --min-base-quality-score 13 --output 10_1-135534747_COLO829T_vs_COLO829N.vcf --intervals 10_1-135534747.bed --input COLO829T_recal.bam --input COLO829N_recal.bam --reference hg19.fa --minimum-mapping-quality 20 --f1r2-median-mq 50 --f1r2-min-bq 20 --f1r2-max-depth 200 --genotype-pon-sites false --genotype-germline-sites false --af-of-alleles-not-in-resource -1.0 --mitochondria-mode false --tumor-lod-to-emit 3.0 --initial-tumor-lod 2.0 --pcr-snv-qual 40 --pcr-indel-qual 40 --max-population-af 0.01 --downsampling-stride 1 --callable-depth 10 --max-suspicious-reads-per-alignment-start 0 --normal-lod 2.2 --ignore-itr-artifacts false --gvcf-lod-band -2.5 --gvcf-lod-band -2.0 --gvcf-lod-band -1.5 --gvcf-lod-band -1.0 --gvcf-lod-band -0.5 --gvcf-lod-band 0.0 --gvcf-lod-band 0.5 --gvcf-lod-band 1.0 --minimum-allele-fraction 0.0 --independent-mates false --disable-adaptive-pruning false --kmer-size 10 --kmer-size 25 --dont-increase-kmer-sizes-for-cycles false --allow-non-unique-kmers-in-ref false --num-pruning-samples 1 --min-dangling-branch-length 4 --recover-all-dangling-branches false --max-num-haplotypes-in-population 128 --min-pruning 2 --adaptive-pruning-initial-error-rate 0.001 --pruning-lod-threshold 2.302585092994046 --pruning-seeding-lod-threshold 9.210340371976184 --max-unpruned-variants 100 --linked-de-bruijn-graph false --disable-artificial-haplotype-recovery false --debug-assembly false --debug-graph-transformations false --capture-assembly-failure-bam false --error-correction-log-odds -Infinity --error-correct-reads false --kmer-length-for-read-error-correction 25 --min-observations-for-kmer-to-be-solid 20 --base-quality-score-threshold 18 --pair-hmm-gap-continuation-penalty 10 --pair-hmm-implementation FASTEST_AVAILABLE --pcr-indel-model CONSERVATIVE --phred-scaled-global-read-mismapping-rate 45 --native-pair-hmm-threads 4 --native-pair-hmm-use-double-precision false --bam-writer-type CALLED_HAPLOTYPES --smith-waterman JAVA --emit-ref-confidence NONE --max-mnp-distance 1 --force-call-filtered-alleles false --allele-informative-reads-overlap-margin 2 --min-assembly-region-size 50 --max-assembly-region-size 300 --active-probability-threshold 0.002 --max-prob-propagation-distance 50 --force-active false --assembly-region-padding 100 --padding-around-indels 75 --padding-around-snps 20 --padding-around-strs 75 --max-reads-per-alignment-start 50 --interval-set-rule UNION --interval-padding 0 --interval-exclusion-padding 0 --interval-merging-rule ALL --read-validation-stringency SILENT --seconds-between-progress-updates 10.0 --disable-sequence-dictionary-validation false --create-output-bam-index true --create-output-bam-md5 false --create-output-variant-index true --create-output-variant-md5 false --lenient false --add-output-sam-program-record true --add-output-vcf-command-line true --cloud-prefetch-buffer 40 --cloud-index-prefetch-buffer -1 --disable-bam-index-caching false --sites-only-vcf-output false --help false --version false --showHidden false --verbosity INFO --QUIET false --use-jdk-deflater false --use-jdk-inflater false --gcs-max-retries 20 --gcs-project-for-requester-pays --disable-tool-default-read-filters false --max-read-length 2147483647 --min-read-length 30 --disable-tool-default-annotations false --enable-all-annotations false",Version="4.1.9.0",Date="8 juillet 2021 12:41:08 CEST">
+##INFO=<ID=AS_FilterStatus,Number=A,Type=String,Description="Filter status for each allele, as assessed by ApplyRecalibration. Note that the VCF filter field will reflect the most lenient/sensitive status across all alleles.">
+##INFO=<ID=AS_SB_TABLE,Number=1,Type=String,Description="Allele-specific forward/reverse read counts for strand bias tests. Includes the reference and alleles separated by |.">
+##INFO=<ID=AS_UNIQ_ALT_READ_COUNT,Number=A,Type=Integer,Description="Number of reads with unique start and mate end positions for each alt at a variant site">
+##INFO=<ID=CONTQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to contamination">
+##INFO=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth; some reads may have been filtered">
+##INFO=<ID=ECNT,Number=1,Type=Integer,Description="Number of events in this haplotype">
+##INFO=<ID=GERMQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not germline variants">
+##INFO=<ID=MBQ,Number=R,Type=Integer,Description="median base quality">
+##INFO=<ID=MFRL,Number=R,Type=Integer,Description="median fragment length">
+##INFO=<ID=MMQ,Number=R,Type=Integer,Description="median mapping quality">
+##INFO=<ID=MPOS,Number=A,Type=Integer,Description="median distance from end of read">
+##INFO=<ID=NALOD,Number=A,Type=Float,Description="Negative log 10 odds of artifact in normal with same allele fraction as tumor">
+##INFO=<ID=NCount,Number=1,Type=Integer,Description="Count of N bases in the pileup">
+##INFO=<ID=NLOD,Number=A,Type=Float,Description="Normal log 10 likelihood ratio of diploid het or hom alt genotypes">
+##INFO=<ID=OCM,Number=1,Type=Integer,Description="Number of alt reads whose original alignment doesn't match the current contig.">
+##INFO=<ID=PON,Number=0,Type=Flag,Description="site found in panel of normals">
+##INFO=<ID=POPAF,Number=A,Type=Float,Description="negative log 10 population allele frequencies of alt alleles">
+##INFO=<ID=ROQ,Number=1,Type=Float,Description="Phred-scaled qualities that alt allele are not due to read orientation artifact">
+##INFO=<ID=RPA,Number=R,Type=Integer,Description="Number of times tandem repeat unit is repeated, for each allele (including reference)">
+##INFO=<ID=RU,Number=1,Type=String,Description="Tandem repeat unit (bases)">
+##INFO=<ID=SEQQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles are not sequencing errors">
+##INFO=<ID=STR,Number=0,Type=Flag,Description="Variant is a short tandem repeat">
+##INFO=<ID=STRANDQ,Number=1,Type=Integer,Description="Phred-scaled quality of strand bias artifact">
+##INFO=<ID=STRQ,Number=1,Type=Integer,Description="Phred-scaled quality that alt alleles in STRs are not polymerase slippage errors">
+##INFO=<ID=TLOD,Number=A,Type=Float,Description="Log 10 likelihood ratio score of variant existing versus not existing">
+##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
+##MutectVersion=2.2
+##contig=<ID=1,length=249250621>
+##contig=<ID=2,length=243199373>
+##contig=<ID=3,length=198022430>
+##contig=<ID=4,length=191154276>
+##contig=<ID=5,length=180915260>
+##contig=<ID=6,length=171115067>
+##contig=<ID=7,length=159138663>
+##contig=<ID=8,length=146364022>
+##contig=<ID=9,length=141213431>
+##contig=<ID=10,length=135534747>
+##contig=<ID=11,length=135006516>
+##contig=<ID=12,length=133851895>
+##contig=<ID=13,length=115169878>
+##contig=<ID=14,length=107349540>
+##contig=<ID=15,length=102531392>
+##contig=<ID=16,length=90354753>
+##contig=<ID=17,length=81195210>
+##contig=<ID=18,length=78077248>
+##contig=<ID=19,length=59128983>
+##contig=<ID=20,length=63025520>
+##contig=<ID=21,length=48129895>
+##contig=<ID=22,length=51304566>
+##contig=<ID=Un_gl000211,length=166566>
+##contig=<ID=Un_gl000212,length=186858>
+##contig=<ID=Un_gl000213,length=164239>
+##contig=<ID=Un_gl000214,length=137718>
+##contig=<ID=Un_gl000215,length=172545>
+##contig=<ID=Un_gl000216,length=172294>
+##contig=<ID=Un_gl000217,length=172149>
+##contig=<ID=Un_gl000218,length=161147>
+##contig=<ID=Un_gl000219,length=179198>
+##contig=<ID=Un_gl000220,length=161802>
+##contig=<ID=Un_gl000221,length=155397>
+##contig=<ID=Un_gl000222,length=186861>
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+##contig=<ID=1_gl000191_random,length=106433>
+##contig=<ID=1_gl000192_random,length=547496>
+##contig=<ID=4_ctg9_hap1,length=590426>
+##contig=<ID=4_gl000193_random,length=189789>
+##contig=<ID=4_gl000194_random,length=191469>
+##contig=<ID=6_apd_hap1,length=4622290>
+##contig=<ID=6_cox_hap2,length=4795371>
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+##contig=<ID=6_ssto_hap7,length=4928567>
+##contig=<ID=7_gl000195_random,length=182896>
+##contig=<ID=8_gl000196_random,length=38914>
+##contig=<ID=8_gl000197_random,length=37175>
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+##contig=<ID=17_gl000206_random,length=41001>
+##contig=<ID=18_gl000207_random,length=4262>
+##contig=<ID=19_gl000208_random,length=92689>
+##contig=<ID=19_gl000209_random,length=159169>
+##contig=<ID=21_gl000210_random,length=27682>
+##contig=<ID=X,length=155270560>
+##contig=<ID=Y,length=59373566>
+##contig=<ID=M,length=16571>
+##filtering_status=These calls have been filtered by FilterMutectCalls to label false positives with a list of failed filters and true positives with PASS.
