diff --git a/.github/workflows/kinship_lint.yml b/.github/workflows/kinship_lint.yml
new file mode 100644
index 0000000..3e109cc
--- /dev/null
+++ b/.github/workflows/kinship_lint.yml
@@ -0,0 +1,17 @@
+name: Kinship Lint
+on:
+  pull_request:
+    paths: [Kinship/**]
+  push:
+    branches: [master, develop]
+    paths: [Kinship/**]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: Kinship/
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1
diff --git a/.github/workflows/kinship_test.yml b/.github/workflows/kinship_test.yml
new file mode 100644
index 0000000..25acb4e
--- /dev/null
+++ b/.github/workflows/kinship_test.yml
@@ -0,0 +1,63 @@
+# Source: https://github.com/marketplace/actions/install-poetry-action
+name: Kinship Test
+on:
+  pull_request:
+    paths: [Kinship/**]
+  push:
+    branches: [master, develop]
+    paths: [Kinship/**]
+
+jobs:
+  pytest:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: Kinship/
+    steps:
+      #----------------------------------------------
+      # check-out repo and set-up python
+      #----------------------------------------------
+      - name: Check out repository
+        uses: actions/checkout@v4
+      - name: Set up python
+        id: setup-python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11.5'
+      #----------------------------------------------
+      # install & configure poetry
+      #----------------------------------------------
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.8.3
+          virtualenvs-create: true
+          virtualenvs-in-project: true
+          installer-parallel: true
+
+      #----------------------------------------------
+      # load cached venv if cache exists
+      #----------------------------------------------
+      - name: Load cached venv
+        id: cached-poetry-dependencies
+        uses: actions/cache@v4
+        with:
+          path: .venv
+          key: venv_kinship-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
+      #----------------------------------------------
+      # install dependencies if cache does not exist
+      #----------------------------------------------
+      - name: Install dependencies
+        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        run: poetry install --no-interaction --no-root
+      #----------------------------------------------
+      # install root project
+      #----------------------------------------------
+      - name: Install project
+        run: poetry install --no-interaction
+      #----------------------------------------------
+      # run pytest
+      #----------------------------------------------
+      - name: Run tests
+        run: |
+          poetry run pytest .
diff --git a/Kinship/Dockerfile b/Kinship/Dockerfile
index c626fb2..0a9db8a 100644
--- a/Kinship/Dockerfile
+++ b/Kinship/Dockerfile
@@ -1,10 +1,44 @@
-FROM --platform=linux/amd64 continuumio/miniconda3
+################## BASE IMAGEs ######################
+FROM continuumio/miniconda3:24.5.0-0
 
-LABEL base_image="continuumio/miniconda3"
-LABEL version="1.0.0"
-LABEL extra.binaries="vcftools, plink, king"
+################## METADATA ######################
+LABEL base_image="continuumio/miniconda3:24.5.0-0"
+LABEL version="1.1.1"
+LABEL extra.binaries="vcftools, plink, king, pandas"
 
+################## INSTALLATIONS ######################
+# Use conda to install other dependencies.
 RUN conda config --add channels bioconda && \
     conda config --add channels conda-forge && \
     conda install -y vcftools=0.1.14 plink=1.90b4 king=2.2.7
 
+# Use poetry to install virtualenv.
+ENV POETRY_VERSION=1.8.3
+ENV POETRY_HOME=/opt/poetry
+ENV POETRY_VENV=/opt/poetry-venv
+
+# Tell Poetry where to place its cache and virtual environment
+ENV POETRY_CACHE_DIR=/tmp/poetry_cache
+
+# Do not ask any interactive question
+ENV POETRY_NO_INTERACTION=1
+
+# Make poetry create the virtual environment in the project's root
+# it gets named `.venv`
+ENV POETRY_VIRTUALENVS_IN_PROJECT=1
+ENV POETRY_VIRTUALENVS_CREATE=1
+
+# Set virtual_env variable
+ENV VIRTUAL_ENV=/.venv
+# Prepend virtual environments path
+ENV PATH="${VIRTUAL_ENV}/bin:${POETRY_VENV}/bin:${PATH}"
+
+# Creating a virtual environment just for poetry and install it with pip
+RUN python3 -m venv $POETRY_VENV \
+	&& $POETRY_VENV/bin/pip3 install poetry==${POETRY_VERSION}
+
+# Copy project requirement files here to ensure they will be cached.
+COPY pyproject.toml poetry.lock ./
+
+# Install dependencies.
+RUN --mount=type=cache,target=$POETRY_CACHE_DIR poetry install --without dev --no-interaction --no-cache
diff --git a/Kinship/Kinship.nf b/Kinship/Kinship.nf
index 9241dbf..3e670b0 100644
--- a/Kinship/Kinship.nf
+++ b/Kinship/Kinship.nf
@@ -1,7 +1,8 @@
 process Kinship {
     tag {"Kinship ${analysis_id}"}
     label 'Kinship'
-    container = 'docker.io/umcugenbioinf/kinship:1.0.0'
+    container = 'ghcr.io/umcugenetics/kinship:1.1.1'
+
     shell = ['/bin/bash', '-euo', 'pipefail']
 