+##normal_sample=COLO829N
+##source=FilterMutectCalls
+##source=Mutect2
+##tumor_sample=COLO829T
+##bcftools_normVersion=1.10.2+htslib-1.10.2
+##bcftools_normCommand=norm -Oz -m -both -f hg19.fa --threads 8 -o COLO829T_vs_COLO829N_Mutect2_filtered_pass_norm.vcf.gz COLO829T_vs_COLO829N_Mutect2_filtered_pass.vcf.gz; Date=Thu Jul  8 16:14:34 2021
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	COLO829N	COLO829T
+1	2	dup_bracket	G	]1:110]G	.	PASS	AS_FilterStatus=SITE;CSQ=ENSG00000142611|ZSWIM7|protein_altering_variant,ENSG00000142611|PRDM16|protein_altering_variant;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	63	.	A	C	.	PASS	AS_FilterStatus=SITE;CSQ=ENSG00000142611|ZSWIM7|protein_altering_variant,ENSG00000142611|PRDM16|protein_altering_variant;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	63	.	A	C	.	PASS	DUP;AS_FilterStatus=SITE;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	63	.	A	C	.	PASS	DUP;AS_FilterStatus=SITE;AS_SB_TABLE=88,80|4,3;DP=176;ECNT=1;GERMQ=93;MBQ=36,35;MFRL=542,675;MMQ=40,34;MPOS=15;NALOD=1.63;NLOD=12.59;POPAF=0.903;TLOD=10.11	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:42,0:0.023:42:17,0:24,0:19,23,0,0	0/1:126,7:0.06:133:69,3:56,4:69,57,4,3
+1	74	.	T	G	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	79	.	G	C	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	79	.	G	C	.	PASS	DUP;AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	110	.	T	T]1:210]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	90	.	T	T]1:300]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	100	.	T	T]1:200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	200	.	T	T]1:400]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	280	.	T	T]1:600]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	290	.	T	T]1:600]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+1	300	.	T	T]1:600]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	100	.	T	T]2:300]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	90	.	T	T]2:300]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	2100	.	T	T]2:2200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
+2	2102	.	T	T]2:2200]	.	PASS	AS_FilterStatus=SITE;AS_SB_TABLE=16,23|7,7;DP=55;ECNT=1;GERMQ=6;MBQ=38,34;MFRL=531,532;MMQ=60,38;MPOS=14;NALOD=1.23;NLOD=4.77;POPAF=1.54;TLOD=33.58	GT:AD:AF:DP:F1R2:F2R1:SB	0/0:16,0:0.056:16:4,0:11,0:7,9,0,0	0/1:23,14:0.38:37:13,5:10,7:9,14,7,7
diff --git a/oncoliner_assesment/examples/output_test/.gitignore b/oncoliner_assesment/examples/output_test/.gitignore
new file mode 100644
index 0000000..c96a04f
--- /dev/null
+++ b/oncoliner_assesment/examples/output_test/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
\ No newline at end of file
diff --git a/oncoliner_assesment/examples/test_bulk.sh b/oncoliner_assesment/examples/test_bulk.sh
new file mode 100644
index 0000000..9503758
--- /dev/null
+++ b/oncoliner_assesment/examples/test_bulk.sh
@@ -0,0 +1,2 @@
+
+python3 -O ../src/assesment_bulk.py -c test_config.tsv -o output_test/ --no-gzip
\ No newline at end of file
diff --git a/oncoliner_assesment/examples/test_config.tsv b/oncoliner_assesment/examples/test_config.tsv
new file mode 100644
index 0000000..380f39a
--- /dev/null
+++ b/oncoliner_assesment/examples/test_config.tsv
@@ -0,0 +1,3 @@
+sample_name	sample_types	reference_fasta_path	truth_vcf_paths	test_vcf_paths
+sample_1	recall	fake_ref.fa	./input_test/truth/sample_1/test_small_truth.vcf	./input_test/test/sample_1/test_small.vcf
+sample_2	precision	fake_ref.fa	./input_test/truth/sample_2/test_small_truth_2.vcf	./input_test/test/sample_2/test_small_2.vcf
diff --git a/oncoliner_assesment/examples/test_main.sh b/oncoliner_assesment/examples/test_main.sh
new file mode 100644
index 0000000..82bb331
--- /dev/null
+++ b/oncoliner_assesment/examples/test_main.sh
@@ -0,0 +1,2 @@
+
+python3 -O ../src/assesment_main.py -t ./input_test/truth/sample_1/test_small_truth.vcf -v ./input_test/test/sample_1/test_small.vcf -f fake_ref.fa -o output_test/test_ --no-gzip
\ No newline at end of file
diff --git a/oncoliner_assesment/src/assesment_bulk.py b/oncoliner_assesment/src/assesment_bulk.py
new file mode 100644
index 0000000..a7297f5
--- /dev/null
+++ b/oncoliner_assesment/src/assesment_bulk.py
@@ -0,0 +1,107 @@
+from typing import Iterable, List
+import os
+import argparse
+import glob
+from concurrent.futures import ProcessPoolExecutor
+
+from assesment_main import main  # noqa
+from vcf_ops.constants import DEFAULT_CONTIGS, DEFAULT_INDEL_THRESHOLD, DEFAULT_WINDOW_RADIUS, DEFAULT_SV_BINS  # noqa
+from vcf_ops.metrics import aggregate_metrics, combine_precision_recall_metrics  # noqa
+
+import pandas as pd
+import logging
+
+
+def read_config(config_path: str) -> pd.DataFrame:
+    """
+    Reads the config file and returns a dataframe
+    """
+    config = pd.read_csv(config_path, sep='\t')
+    config['truth_vcf_paths'] = config['truth_vcf_paths'].map(lambda x: x.split(','))
+    config['test_vcf_paths'] = config['test_vcf_paths'].map(lambda x: x.split(','))
+    config['sample_types'] = config['sample_types'].map(lambda x: set(x.split(',')))
+    # There must be at least one recall and one precision sample
+    if len(config[config['sample_types'].map(lambda x: 'recall' in x)]) == 0:
+        raise ValueError('There must be at least one recall sample')
+    if len(config[config['sample_types'].map(lambda x: 'precision' in x)]) == 0:
+        raise ValueError('There must be at least one precision sample')
+    return config
+
+
+def run_evaluator_sample(output_folder: str, sample: pd.Series, indel_threshold: int, window_radius: int, sv_size_bins: List[str], contigs: List[str], keep_intermediates: bool, no_gzip: bool) -> None:
+    """
+    Runs the evaluator for a single sample
+    """
+    sample_name = sample['sample_name']
+    truth_vcf_paths = sample['truth_vcf_paths']
+    test_vcf_paths = sample['test_vcf_paths']
+    fasta_path = sample['reference_fasta_path']
+    output_folder = os.path.join(output_folder, sample_name)
+    os.makedirs(output_folder, exist_ok=True)
+    output_prefix = os.path.join(output_folder, 'pipeline')
+    # If output_prefixmetrics.csv already exists and is not empty, skip the evaluation
+    if os.path.exists(output_prefix + 'metrics.csv') and os.path.getsize(output_prefix + 'metrics.csv') > 0:
+        logging.info(f'Skipping {sample_name} evaluation because the metrics file already exists')
+        return
+    main(truth_vcf_paths, test_vcf_paths, output_prefix, fasta_path, indel_threshold,
+         window_radius, sv_size_bins, contigs, keep_intermediates, no_gzip)
+
+
+def aggregate_metrics_from_samples(output_file: str, samples_folder: str, recall_samples: Iterable[str], precision_samples: Iterable[str]):
+    # List all folders in the output folder
+    recall_samples_folders = [os.path.join(samples_folder, sample) for sample in recall_samples]
+    precision_samples_folders = [os.path.join(samples_folder, sample) for sample in precision_samples]
+    # List all metrics files
+    recall_samples_files = [glob.glob(os.path.join(sample_folder, '*metrics.csv'))[0] for sample_folder in recall_samples_folders]
+    precision_samples_files = [glob.glob(os.path.join(sample_folder, '*metrics.csv'))[0] for sample_folder in precision_samples_folders]
+    recall_dfs = [pd.read_csv(file) for file in recall_samples_files]
+    precision_dfs = [pd.read_csv(file) for file in precision_samples_files]
+    # Aggregate metrics
+    recall_agg_df = aggregate_metrics(recall_dfs)
+    precision_agg_df = aggregate_metrics(precision_dfs)
+    agg_metrics = combine_precision_recall_metrics(recall_agg_df, precision_agg_df)
+    # Write to file
+    agg_metrics.to_csv(output_file, index=False)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser('Oncoliner Assesment Bulk')
+    parser.add_argument('-c', '--config-file', required=True, type=str, help='Path to the config TSV file')
+    parser.add_argument('-o', '--output-folder', required=True, type=str, help='Path to the output folder')
+    parser.add_argument('-it', '--indel-threshold',
+                        help=f'Indel threshold, inclusive (default={DEFAULT_INDEL_THRESHOLD})', default=DEFAULT_INDEL_THRESHOLD, type=int)
+    parser.add_argument('-wr', '--window-radius',
+                        help=f'Window radius (default={DEFAULT_WINDOW_RADIUS})', default=DEFAULT_WINDOW_RADIUS, type=int)
+    parser.add_argument(
+        '--sv-size-bins', help=f'SV size bins for the output_prefix metrics (default={DEFAULT_SV_BINS})', nargs='+', default=DEFAULT_SV_BINS, type=int)
+    parser.add_argument(
+        '--contigs', help=f'Contigs to process (default={DEFAULT_CONTIGS})', nargs='+', default=DEFAULT_CONTIGS, type=str)
+    parser.add_argument('--keep-intermediates',
+                        help='Keep intermediate CSV/VCF files from input VCF files', action='store_true', default=False)
+    parser.add_argument('--no-gzip', help='Do not gzip output_prefix VCF files', action='store_true', default=False)
+    parser.add_argument('-p', '--max-processes', type=int, default=1, help='Maximum number of processes to use (defaults to 1)')
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+
+    # Convert to absolute paths
+    args.config_file = os.path.abspath(args.config_file)
+
+    config = read_config(args.config_file)
+    pool = ProcessPoolExecutor(max_workers=args.max_processes)
+    samples_output_folder = os.path.join(args.output_folder, 'samples')
+    os.makedirs(samples_output_folder, exist_ok=True)
+    futures = []
+    for _, sample in config.iterrows():
+        futures.append(pool.submit(run_evaluator_sample, samples_output_folder, sample, args.indel_threshold, args.window_radius,
+                       args.sv_size_bins, args.contigs, args.keep_intermediates, args.no_gzip))
+    for future in futures:
+        future.result()
+
+    pool.shutdown()
+
+    # Aggregate metrics
+    output_file = os.path.join(args.output_folder, 'aggregated_metrics.csv')
+    recall_samples = set(config[config['sample_types'].map(lambda x: 'recall' in x)]['sample_name'].tolist())
+    precision_samples = set(config[config['sample_types'].map(lambda x: 'precision' in x)]['sample_name'].tolist())
+    aggregate_metrics_from_samples(output_file, samples_output_folder, recall_samples, precision_samples)
diff --git a/oncoliner_assesment/src/assesment_main.py b/oncoliner_assesment/src/assesment_main.py
new file mode 100644
index 0000000..5003e9e
--- /dev/null
+++ b/oncoliner_assesment/src/assesment_main.py
@@ -0,0 +1,159 @@
+import os
+import sys
+import argparse
+import glob
+import pandas as pd
+
+# Add vcf-ops to the path
+sys.path.insert(0, os.path.join(os.path.abspath(os.path.dirname(__file__)), '..', '..', 'vcf_ops', 'src'))
+
+from vcf_ops import VariantType  # noqa
+from vcf_ops.i_o import read_vcfs, write_masked_vcfs  # noqa
+from vcf_ops.masks import snv_mask, indel_mask  # noqa
+from vcf_ops.intersect import intersect  # noqa
+from vcf_ops.metrics import compute_metrics  # noqa
+from vcf_ops.constants import DEFAULT_CONTIGS, DEFAULT_INDEL_THRESHOLD, DEFAULT_WINDOW_RADIUS, DEFAULT_SV_BINS  # noqa
+from indel_sv_converter import convert_indels, convert_svs  # noqa
+
+def _ingest(truth_vcfs, test_vcfs, fasta_ref, indel_threshold, output_prefix, contigs, keep_intermediates=False):
+    # Load truth and test VCFs
+    df_truth = read_vcfs(truth_vcfs)
+    df_test = read_vcfs(test_vcfs)
+
+    contigs = set(contigs)
+    # Skip SGL, 0-length variants and variants without contig in contigs list
+    contigs_truth_mask = df_truth['start_chrom'].isin(contigs) & df_truth['end_chrom'].isin(contigs)
+    contigs_test_mask = df_test['start_chrom'].isin(contigs) & df_test['end_chrom'].isin(contigs)
+    sgl_truth_mask = df_truth['type_inferred'] == VariantType.SGL.name
+    sgl_test_mask = df_test['type_inferred'] == VariantType.SGL.name
+    length_truth_mask = (df_truth['length'] == 0) & \
+        (df_truth['type_inferred'] != VariantType.SNV.name) & (df_truth['type_inferred'] != VariantType.TRA.name)
+    length_test_mask = (df_test['length'] == 0) & \
+        (df_test['type_inferred'] != VariantType.SNV.name) & (df_test['type_inferred'] != VariantType.TRA.name)
+    df_skipped_truth = df_truth[sgl_truth_mask | length_truth_mask | ~contigs_truth_mask]
+    df_skipped_test = df_test[sgl_test_mask | length_test_mask | ~contigs_test_mask]
+    df_truth = df_truth[~sgl_truth_mask & ~length_truth_mask & contigs_truth_mask]
+    df_test = df_test[~sgl_test_mask & ~length_test_mask & contigs_test_mask]
+
+    # Separate indels and SNVs from SVs
+    indel_snv_truth_mask = snv_mask(df_truth) | indel_mask(df_truth, indel_threshold)
+    indel_snv_test_mask = snv_mask(df_test) | indel_mask(df_test, indel_threshold)
+    df_truth_indel = df_truth[indel_snv_truth_mask]
+    df_test_indel = df_test[indel_snv_test_mask]
+    df_truth_sv = df_truth[~indel_snv_truth_mask]
+    df_test_sv = df_test[~indel_snv_test_mask]
+
+    # Convert everything to indel or sv representation
+    df_truth_indel = convert_indels(df_truth_indel, fasta_ref)
+    df_test_indel = convert_indels(df_test_indel, fasta_ref)
+    df_truth_sv = convert_svs(df_truth_sv, fasta_ref)
+    df_test_sv = convert_svs(df_test_sv, fasta_ref)
+
+    # Write CSVs if necessary
+    if keep_intermediates:
+        df_truth_sv.to_csv(output_prefix + 'truth_sv.csv', index=False)
+        df_test_sv.to_csv(output_prefix + 'test_sv.csv', index=False)
+        df_truth_indel.to_csv(output_prefix + 'truth_indel_snv.csv', index=False)
+        df_test_indel.to_csv(output_prefix + 'test_indel_snv.csv', index=False)
+
+    # Concat SVs and indels
+    df_truth = pd.concat([df_truth_indel, df_truth_sv], ignore_index=True)
+    df_test = pd.concat([df_test_indel, df_test_sv], ignore_index=True)
+
+    return df_truth, df_test, df_skipped_truth, df_skipped_test
+
+
+def main(truth_vcfs, test_vcfs, output_prefix, fasta_ref, indel_threshold=DEFAULT_INDEL_THRESHOLD, window_radius=DEFAULT_WINDOW_RADIUS,
+         sv_size_bins=DEFAULT_SV_BINS, contigs=DEFAULT_CONTIGS, keep_intermediates=False, no_gzip=False):
+    # Get files from the truth and test vcfs
+    truth_vcfs = [file for file_pattern in truth_vcfs for file in glob.glob(file_pattern)]
+    test_vcfs = [file for file_pattern in test_vcfs for file in glob.glob(file_pattern)]
+
+    # Sort bins
+    sv_size_bins.sort()
+    if sv_size_bins[0] < indel_threshold:
+        raise ValueError(f'SV size bins must be greater than {indel_threshold}')
+
+    # Read the input files
+    df_truth, df_test, df_skipped_truth, df_skipped_test = _ingest(
+        truth_vcfs, test_vcfs, fasta_ref, indel_threshold, output_prefix, contigs, keep_intermediates)
+
+    # Run benchmark
+    df_tp, df_tp_dup, \
+        df_fp, df_fp_dup, \
+        df_fn, df_fn_dup = intersect(df_truth, df_test, indel_threshold, window_radius)
+
+    # Write VCF files
+    command = ' '.join(sys.argv)
+    if len(df_tp) > 0:
+        write_masked_vcfs(df_tp, f'{output_prefix}tp.', indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'True positives can be found in {output_prefix}tp.*')
+    if len(df_tp_dup) > 0:
+        write_masked_vcfs(df_tp_dup, f'{output_prefix}tp_dup.', indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'True positives (duplicates) can be found in {output_prefix}tp_dup.*')
+    if len(df_fp) > 0:
+        write_masked_vcfs(df_fp, f'{output_prefix}fp.', indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'False positives can be found in {output_prefix}fp.*')
+    if len(df_fp_dup) > 0:
+        write_masked_vcfs(df_fp_dup, f'{output_prefix}fp_dup.', indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'False positives (duplicates) can be found in {output_prefix}fp_dup.*')
+    if len(df_skipped_test) > 0:
+        write_masked_vcfs(df_skipped_test, f'{output_prefix}skipped_test.',
+                          indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'Skipped test variants can be found in {output_prefix}skipped_test.*')
+    if len(df_fn) > 0:
+        write_masked_vcfs(df_fn, f'{output_prefix}fn.', indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'False negatives can be found in {output_prefix}fn.*')
+    if len(df_fn_dup) > 0:
+        write_masked_vcfs(df_fn_dup, f'{output_prefix}fn_dup.',
+                          indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'False negatives (duplicates) can be found in {output_prefix}fn_dup.*')
+    if len(df_skipped_truth) > 0:
+        write_masked_vcfs(df_skipped_truth, f'{output_prefix}skipped_truth.',
+                          indel_threshold, fasta_ref, command, not no_gzip)
+        print(f'Skipped truth variants can be found in {output_prefix}skipped_truth.*')
+
+    # Compute metrics
+    metrics_df = compute_metrics(df_tp, df_fp, df_fn, indel_threshold, window_radius, sv_size_bins)
+    # Write CSV with metrics
+    metrics_df.to_csv(f'{output_prefix}metrics.csv', index=False)
+
+    # Print info about metrics
+    print(f'True positives: {len(df_tp)} + {len(df_tp_dup)} duplicates')
+    print(f'False positives: {len(df_fp)} + {len(df_fp_dup)} duplicates')
+    print(f'False negatives: {len(df_fn)} + {len(df_fn_dup)} duplicates')
+    print(f'Total truth variants analyzed: {len(df_truth)} + {len(df_skipped_truth)} skipped')
+    print(f'Total test variants analyzed: {len(df_test)} + {len(df_skipped_test)} skipped')
+    print('Benchmark metrics:')
+    print(metrics_df.to_string(index=False))
+    print(f'Benchmark metrics can be found in {output_prefix}metrics.csv')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser('Oncoliner Assesment')
+    parser.add_argument('-t', '--truths', help='Path to the VCF truth files', nargs='+', required=True, type=str)
+    parser.add_argument('-v', '--tests', help='Path to the VCF test files', nargs='+', required=True, type=str)
+    parser.add_argument('-o', '--output_prefix',
+                        help='Prefix path for the output_prefix VCF files', required=True, type=str)
+    parser.add_argument('-f', '--fasta-ref', help='Path to reference FASTA file', required=True, type=str)
+    parser.add_argument('-it', '--indel-threshold',
+                        help=f'Indel threshold, inclusive (default={DEFAULT_INDEL_THRESHOLD})', default=DEFAULT_INDEL_THRESHOLD, type=int)
+    parser.add_argument('-wr', '--window-radius',
+                        help=f'Window ratio (default={DEFAULT_WINDOW_RADIUS})', default=DEFAULT_WINDOW_RADIUS, type=int)
+    parser.add_argument(
+        '--sv-size-bins', help=f'SV size bins for the output_prefix metrics (default={DEFAULT_SV_BINS})', nargs='+', default=DEFAULT_SV_BINS, type=int)
+    parser.add_argument(
+        '--contigs', help=f'Contigs to process (default={DEFAULT_CONTIGS})', nargs='+', default=DEFAULT_CONTIGS, type=str)
+    parser.add_argument('--keep-intermediates',
+                        help='Keep intermediate CSV/VCF files from input VCF files', action='store_true', default=False)
+    parser.add_argument('--no-gzip', help='Do not gzip output_prefix VCF files', action='store_true', default=False)
+    args = parser.parse_args()
+
+    # Convert everything to absolute paths
+    args.truths = [os.path.abspath(x) for x in args.truths]
+    args.tests = [os.path.abspath(x) for x in args.tests]
+    args.fasta_ref = os.path.abspath(args.fasta_ref)
+    args.output_prefix = os.path.abspath(args.output_prefix)
+
+    main(args.truths, args.tests, args.output_prefix, args.fasta_ref, args.indel_threshold,
+         args.window_radius, args.sv_size_bins, args.contigs, args.keep_intermediates, args.no_gzip)
diff --git a/oncoliner_assesment/src/indel_sv_converter.py b/oncoliner_assesment/src/indel_sv_converter.py
new file mode 100644
index 0000000..90b1b43
--- /dev/null
+++ b/oncoliner_assesment/src/indel_sv_converter.py
@@ -0,0 +1,102 @@
+import os
+import sys
+import pysam
+
+# Add vcf-ops to the path
+sys.path.insert(0, os.path.join(os.path.abspath(os.path.dirname(__file__)), '..', '..', 'vcf_ops', 'src'))
+
+from vcf_ops import VariantType  # noqa
+
+
+SV_REGEX = r'[\[\]<>.]'
+
+
+def _variant_record_obj(row, chrom_preffix):
+    original_obj = row['variant_record_obj']
+    new_contig = chrom_preffix + original_obj.contig.replace('chr', '')
+    new_obj = original_obj._replace(contig=new_contig, pos=row['start'], end=row['end'], ref=row['ref'], alt=row['alt'])
+    return new_obj
+
+
+def _get_chrom_preffix(fasta):
+    # Fix chr1 vs 1
+    chrom_preffix = ''
+    if fasta.references[0].startswith('chr'):
+        chrom_preffix = 'chr'
+    return chrom_preffix
+
+
+def convert_svs(variants_df, fasta_ref):
+    if len(variants_df) == 0:
+        return variants_df
+
+    fasta = pysam.FastaFile(fasta_ref)
+
+    # Fix chr1 vs 1
+    chrom_preffix = _get_chrom_preffix(fasta)
+
+    # Check all chromosomes are in the reference
+    for chrom in variants_df['start_chrom'].unique():
+        if chrom_preffix+chrom not in fasta.references:
+            raise ValueError(f'Chromosome {chrom} not in FASTA references {fasta.references}')
+
+    def convert_row(row):
+        # Only convert INS
+        # Ignore INS with <INS> in the ALF field
+        # Check if the INS is actually a DUP
+        # Search if the ALT sequence is repeated in the reference
+        if row['alt'] == fasta.fetch(chrom_preffix+row['start_chrom'], row['start']-1, row['start']+row['length']):
+            row['type_inferred'] = VariantType.DUP.name
+            # Change to bracket notation offsetting 1 from start
+            row['start'] += 1
+            row['length'] -= 1
+            row['end'] = row['start'] + row['length']
+            row['ref'] = row['alt'][1]
+            row['alt'] = f']{chrom_preffix+row["start_chrom"]}:{row["end"]}]{row["ref"]}'
+            row['brackets'] = ']N'
+            row['variant_record_obj'] = _variant_record_obj(row, chrom_preffix)
+        return row
+
+    variants_df.loc[
+        (variants_df['type_inferred'] == VariantType.INS.name) &
+        (variants_df['alt'] != '<INS>'), :].apply(convert_row, axis=1)
+    return variants_df
+
+
+def convert_indels(variants_df, fasta_ref):
+    if len(variants_df) == 0:
+        return variants_df
+
+    fasta = pysam.FastaFile(fasta_ref)
+
+    # Fix chr1 vs 1
+    chrom_preffix = _get_chrom_preffix(fasta)
+
+    # Check all chromosomes are in the reference
+    for chrom in variants_df['start_chrom'].unique():
+        if chrom_preffix+chrom not in fasta.references:
+            raise ValueError(f'Chromosome {chrom} not in FASTA references {fasta.references}')
+
+    def convert_row(row):
+        if row['type_inferred'] == VariantType.DEL.name:
+            new_ref = fasta.fetch(chrom_preffix+row['start_chrom'], row['start']-1, row['end']).upper()
+            new_alt = new_ref[0]
+            row['alt'] = new_alt
+            row['ref'] = new_ref
+            row['variant_record_obj'] = _variant_record_obj(row, chrom_preffix)
+        elif row['type_inferred'] == VariantType.DUP.name:
+            new_ref = fasta.fetch(chrom_preffix+row['start_chrom'], row['end']-1, row['end'])
+            new_alt = new_ref + fasta.fetch(chrom_preffix+row['start_chrom'], row['start']-1, row['end']-1).upper()
+            row['start'] = row['end']
+            row['alt'] = new_alt
+            row['ref'] = new_ref
+            row['variant_record_obj'] = _variant_record_obj(row, chrom_preffix)
+        else:
+            raise ValueError('Unknown variant type: ' + row['type_inferred'])
+        return row
+
+    variants_df.loc[
+        (variants_df['type_inferred'] != VariantType.INS.name) &
+        (variants_df['type_inferred'] != VariantType.SNV.name) &
+        (variants_df['alt'].str.contains(SV_REGEX)), :].apply(convert_row, axis=1)
+    return variants_df
diff --git a/vcf_ops/.gitignore b/vcf_ops/.gitignore
new file mode 100644
index 0000000..b6e4761
--- /dev/null
+++ b/vcf_ops/.gitignore
@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
diff --git a/vcf_ops/README.md b/vcf_ops/README.md
new file mode 100644
index 0000000..b74e495
--- /dev/null
+++ b/vcf_ops/README.md
@@ -0,0 +1 @@
+# vcf_ops
\ No newline at end of file
diff --git a/vcf_ops/data/genes_cancer.tsv b/vcf_ops/data/genes_cancer.tsv
new file mode 100644
index 0000000..6a385f3
--- /dev/null
+++ b/vcf_ops/data/genes_cancer.tsv
@@ -0,0 +1,620 @@
+SYMBOL
+ABCC4
+ABL1
+ABL2
+ACKR3
+ACSL3
+ACSL6
+ACSM3
+ACVR1
+ACVR1B
+ACVR2A
+AFDN
+AFF1
+AJUBA
+AKAP9
+AKT1
+AKT3
+ALDH2
+ALK
+ALPK1
+AMER1
+ANK1
+ANKRD11
+APC
+AR
+ARAF
+ARHGAP35
+ARHGAP5
+ARHGEF10
+ARHGEF10L
+ARHGEF12
+ARID1A
+ARID1B
+ARID2
+ARNT
+ASXL1
+ASXL2
+ATF7IP
+ATG7
+ATM
+ATP1A1
+ATR
+ATRX
+AXIN1
+AXIN2
+B2M
+BAP1
+BARD1
+BAZ1A
+BCL10
+BCL11A
+BCL11B
+BCL2
+BCL2L12
+BCL6
+BCL7A
+BCL9
+BCL9L
+BCLAF1
+BCOR
+BCORL1
+BCR
+BIRC3
+BIRC6
+BLM
+BMP6
+BMPR1A
+BRAF
+BRCA1
+BRCA2
+BRD4
+BRD7
+BTG1
+BTG2
+BTK
+CACNA1D
+CADM2
+CAMTA1
+CANT1
+CARD11
+CASP8
+CASZ1
+CBFB
+CBL
+CBLB
+CCDC6
+CCND1
+CCND2
+CCND3
+CCNE1
+CCR7
+CD209
+CD274
+CD58
+CD79B
+CDC73
+CDH1
+CDH10
+CDH11
+CDK12
+CDK4
+CDKN1A
+CDKN1B
+CDKN2A
+CDKN2C
+CDX2
+CEBPA
+CHD2
+CHD4
+CHEK2
+CHRDL1
+CIC
+CIITA
+CLIP1
+CLTC
+CLTCL1
+CMTR2
+CNOT3
+CNOT9
+CNTRL
+COL1A1
+COL2A1
+COL6A3
+COL6A6
+CPEB3
+CREBBP
+CREM
+CRNKL1
+CRTC1
+CSF1R
+CSF3R
+CSMD3
+CSPG4
+CTCF
+CTNNA2
+CTNNB1
+CTNND2
+CUL3
+CUX1
+CXCR4
+CYLD
+CYP2C8
+CYSLTR2
+DAXX
+DCAF12L2
+DCC
+DCSTAMP
+DDB2
+DDR2
+DDX3X
+DDX6
+DGCR8
+DHX9
+DICER1
+DIS3
+DNMT3A
+DOT1L
+DROSHA
+DSP
+DTX1
+DUSP16
+EBF1
+EFTUD2
+EGFR
+EGR2
+EIF1AX
+EIF3E
+EIF4A2
+ELF3
+ELF4
+ELL
+ELN
+EP300
+EPAS1
+EPHA2
+EPHA3
+EPHA7
+EPS15
+ERBB2
+ERBB3
+ERBB4
+ERCC2
+ERCC3
+ESR1
+ESRRA
+ETNK1
+ETV1
+ETV4
+ETV5
+ETV6
+EWSR1
+EZH2
+FAM135B
+FAM174B
+FANCC
+FANCD2
+FANCF
+FAS
+FAT1
+FAT2
+FAT3
+FAT4
+FBLN2
+FBN2
+FBXO11
+FBXW7
+FCGBP
+FCGR2B
+FCRL4
+FGD1
+FGD5
+FGFR1
+FGFR2
+FGFR3
+FGFR4
+FH
+FHIT
+FLCN
+FLT3
+FLT4
+FMN2
+FN1
+FOXA1
+FOXA2
+FOXL2
+FOXO1
+FOXO3
+FOXP1
+FUBP1
+GATA2
+GATA3
+GLI1
+GMPS
+GNA11
+GNA13
+GNAI2
+GNAQ
+GNAS
+GPAA1
+GPC5
+GRIN2A
+GRM3
+GTF2I
+H3-3A
+H3C2
+HCAR3
+HDAC4
+HIP1
+HLA-A
+HLA-B
+HLA-C
+HNF1A
+HNRNPA2B1
+HOXC13
+HRAS
+HSP90AA1
+HSP90AB1
+HSPG2
+ID3
+IDH1
+IDH2
+IKBKB
+IKZF1
+IKZF3
+IL6ST
+IL7R
+ING1
+INPPL1
+IRAK1
+IRF1
+IRF4
+IRS4
+ITGAV
+JAK1
+JAK2
+JAK3
+JUN
+KAT6A
+KAT6B
+KDM3B
+KDM5C
+KDM6A
+KDR
+KDSR
+KEAP1
+KIF5B
+KIT
+KLF4
+KLF5
+KLF6
+KLHL6
+KMT2A
+KMT2B
+KMT2C
+KMT2D
+KMT5A
+KNL1
+KNSTRN
+KRAS
+LATS1
+LATS2
+LDB1
+LEF1
+LIFR
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diff --git a/vcf_ops/data/genes_with_protein_product.tsv b/vcf_ops/data/genes_with_protein_product.tsv
new file mode 100644
index 0000000..03b031a
--- /dev/null
+++ b/vcf_ops/data/genes_with_protein_product.tsv
@@ -0,0 +1,20246 @@
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diff --git a/vcf_ops/setup.cfg b/vcf_ops/setup.cfg
new file mode 100644
index 0000000..6e7a831
--- /dev/null
+++ b/vcf_ops/setup.cfg
@@ -0,0 +1,7 @@
+[semantic_release]
+version_variable = src/vcf_ops/__init__.py:__version__
+branch = main
+
+[metadata]
+description-file=README.md
+license_files=LICENSE
diff --git a/vcf_ops/src/vcf_ops/__init__.py b/vcf_ops/src/vcf_ops/__init__.py
new file mode 100644
index 0000000..d6004ea
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/__init__.py
@@ -0,0 +1,9 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+
+# Export VariantType from VariantExtractor
+from variant_extractor.variants import VariantType
+
+__version__ = '0.0.1'
+__author__ = 'Rapsssito'
diff --git a/vcf_ops/src/vcf_ops/_internal/internal_ops.py b/vcf_ops/src/vcf_ops/_internal/internal_ops.py
new file mode 100644
index 0000000..eed6c62
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/_internal/internal_ops.py
@@ -0,0 +1,170 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+import pandas as pd
+
+
+def intersect_exact(df_truth, df_test, matching_fields):
+    # Find exact matches
+    df_all_tp = pd.merge(df_test.reset_index(), df_truth, how='inner', on=matching_fields,
+                         copy=False, suffixes=(None, '_truth')).set_index('index')
+    # Drop columns from truth
+    df_all_tp.drop([col for col in df_all_tp.columns if col.endswith('_truth') and 'variant_record_obj' not in col], axis=1, inplace=True)
+    # Find duplicates
+    df_tp_dup_mask = df_all_tp.duplicated(subset=matching_fields, keep='first')
+    df_tp_dup = df_all_tp[df_tp_dup_mask]
+    df_tp = df_all_tp[~df_tp_dup_mask]
+    # Find false positives
+    df_fp = df_test[~df_test.index.isin(df_all_tp.index)]
+    # Find duplicates
+    df_fp_dup_mask = df_fp.duplicated(subset=matching_fields, keep='first')
+    df_fp_dup = df_fp[df_fp_dup_mask]
+    df_fp = df_fp[~df_fp_dup_mask]
+    # Find false negatives
+    df_all_fn_inverse = pd.merge(df_truth.reset_index(), df_all_tp, how='inner',
+                                 on=matching_fields, copy=False, suffixes=(None, '_test')).set_index('index')
+    # Find duplicates
+    df_all_fn = df_truth[~df_truth.index.isin(df_all_fn_inverse.index)]
+    df_fn_dup_mask = df_all_fn.duplicated(subset=matching_fields, keep='first')
+    df_fn_dup = df_all_fn[df_fn_dup_mask]
+    df_fn = df_all_fn[~df_fn_dup_mask]
+    return df_tp, df_tp_dup, df_fp, df_fp_dup, df_fn, df_fn_dup
+
+
+def _entries_in_window(df_truth, df_test, matching_fields, window_fields, window_radius):
+    # Calculate window radius
+    curr_df_truth = df_truth.assign(window_radius=window_radius)
+    curr_df_test = df_test.assign(window_radius=window_radius)
+    # Necessary columns
+    necessary_cols = matching_fields + window_fields + ['window_radius']
+    # Drop unnecessary columns
+    curr_df_test = curr_df_test[necessary_cols]
+    curr_df_truth = curr_df_truth[necessary_cols]
+    # Reset index
+    curr_df_test.reset_index(inplace=True)
+    curr_df_truth.reset_index(inplace=True)
+
+    # Merge on matching fields
+    cross_merge = pd.merge(curr_df_truth, curr_df_test, suffixes=('_truth', '_test'),
+                           how='left', on=matching_fields, copy=False)
+    # Keep only entries that have matches
+    cross_merge.dropna(subset=['window_radius_test'], inplace=True)
+    # Drop entries that are not in the window
+    for window_field in window_fields:
+        window_field_test = window_field + '_test'
+        window_field_truth = window_field + '_truth'
+        diff = (cross_merge[window_field_truth].astype(int) - cross_merge[window_field_test].astype(int)).abs()
+        cross_merge = cross_merge[diff <= cross_merge['window_radius_truth']]
+    return cross_merge.drop(matching_fields + ['window_radius_truth', 'window_radius_test'], axis=1)
+
+
+def _matching_window_entries(df_truth, df_test, matching_fields, window_fields, window_radius):
+    # Get matching window entries
+    cross_merge = _entries_in_window(df_truth, df_test, matching_fields, window_fields, window_radius)
+    # Calculate window_fields_sum_diff
+    cross_merge['window_fields_sum_test'] = cross_merge[[w + '_test' for w in window_fields]].sum(axis=1)
+    cross_merge['window_fields_sum_truth'] = cross_merge[[w + '_truth' for w in window_fields]].sum(axis=1)
+    cross_merge['window_fields_sum_diff'] = (
+        cross_merge['window_fields_sum_test'] - cross_merge['window_fields_sum_truth']).abs()
+    # Sort by window_fields_sum_diff
+    cross_merge.sort_values(by='window_fields_sum_diff', inplace=True)
+    # Right now, a test entry can match multiple truth entries
+    # We want to match a test entry to only one truth entry
+    # A truth entry can still be matched to multiple test entries
+    # We want to leave the least amount of truth entries unmatched
+    # In the case of a tie, we want to select the test entry to the truth entry with the least window_fields_sum_diff
+    entries_to_keep = pd.Series(False, index=cross_merge.index)
+    curr_cross_merge = cross_merge
+    while True:
+        duplicated_truth_mask = curr_cross_merge.duplicated(subset=['index_truth'], keep=False)
+        unique_truth_df = curr_cross_merge[~duplicated_truth_mask]
+        duplicated_truth_df = curr_cross_merge[duplicated_truth_mask]
+        new_duplicated_truth_df = duplicated_truth_df[~duplicated_truth_df['index_test'].isin(
+            unique_truth_df['index_test'])]
+        entries_to_keep.loc[unique_truth_df.index.union(new_duplicated_truth_df.index)] = True
+        curr_cross_merge = curr_cross_merge.loc[unique_truth_df.index.union(new_duplicated_truth_df.index)]
+        if len(new_duplicated_truth_df) == len(duplicated_truth_df):
+            break
+    cross_merge = cross_merge[entries_to_keep]
+    # Remove test entries duplicates keeping the one with the least window_fields_sum_diff
+    selected_entries = pd.Series(False, index=cross_merge.index)
+    # Those test entries that are matched to an already matched truth entry are marked as duplicates
+    possible_duplications = dict()
+    already_selected_test_entries = set()
+    already_selected_truth_entries = set()
+    for idx, row in cross_merge.iterrows():
+        if row['index_test'] in already_selected_test_entries:
+            continue
+        if row['index_truth'] in already_selected_truth_entries:
+            possible_duplications.setdefault(row['index_test'], []).append(idx)
+            continue
+        already_selected_test_entries.add(row['index_test'])
+        already_selected_truth_entries.add(row['index_truth'])
+        selected_entries.loc[idx] = True  # type: ignore
+    duplicated_entries = pd.Series(False, index=cross_merge.index)
+    for index_test, duplicated_idxs in possible_duplications.items():
+        if index_test in already_selected_test_entries:
+            continue
+        duplicated_entries.loc[duplicated_idxs[0]] = True
+    selected_cross_merge = cross_merge[selected_entries]
+    duplicated_cross_merge = cross_merge[duplicated_entries]
+    df_tp_test = df_test.loc[selected_cross_merge['index_test']]
+    df_tp_dup_test = df_test.loc[duplicated_cross_merge['index_test']]
+    df_tp_truth = df_truth.loc[selected_cross_merge['index_truth'].unique()]
+    df_tp_test['variant_record_obj_truth'] = df_truth.loc[selected_cross_merge['index_truth']]['variant_record_obj'].values
+    return df_tp_test, df_tp_dup_test, df_tp_truth
+
+
+def _matching_window_duplicate_entries(df, matching_fields, window_fields, window_radius):
+    # Get matching window entries
+    cross_merge = _entries_in_window(df, df, matching_fields, window_fields, window_radius)
+    # Avoid returning the same entry
+    cross_merge = cross_merge[cross_merge['index_truth'] != cross_merge['index_test']]
+    # Calculate window_fields_sum_diff
+    cross_merge['window_fields_sum_test'] = cross_merge[[w + '_test' for w in window_fields]].sum(axis=1)
+    cross_merge['window_fields_sum_truth'] = cross_merge[[w + '_truth' for w in window_fields]].sum(axis=1)
+    cross_merge['window_fields_sum_diff'] = (
+        cross_merge['window_fields_sum_test'] - cross_merge['window_fields_sum_truth']).abs()
+    # Group by index_truth and get the count of index_test and sum of window_fields_sum_diff
+    grouped = cross_merge.groupby('index_truth', sort=False).agg(
+        {'index_test': 'count', 'window_fields_sum_diff': 'sum'})
+    grouped.rename(columns={'index_test': 'mate_count'}, inplace=True)
+    # Sort by mate_count and window_fields_sum_diff
+    grouped.sort_values(by=['mate_count', 'window_fields_sum_diff'], ascending=[False, True], inplace=True)
+    # The entries with the most mates are the non-duplicated ones
+    duplicated_entries = set()
+    while len(grouped) > 0:
+        mates_to_remove = cross_merge.loc[cross_merge['index_truth'] == grouped.index[0], 'index_test']
+        # Remove the selected entry and its mates
+        grouped.drop(index=grouped.index[0], inplace=True)
+        grouped.drop(index=mates_to_remove, inplace=True, errors='ignore')  # type: ignore
+        duplicated_entries.update(mates_to_remove.values)
+
+    # Get the original entries from the selected_truth_entries and the same_entry_mask
+    duplicated_entries = df.loc[list(duplicated_entries)]
+    original_entries = df.loc[df.index.difference(duplicated_entries.index)]
+    return original_entries, duplicated_entries
+
+
+def intersect_window(df_truth, df_test, matching_fields, window_fields, window_radius):
+    # Compute TP
+    df_tp, df_tp_dup_test, df_all_truth_tp = _matching_window_entries(
+        df_truth, df_test, matching_fields, window_fields, window_radius)
+    df_all_test_tp = df_test.loc[df_tp.index.union(df_tp_dup_test.index)]
+    # Find FP
+    df_all_fp = df_test.loc[~df_test.index.isin(df_all_test_tp.index)]
+    # Find duplicates in FP
+    df_fp, df_fp_dup = _matching_window_duplicate_entries(
+        df_all_fp, matching_fields, window_fields, window_radius)
+    # Find FN
+    df_all_fn = df_truth.drop(df_all_truth_tp.index)
+    # Find duplicates in TP from truth
+    df_1, df_2, df_tp_dup_truth = _matching_window_entries(
+        df_all_truth_tp, df_all_fn, matching_fields, window_fields, window_radius)
+    # Concat truth TP and truth TP duplicates
+    df_tp_dup = pd.concat([df_tp_dup_test, df_tp_dup_truth], ignore_index=True)
+    # Find duplicates in FN
+    df_all_fn = df_all_fn.drop(df_1.index.union(df_2.index))
+    df_fn, df_fn_dup = _matching_window_duplicate_entries(
+        df_all_fn, matching_fields, window_fields, window_radius)
+    return df_tp, df_tp_dup, df_fp, df_fp_dup, df_fn, df_fn_dup
diff --git a/vcf_ops/src/vcf_ops/constants.py b/vcf_ops/src/vcf_ops/constants.py
new file mode 100644
index 0000000..ec40f0a
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/constants.py
@@ -0,0 +1,7 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+DEFAULT_INDEL_THRESHOLD = 100
+DEFAULT_WINDOW_RADIUS = 100
+DEFAULT_SV_BINS = [500]
+DEFAULT_CONTIGS = [str(x) for x in range(1, 23)] + ['X', 'Y']
diff --git a/vcf_ops/src/vcf_ops/genes.py b/vcf_ops/src/vcf_ops/genes.py
new file mode 100644
index 0000000..3f70669
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/genes.py
@@ -0,0 +1,93 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+from typing import Set
+import os
+import pandas as pd
+
+GENE_SPLIT_SYMBOL = ';'
+ONCOLINER_INFO_FIELD_NAME = 'ONCOLINER_PROT_GENES'
+# Lazy load
+_PROTEIN_CODING_GENES = None
+_CANCER_CENSUS_GENES = None
+
+
+def _read_genes_file(genes_tsv_file_path: str):
+    df = pd.read_csv(genes_tsv_file_path, sep='\t')
+    # Lowercase all columns
+    df.columns = [col.lower() for col in df.columns]
+    # Get all the genes in the "symbol" column
+    return frozenset(df['symbol'].unique())
+
+
+def get_cancer_census_genes():
+    # Lazy load CANCER_CENSUS_GENES
+    global _CANCER_CENSUS_GENES
+    if _CANCER_CENSUS_GENES is None:
+        _CANCER_CENSUS_GENES = _read_genes_file(os.path.join(os.path.dirname(__file__), '..', '..', 'data', 'genes_cancer.tsv'))
+    return _CANCER_CENSUS_GENES
+
+
+def _extract_protein_affected_genes_from_oncoliner(variant_record_obj) -> Set[str]:
+    # Extract protein coding genes from ONCOLINER annotation
+    protein_affected_genes = set(variant_record_obj.info[ONCOLINER_INFO_FIELD_NAME])
+    return protein_affected_genes
+
+
+def _extract_protein_affected_genes_from_vep(variant_record_obj) -> Set[str]:
+    # Extract protein coding genes from VEP annotation
+    protein_affected_consequences = set(['stop_gained', 'frameshift_variant', 'stop_lost', 'start_lost', 'inframe_insertion',
+                                        'inframe_deletion', 'missense_variant', 'protein_altering_variant', 'coding_sequence_variant', 'coding_transcript_variant'])
+    protein_affected_genes = set()
+    for vep_annotation in variant_record_obj.info['CSQ']:
+        # Find an annotation with a protein affecting consequence
+        found = False
+        for ann in vep_annotation.split('|'):
+            if len(set(ann.split('&')).intersection(protein_affected_consequences)) > 0:
+                found = True
+                break
+        if not found:
+            continue
+        # Extract the gene symbol
+        for ann in vep_annotation.split('|'):
+            if ann in _PROTEIN_CODING_GENES:
+                protein_affected_genes.add(ann)
+    return protein_affected_genes
+
+
+def extract_protein_affected_genes(variant_record_obj) -> Set[str]:
+    # Load PROTEIN_CODING_GENES
+    global _PROTEIN_CODING_GENES
+    if _PROTEIN_CODING_GENES is None:
+        _PROTEIN_CODING_GENES = _read_genes_file(os.path.join(os.path.dirname(
+            __file__), '..', '..', 'data', 'genes_with_protein_product.tsv'))
+    # Check for ONCOLINER annotation
+    if _is_gene_annotated_in_oncoliner(variant_record_obj):
+        return _extract_protein_affected_genes_from_oncoliner(variant_record_obj)
+    # Check for VEP annotation
+    if _is_gene_annotated_in_vep(variant_record_obj):
+        return _extract_protein_affected_genes_from_vep(variant_record_obj)
+    return set()
+
+
+def combine_genes_symbols(genes_symbols_sets: pd.Series) -> Set[str]:
+    genes_symbols = set()
+    for genes_symbols_set in genes_symbols_sets:
+        genes_symbols = genes_symbols.union(genes_symbols_set)
+    return genes_symbols
+
+
+def add_gene_annotation_to_variant_record(variant_record_obj, genes_symbols: Set[str]):
+    variant_record_obj.info[ONCOLINER_INFO_FIELD_NAME] = list(genes_symbols)
+
+
+def _is_gene_annotated_in_vep(variant_record_obj) -> bool:
+    return 'CSQ' in variant_record_obj.info
+
+
+def _is_gene_annotated_in_oncoliner(variant_record_obj) -> bool:
+    return ONCOLINER_INFO_FIELD_NAME in variant_record_obj.info
+
+
+def is_gene_annotated(variant_record_obj) -> bool:
+    return _is_gene_annotated_in_oncoliner(variant_record_obj) or _is_gene_annotated_in_vep(variant_record_obj)
diff --git a/vcf_ops/src/vcf_ops/i_o.py b/vcf_ops/src/vcf_ops/i_o.py
new file mode 100644
index 0000000..5d5e762
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/i_o.py
@@ -0,0 +1,135 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+from typing import List, Tuple
+import gzip
+import pandas as pd
+import pysam
+
+from variant_extractor import VariantExtractor
+
+from .masks import snv_mask, indel_mask  # noqa
+
+
+def _extract_header(vcf_files: List[str]) -> Tuple[pysam.VariantHeader, bool]:
+    headers = [pysam.VariantFile(open(vcf_file)).header for vcf_file in vcf_files]
+    main_header = headers[0]
+    # Check if samples are the same in all VCFs
+    same_samples = True
+    for header in headers[1:]:
+        if header.samples.header != main_header.samples.header:
+            same_samples = False
+        main_header.merge(header)
+    return main_header, same_samples
+
+
+def _get_contig_order(header: pysam.VariantHeader, fasta_ref=None):
+    # Set contigs from fasta_ref
+    contig_order = dict()
+    if fasta_ref:
+        added_contigs = set()
+        with open(fasta_ref + '.fai') as f:
+            for i, line in enumerate(f):
+                chrom, length = line.split()[:2]
+                added_contigs.add(chrom)
+                contig_order[chrom.replace('chr', '')] = i
+                if chrom in header.contigs:
+                    continue
+                header.contigs.add(chrom, length=int(length))
+        for contig in header.contigs:
+            if contig not in added_contigs:
+                header.contigs.remove_header(contig)
+    else:
+        for i, contig in enumerate(header.contigs):
+            contig_order[str(contig).replace('chr', '')] = i
+    return contig_order
+
+
+def _write_raw_file(f, header: pysam.VariantHeader, variants_obj_df, same_samples: bool, same_formats: bool):
+    # Remove all format fields except GT for compatibility between VCFs
+    if not same_formats or not same_samples:
+        for format_ in header.formats:
+            if format_ != 'GT':
+                header.formats.remove_header(format_)
+    if 'GT' not in header.formats:
+        header.formats.add('GT', 1, 'String', 'Genotype')
+    header_str = str(header)
+    # Reset samples if they are not the same in all VCFs
+    if not same_samples:
+        header_pos = header_str.find('#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO')
+        custom_header_header = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tDUMMY_NORMAL\tDUMMY_TUMOR\n'
+        header_str = header_str[:header_pos] + custom_header_header
+    f.write(header_str)
+    # Dummy samples (in case samples are not the same in all VCFs)
+    dummy_samples = {'DUMMY_NORMAL': {'GT': (0, 0)}, 'DUMMY_TUMOR': {'GT': (0, 1)}}
+    # Write variants
+    for variant_record_obj in variants_obj_df:
+        # Reset samples if they are not the same in all VCFs
+        if not same_formats or not same_samples:
+            # Remove all format fields except GT for compatibility between VCFs
+            variant_record_obj.format = ['GT']
+        if not same_samples:
+            variant_record_obj.samples = dummy_samples
+        f.write(str(variant_record_obj) + '\n')
+
+
+def write_masked_vcfs(variants_df, output_path_prefix: str, indel_threshold: int, fasta_ref=None, command=None, gzip=True):
+    df_snv_mask = snv_mask(variants_df)
+    df_indel_mask = indel_mask(variants_df, indel_threshold)
+    df_snv = variants_df[df_snv_mask]
+    df_indel = variants_df[df_indel_mask]
+    df_sv = variants_df[~df_snv_mask & ~df_indel_mask]
+    for df_split, var_type in zip([df_snv, df_indel, df_sv], ['snv', 'indel', 'sv']):
+        if len(df_split) == 0:
+            continue
+        output_file_path = f'{output_path_prefix}{var_type}.vcf'
+        if gzip:
+            output_file_path += '.gz'
+        write_vcf(df_split, output_file_path, fasta_ref, command)
+
+
+def write_vcf(variants_df: pd.DataFrame, output_vcf, fasta_ref=None, command=None):
+    template_vcfs = list(variants_df['vcf_file'].unique())
+    header, same_samples = _extract_header(template_vcfs)
+    # Add meta field with the command used to generate the VCF
+    if command:
+        header.add_meta('vcf_ops', command)
+    # Get contig order
+    contig_order = _get_contig_order(header, fasta_ref)
+    variants_df = variants_df.copy()
+    # Add contig_order column
+    variants_df['contig_order'] = variants_df['start_chrom'].map(contig_order)
+    # Sort by chromosome and position
+    variants_df.sort_values(by=['contig_order', 'start'], inplace=True)
+    # Check if all variants have the same format fields
+    formats = variants_df.iloc[0]['variant_record_obj'].format
+    same_formats = True
+    if len(template_vcfs) > 1:
+        for variant_record_obj in variants_df['variant_record_obj']:
+            if variant_record_obj.format != formats:
+                same_formats = False
+
+    # Check if the VCF is gzipped
+    if output_vcf.endswith('.gz'):
+        f = gzip.open(output_vcf, 'wt')
+    else:
+        f = open(output_vcf, 'w')
+    # Write the file
+    _write_raw_file(f, header, variants_df['variant_record_obj'], same_samples, same_formats)
+    f.close()
+
+
+def _extract_variants(vcf_file) -> pd.DataFrame:
+    extractor = VariantExtractor(vcf_file, pass_only=True)
+    variants_df = extractor.to_dataframe()
+    variants_df['vcf_file'] = vcf_file
+    return variants_df
+
+
+def read_vcfs(vcf_files):
+    if len(vcf_files) == 0:
+        empty_df = VariantExtractor.empty_dataframe()
+        empty_df['vcf_file'] = ''
+        return empty_df
+    vcf_dfs = [_extract_variants(vcf_file) for vcf_file in vcf_files]
+    return pd.concat(vcf_dfs, ignore_index=True)
diff --git a/vcf_ops/src/vcf_ops/intersect.py b/vcf_ops/src/vcf_ops/intersect.py
new file mode 100644
index 0000000..c45ef08
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/intersect.py
@@ -0,0 +1,116 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+import pandas as pd
+
+from variant_extractor.variants import VariantType
+
+from ._internal.internal_ops import intersect_exact, intersect_window  # noqa
+from .masks import snv_mask, indel_mask  # noqa
+from .genes import is_gene_annotated, extract_protein_affected_genes, add_gene_annotation_to_variant_record
+
+
+def _combine_gene_annotations(df_tp: pd.DataFrame):
+    # Add gene annotations from the truth files if not present
+    # Check if the variant_record_obj is gene annotated in the test file
+    is_test_annotated = df_tp['variant_record_obj'].apply(is_gene_annotated)
+    if is_test_annotated.any():
+        return
+    # Check if the variant_record_obj is gene annotated in the truth file
+    is_truth_annotated = df_tp['variant_record_obj_truth'].apply(is_gene_annotated)
+    if not is_truth_annotated.any():
+        return
+    # Extract protein affected genes from the truth file
+    truth_protein_affected_genes = df_tp['variant_record_obj_truth'].apply(extract_protein_affected_genes)
+
+    # Add gene annotations to the test file
+    for idx, row in df_tp[is_truth_annotated].iterrows():
+        add_gene_annotation_to_variant_record(row['variant_record_obj'], truth_protein_affected_genes[idx])
+
+
+def intersect(df_truth, df_test, indel_threshold, window_radius, combine_gene_annotations=True):
+    # Intersect for SNV and indels comparing position, alt, length and type
+    snv_indel_truth_mask = snv_mask(df_truth) | indel_mask(df_truth, indel_threshold)
+    snv_indel_test_mask = snv_mask(df_test) | indel_mask(df_test, indel_threshold)
+    snv_indel_truth = df_truth[snv_indel_truth_mask]
+    snv_indel_test = df_test[snv_indel_test_mask]
+    snv_indel_tp, snv_indel_tp_dup, snv_indel_fp, snv_indel_fp_dup, snv_indel_fn, snv_indel_fn_dup = \
+        intersect_exact(snv_indel_truth, snv_indel_test, ['start_chrom', 'start', 'alt', 'length', 'type_inferred'])
+    # Remove from the rest of the benchmark
+    df_truth = df_truth[~snv_indel_truth_mask]
+    df_test = df_test[~snv_indel_test_mask]
+
+    # Benchmark INS comparing start position and length
+    ins_truth_mask = df_truth['type_inferred'] == VariantType.INS.name
+    ins_test_mask = df_test['type_inferred'] == VariantType.INS.name
+    ins_truth = df_truth[ins_truth_mask]
+    ins_test = df_test[ins_test_mask]
+    ins_tp, ins_tp_dup,\
+        ins_fp, ins_fp_dup,\
+        ins_fn, ins_fn_dup = intersect_window(ins_truth, ins_test, ['start_chrom'],
+                                              ['start', 'length'], window_radius)
+    # Remove from the rest of the benchmark
+    df_truth = df_truth[~ins_truth_mask]
+    df_test = df_test[~ins_test_mask]
+
+    # Benchmark rest of SVs comparing start and end positions
+    sv_tp, sv_tp_dup,\
+        sv_fp, sv_fp_dup,\
+        sv_fn, sv_fn_dup = intersect_window(df_truth, df_test, ['start_chrom', 'end_chrom', 'brackets'],
+                                            ['start', 'end'], window_radius)
+
+    # Concatenate all results
+    df_tp = pd.concat([snv_indel_tp, ins_tp, sv_tp])
+    df_tp_dup = pd.concat([snv_indel_tp_dup, ins_tp_dup, sv_tp_dup])
+    df_fp = pd.concat([snv_indel_fp, ins_fp, sv_fp])
+    df_fp_dup = pd.concat([snv_indel_fp_dup, ins_fp_dup, sv_fp_dup])
+    df_fn = pd.concat([snv_indel_fn, ins_fn, sv_fn])
+    df_fn_dup = pd.concat([snv_indel_fn_dup, ins_fn_dup, sv_fn_dup])
+
+    if combine_gene_annotations:
+        _combine_gene_annotations(df_tp)
+
+    return df_tp, df_tp_dup, df_fp, df_fp_dup, df_fn, df_fn_dup
+
+
+if __name__ == '__main__':
+    import argparse
+    from .i_o import read_vcfs, write_masked_vcfs
+    from .constants import DEFAULT_INDEL_THRESHOLD, DEFAULT_WINDOW_RADIUS
+
+    parser = argparse.ArgumentParser(description='Intersect two sets of VCF/BCF/VCF.GZ files')
+    parser.add_argument('--files-1', nargs='+', required=True, help='VCF/BCF/VCF.GZ files from the first set')
+    parser.add_argument('--files-2', nargs='+', required=True, help='VCF/BCF/VCF.GZ files from the second set')
+    parser.add_argument('-o', '--output', help='Prefix path for the output VCF files', required=True, type=str)
+    parser.add_argument('-it', '--indel-threshold',
+                        help=f'Indel threshold, inclusive (default={DEFAULT_INDEL_THRESHOLD})', default=DEFAULT_INDEL_THRESHOLD, type=int)
+    parser.add_argument('-wr', '--window-radius',
+                        help=f'Window radius (default={DEFAULT_WINDOW_RADIUS})', default=DEFAULT_WINDOW_RADIUS, type=float)
+    args = parser.parse_args()
+
+    # Read the input files
+    df_truth = read_vcfs(args.files_1)
+    df_test = read_vcfs(args.files_2)
+
+    # Intersect
+    df_tp, df_tp_dup, df_fp, df_fp_dup, df_fn, df_fn_dup = intersect(df_truth, df_test, args.indel_threshold, args.window_radius)
+
+    # Write VCF files
+    if len(df_tp) > 0:
+        write_masked_vcfs(df_tp, args.output + 'intersect_in.', args.test_files, args.indel_threshold)
+        print(f'True positives can be found in {args.output}intersect_in.*')
+    if len(df_tp_dup) > 0:
+        write_masked_vcfs(df_tp_dup, args.output + 'intersect_in_dup.', args.test_files, args.indel_threshold)
+        print(f'True positives with duplicates can be found in {args.output}intersect_in_dup.*')
+    if len(df_fp) > 0:
+        write_masked_vcfs(df_fp, args.output + 'intersect_out_2.', args.test_files, args.indel_threshold)
+        print(f'False positives can be found in {args.output}intersect_out_2.*')
+    if len(df_fp_dup) > 0:
+        write_masked_vcfs(df_fp_dup, args.output + 'intersect_out_2_dup.', args.test_files, args.indel_threshold)
+        print(f'False positives with duplicates can be found in {args.output}intersect_out_2_dup.*')
+    if len(df_fn) > 0:
+        write_masked_vcfs(df_fn, args.output + 'intersect_out_1.', args.truth_files, args.indel_threshold)
+        print(f'False negatives can be found in {args.output}intersect_out_1.*')
+    if len(df_fn_dup) > 0:
+        write_masked_vcfs(df_fn_dup, args.output + 'intersect_out_1_dup.', args.truth_files, args.indel_threshold)
+        print(f'False negatives with duplicates can be found in {args.output}intersect_out_1_dup.*')
diff --git a/vcf_ops/src/vcf_ops/masks.py b/vcf_ops/src/vcf_ops/masks.py
new file mode 100644
index 0000000..ad189cb
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/masks.py
@@ -0,0 +1,14 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+from variant_extractor.variants import VariantType
+
+
+def snv_mask(df):
+    return df['type_inferred'] == VariantType.SNV.name
+
+
+def indel_mask(df, indel_threshold):
+    return (df['length'] > 0) & (df['length'] <= indel_threshold) & (df['alt'] != '<INS>') & \
+        (df['type_inferred'] != VariantType.SNV.name) & \
+        (df['type_inferred'] != VariantType.INV.name) & (df['type_inferred'] != VariantType.TRA.name)
diff --git a/vcf_ops/src/vcf_ops/metrics.py b/vcf_ops/src/vcf_ops/metrics.py
new file mode 100644
index 0000000..517238e
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/metrics.py
@@ -0,0 +1,219 @@
+import pandas as pd
+from functools import reduce
+
+from variant_extractor.variants import VariantType
+
+from .genes import combine_genes_symbols, get_cancer_census_genes, is_gene_annotated, extract_protein_affected_genes, GENE_SPLIT_SYMBOL
+
+METRICS_COLUMNS = ['variant_type', 'variant_size', 'window_radius', 'recall', 'precision', 'f1_score', 'tp', 'fp', 'fn',
+                   'protein_affected_genes_count', 'protein_affected_driver_genes_count', 'protein_affected_genes', 'protein_affected_driver_genes']
+
+
+def infer_parameters_from_metrics(metrics, window_radius=None):
+    # Infer indel threshold
+    indel_threshold = int(metrics[metrics['variant_type'] == 'INDEL']['variant_size'].iloc[0].split('-')[-1])
+    # Infer window ratio from INV
+    if window_radius is None:
+        window_radius = metrics[metrics['variant_type'] == 'INV']['window_radius'].iloc[1]
+    # Infer sv_size_bins from INV
+    sv_size_bins = []
+    for _, row in metrics[metrics['variant_type'] == 'INV'].iloc[2:-1].iterrows():
+        _, size_2 = row['variant_size'].split('-')
+        sv_size_bins.append(int(size_2))
+    return indel_threshold, window_radius, sv_size_bins
+
+
+def aggregate_metrics(metrics_list):
+    concat_metrics = pd.concat(metrics_list, ignore_index=True)
+    # Aggregate metrics
+    # Convert to set (if not NaN)
+    concat_metrics['protein_affected_genes_sets'] = concat_metrics['protein_affected_genes'].apply(
+        lambda x: set(x.split(GENE_SPLIT_SYMBOL) if not pd.isna(x) else []))
+    agg_metrics = concat_metrics.groupby(['variant_type', 'variant_size', 'window_radius'], sort=False).agg(
+        tp=pd.NamedAgg(column='tp', aggfunc='sum'),
+        fp=pd.NamedAgg(column='fp', aggfunc='sum'),
+        fn=pd.NamedAgg(column='fn', aggfunc='sum'),
+        # Coding genes are splitted by GENE_SPLIT_SYMBOL, create a set of all coding genes and then join them again
+        protein_affected_genes_count=pd.NamedAgg(column='protein_affected_genes_count', aggfunc='sum'),
+        protein_affected_driver_genes_count=pd.NamedAgg(column='protein_affected_driver_genes_count', aggfunc='sum'),
+        protein_affected_genes=pd.NamedAgg(column='protein_affected_genes_sets', aggfunc=combine_genes_symbols),
+    ).reset_index()
+    agg_metrics['recall'] = agg_metrics['tp'] / (agg_metrics['tp'] + agg_metrics['fn'])
+    agg_metrics['precision'] = agg_metrics['tp'] / (agg_metrics['tp'] + agg_metrics['fp'])
+    agg_metrics['f1_score'] = 2 * agg_metrics['precision'] * \
+        agg_metrics['recall'] / (agg_metrics['precision'] + agg_metrics['recall'])
+    # Fill NaNs with 0
+    agg_metrics['recall'].fillna(0, inplace=True)
+    agg_metrics['precision'].fillna(0, inplace=True)
+    agg_metrics['f1_score'].fillna(0, inplace=True)
+    # Add protein_affected_driver_genes
+    agg_metrics['protein_affected_driver_genes'] = agg_metrics['protein_affected_genes'].apply(
+        lambda x: GENE_SPLIT_SYMBOL.join(x.intersection(get_cancer_census_genes())))
+    # Convert to string
+    agg_metrics['protein_affected_genes'] = agg_metrics['protein_affected_genes'].apply(lambda x: GENE_SPLIT_SYMBOL.join(x))
+    # Reorder columns
+    agg_metrics = agg_metrics[METRICS_COLUMNS]
+    return agg_metrics
+
+
+def combine_precision_recall_metrics(recall_df, precision_df):
+    df = pd.DataFrame()
+    df[precision_df.columns] = precision_df[precision_df.columns]
+    df['recall'] = recall_df['recall']
+    df['tp'] = recall_df['tp']
+    df['fn'] = recall_df['fn']
+    df['precision'] = precision_df['precision']
+    df['fp'] = precision_df['fp']
+    # Calculate F1 score
+    df['f1_score'] = 2 * df['precision'] * df['recall'] / (df['precision'] + df['recall'])
+    # Fill NaNs in f1_score with 0
+    df['f1_score'].fillna(0, inplace=True)
+    df['protein_affected_genes_count'] = recall_df['protein_affected_genes_count']
+    df['protein_affected_driver_genes_count'] = recall_df['protein_affected_driver_genes_count']
+    df['protein_affected_genes'] = recall_df['protein_affected_genes']
+    df['protein_affected_driver_genes'] = recall_df['protein_affected_driver_genes']
+    return df
+
+
+def compute_metrics(df_tp, df_fp, df_fn, indel_threshold, window_radius, sv_size_bins):
+    # Add temporal benchmark column
+    df_tp['benchmark'] = 'TP'
+    df_fp['benchmark'] = 'FP'
+    df_fn['benchmark'] = 'FN'
+
+    df = pd.concat([df_tp, df_fp, df_fn], ignore_index=True)
+
+    # Remove temporal benchmark column
+    df_tp.drop('benchmark', axis=1, inplace=True)
+    df_fp.drop('benchmark', axis=1, inplace=True)
+    df_fn.drop('benchmark', axis=1, inplace=True)
+
+    # Setup bin names
+    bin_names = [VariantType.SNV.name, 'INDEL', VariantType.INS.name + ' / ' + VariantType.DUP.name, VariantType.DEL.name] + \
+        ['SV'] + [VariantType.TRA.name] + \
+        [VariantType.INV.name] * (len(sv_size_bins) + 3) + \
+        [VariantType.INS.name] * (len(sv_size_bins) + 2) + \
+        [VariantType.DEL.name] * (len(sv_size_bins) + 2) + \
+        [VariantType.DUP.name] * (len(sv_size_bins) + 2)
+
+    # Setup bin sizes names
+    repeated_bin_sizes = []
+    for i in range(len(sv_size_bins) + 1):
+        if i == 0:
+            size_text = f'{indel_threshold+1} - {sv_size_bins[i]}'
+        elif i == len(sv_size_bins):
+            size_text = f'> {sv_size_bins[i-1]}'
+        else:
+            size_text = f'{sv_size_bins[i-1]+1} - {sv_size_bins[i]}'
+        repeated_bin_sizes.append(size_text)
+    bin_sizes_names = ['1'] + [f'1 - {indel_threshold}'] * 3 + \
+        ['ALL', VariantType.TRA.name] + \
+        ['> 0', f'1 - {indel_threshold}'] + repeated_bin_sizes + \
+        [f'> {indel_threshold}'] + repeated_bin_sizes + \
+        [f'> {indel_threshold}'] + repeated_bin_sizes + \
+        [f'> {indel_threshold}'] + repeated_bin_sizes
+
+    # Setup window sizes names
+    window_radius_names = []
+    for i in range(len(bin_sizes_names)):
+        if bin_names[i] == 'SNV' or bin_names[i] == 'INDEL':
+            window_radius_names.append('0')
+        elif bin_sizes_names[i].split('-')[0].strip() == '1' and ('INS' in bin_names[i] or 'DEL' in bin_names[i]):
+            window_radius_names.append('0')
+        else:
+            window_radius_names.append(str(window_radius))
+
+    # Setup masks
+    masks = []
+    for i in range(len(bin_names)):
+        if bin_names[i] == 'INDEL' or bin_names[i] == 'SV':
+            masks.append(None)  # Skip indels and SVs, fill in later
+            continue
+        curr_names = bin_names[i].split('/')
+        curr_mask = df['type_inferred'] == curr_names[0].strip()
+        for name in curr_names[1:]:
+            curr_mask = curr_mask | (df['type_inferred'] == name.strip())
+
+        curr_sizes = bin_sizes_names[i].split('-')
+        if '>' in curr_sizes[0]:
+            curr_mask = curr_mask & (df['length'] > int(curr_sizes[0].replace('>', '').strip()))
+        elif len(curr_sizes) == 2:
+            curr_mask = curr_mask & (df['length'] >= int(curr_sizes[0].strip())) & \
+                (df['length'] <= int(curr_sizes[1].strip()))
+        masks.append(curr_mask)
+    # Fill INDEL mask with the existing masks
+    masks[1] = masks[2] | masks[3]
+    # Fill SV mask with the existing masks
+    masks[4] = reduce(lambda x, y: x | y, masks[6:], masks[5])
+
+    rows = []
+    for i in range(len(bin_names)):
+        df_bin = df[masks[i]]
+        row = []
+        row.append(bin_names[i])
+        row.append(bin_sizes_names[i])
+        row.append(window_radius_names[i])
+        tp_df = df_bin[df_bin['benchmark'] == 'TP']
+        fp_df = df_bin[df_bin['benchmark'] == 'FP']
+        fn_df = df_bin[df_bin['benchmark'] == 'FN']
+        tp = len(tp_df)
+        fp = len(fp_df)
+        fn = len(fn_df)
+        recall = tp / (tp + fn) if tp + fn > 0 else 0
+        precision = tp / (tp + fp) if tp + fp > 0 else 0
+        f1 = 2 * recall * precision / (recall + precision) if recall + precision > 0 else 0
+        row.append(recall)
+        row.append(precision)
+        row.append(f1)
+        row.append(tp)
+        row.append(fp)
+        row.append(fn)
+        # Check if the variant_record_obj is gene annotated in the test file
+        is_test_annotated = tp_df['variant_record_obj'].apply(is_gene_annotated)
+        protein_affected_genes = set()
+        protein_affected_driver_genes = set()
+        if is_test_annotated.any():
+            protein_affected_genes = combine_genes_symbols(tp_df['variant_record_obj'].apply(extract_protein_affected_genes))
+            protein_affected_driver_genes = protein_affected_genes & get_cancer_census_genes()
+        row.append(len(protein_affected_genes))
+        row.append(len(protein_affected_driver_genes))
+        row.append(GENE_SPLIT_SYMBOL.join(protein_affected_genes))
+        row.append(GENE_SPLIT_SYMBOL.join(protein_affected_driver_genes))
+        rows.append(row)
+
+    return pd.DataFrame(rows, columns=METRICS_COLUMNS)
+
+
+if __name__ == '__main__':
+    import argparse
+    from .i_o import read_vcfs
+    from .constants import DEFAULT_INDEL_THRESHOLD, DEFAULT_SV_BINS, DEFAULT_WINDOW_RATIO, DEFAULT_WINDOW_LIMIT
+
+    parser = argparse.ArgumentParser(description='Compute metrics from VCF files')
+    parser.add_argument('-tp', type=str, nargs='*', default=[], help='True Positives VCF files')
+    parser.add_argument('-fp', type=str, nargs='*', default=[], help='False Positives VCF files')
+    parser.add_argument('-fn', type=str, nargs='*', default=[], help='False Negatives VCF files')
+    parser.add_argument('-o', '--output', required=True, type=str, help='Output CSV file')
+    parser.add_argument('-it', '--indel-threshold',
+                        help=f'Indel threshold, inclusive (default={DEFAULT_INDEL_THRESHOLD})', default=DEFAULT_INDEL_THRESHOLD, type=int)
+    parser.add_argument('-wr', '--window-ratio',
+                        help=f'Window ratio used for the evaluation (default={DEFAULT_WINDOW_RATIO})', default=DEFAULT_WINDOW_RATIO, type=float)
+    parser.add_argument('-wl', '--window-limit',
+                        help=f'Window limit used for the evaluation (default={DEFAULT_WINDOW_LIMIT})', default=DEFAULT_WINDOW_LIMIT, type=int)
+    parser.add_argument(
+        '--sv-size-bins', help=f'SV size bins for the output metrics (default={DEFAULT_SV_BINS})', nargs='+', default=DEFAULT_SV_BINS, type=int)
+
+    args = parser.parse_args()
+    # Read VCFs
+    df_tp = read_vcfs(args.tp) if len(args.tp) > 0 else pd.DataFrame()
+    df_fp = read_vcfs(args.fp) if len(args.fp) > 0 else pd.DataFrame()
+    df_fn = read_vcfs(args.fn) if len(args.fn) > 0 else pd.DataFrame()
+
+    # Compute metrics
+    metrics_df = compute_metrics(df_tp, df_fp, df_fn, args.indel_threshold,
+                                 args.window_ratio, args.window_limit, args.sv_size_bins)
+    # Write to file
+    metrics_df.to_csv(args.output, index=False)
+
+    # Print metrics
+    print(metrics_df.to_string(index=False))
diff --git a/vcf_ops/src/vcf_ops/union.py b/vcf_ops/src/vcf_ops/union.py
new file mode 100644
index 0000000..7e4c42d
--- /dev/null
+++ b/vcf_ops/src/vcf_ops/union.py
@@ -0,0 +1,49 @@
+# Copyright 2023 - Barcelona Supercomputing Center
+# Author: Rodrigo Martín
+# BSC Dual License
+import pandas as pd
+
+from .intersect import intersect
+
+
+def union(df_truth, df_test, indel_threshold, window_radius):
+    # Intersect
+    df_tp, df_tp_dup, df_fp, df_fp_dup, df_fn, df_fn_dup = intersect(
+        df_truth, df_test, indel_threshold, window_radius)
+
+    # Union
+    df_union = pd.concat([df_tp, df_fp, df_fn])
+    df_union_dup = pd.concat([df_tp_dup, df_fp_dup, df_fn_dup])
+
+    return df_union, df_union_dup
+
+
+if __name__ == '__main__':
+    import argparse
+    from .i_o import read_vcfs, write_masked_vcfs
+    from .constants import DEFAULT_INDEL_THRESHOLD, DEFAULT_WINDOW_RADIUS
+
+    parser = argparse.ArgumentParser(description='Union two sets of VCF/BCF/VCF.GZ files')
+    parser.add_argument('--truth-files', nargs='+', required=True, help='VCF/BCF/VCF.GZ files to use as truth')
+    parser.add_argument('--test-files', nargs='+', required=True, help='VCF/BCF/VCF.GZ files to use as test')
+    parser.add_argument('-o', '--output', help='Prefix path for the output VCF files', required=True, type=str)
+    parser.add_argument('-it', '--indel-threshold',
+                        help=f'Indel threshold, inclusive (default={DEFAULT_INDEL_THRESHOLD})', default=DEFAULT_INDEL_THRESHOLD, type=int)
+    parser.add_argument('-wr', '--window-radius',
+                        help=f'Window radius (default={DEFAULT_WINDOW_RADIUS})', default=DEFAULT_WINDOW_RADIUS, type=int)
+    args = parser.parse_args()
+
+    # Read the input files
+    df_truth = read_vcfs(args.truth_files)
+    df_test = read_vcfs(args.test_files)
+
+    # Union
+    df_union, df_union_dup = union(df_truth, df_test, args.indel_threshold, args.window_ratio, args.window_limit)
+
+    # Write VCF files
+    if len(df_union) > 0:
+        write_masked_vcfs(df_union, args.output + 'union.', args.test_files, args.indel_threshold)
+        print(f'Union VCF file written to {args.output}union.*')
+    if len(df_union_dup):
+        write_masked_vcfs(df_union_dup, args.output + 'union_dup.', args.test_files, args.indel_threshold)
+        print(f'Union duplicate VCF file written to {args.output}union_dup.*')