     input:
@@ -17,6 +18,6 @@ process Kinship {
         plink --file out --make-bed --noweb
         king -b plink.bed --kinship
         cp king.kin0 ${analysis_id}.kinship
-        python ${projectDir}/CustomModules/Kinship/check_kinship.py ${analysis_id}.kinship ${ped_file} > ${analysis_id}.kinship_check.out
+        python ${projectDir}/CustomModules/Kinship/check_kinship.py ${analysis_id}.kinship ${ped_file} --output_prefix ${analysis_id} --output_path .
         """
-}
\ No newline at end of file
+}
diff --git a/Kinship/check_kinship.py b/Kinship/check_kinship.py
index 82c23dc..0c9f4cf 100644
--- a/Kinship/check_kinship.py
+++ b/Kinship/check_kinship.py
@@ -1,97 +1,250 @@
 #! /usr/bin/env python
+# Import statements, alphabetic order of main package.
 import argparse
+from errno import ENOENT as errno_ENOENT
+from os import strerror as os_strerror
+from pathlib import Path
+from sys import argv, exit
+import tempfile
+
+# Third party libraries alphabetic order of main package.
+from pandas import read_table
+
+
+def validate_non_empty_existing_path(file_or_dir):
+    """
+    This function checks whether the provided file or dir exists and is not empty.
+
+    Args:
+        file_or_dir (string): Input file or directory
+
+    Raises:
+        FileNotFoundError: If input string file_or_dir is neither a file nor a dir.
+        OSError: If input is not a dir and file is empty.
+
+    Returns:
+        string: Provided input file or directory. If dir, suffix '/' might be added.
+    """
+    input_path = Path(file_or_dir)
+    if not input_path.is_file() and not input_path.is_dir():
+        raise FileNotFoundError(errno_ENOENT, os_strerror(errno_ENOENT), file_or_dir)
+    elif not input_path.is_dir() and not input_path.stat().st_size:
+        raise OSError(f"File {file_or_dir} is empty.")
+    elif input_path.is_dir() and file_or_dir[::-1][0] != '/':
+        return f"{file_or_dir}/"
+    else:
+        return file_or_dir
+
+
+def parse_arguments_and_check(args_in):
+    """
+    Parses arguments and validates / checks format of input.
+
+    Args:
+        args_in (list of strings): Commandline input arguments.
+
+    Returns:
+        Namespace: Convert argument strings to objects and assign them as attributes of the namespace.
+    """
+    parser = argparse.ArgumentParser(description='Check kinship output based on ped file.')
+    parser.add_argument('kinship_file', type=validate_non_empty_existing_path, help='Kinship file')
+    parser.add_argument('ped_file', type=validate_non_empty_existing_path, help='PED file')
+    parser.add_argument(
+        '-p', '--output_path', type=validate_non_empty_existing_path, default=None,
+        help='Kinship output path where output file will be stored.',
+    )
+    parser.add_argument(
+        '-o', '--output_prefix', type=str, default=None,
+        help='Kinship output prefix for all output file names.'
+    )
+    parser.add_argument(
+        '-s', '--kinship_settings', type=float, nargs=2, metavar=('minimum', 'maximum'), default=[0.177, 0.354],
+        help='Kinship settings defining minimum and maximum threshold.'
+    )
+    arguments = parser.parse_args(args_in)
+    return arguments
 
 
 def parse_ped(ped_file):
+    """
+    Parse ped file to a samples dict where per sample (key) a metadata dict (value) is created
+    with the family ID (string), parents (list) and children (list) as content.
+
+    Args:
+        ped_file (open file): Open file object to a table with sample metadata, including the following values:
+            familyID (string): Unique identifier per family. In other words, s
+                samples from the same family will have the same familyID.
+            sampleID (string): Unique identifier of sample.
+            father (string): reference to another sampleID.
+            mother (string): reference to another sampleID.
+            sex (int): 0 (unknown), 1 (male) or 2 (female)
+            phenotype (int): 0 (unknown), 1 (unaffected) or 2 (affected)
+
+    Returns:
+        dict: Per sample (key) a metadata dict (value) with
+            the family ID (string), parents (list) and children (list).
+    """
     samples = {}  # 'sample_id': {'family': 'fam_id', 'parents': ['sample_id', 'sample_id']}
 
-    for line in ped_file:
-        ped_data = line.strip().split()
-        family, sample, father, mother, sex, phenotype = ped_data
-
-        # Create samples
-        if sample not in samples:
-            samples[sample] = {'family': family, 'parents': [], 'children': []}
-        if father != '0' and father not in samples:
-            samples[father] = {'family': family, 'parents': [], 'children': []}
-        if mother != '0' and mother not in samples:
-            samples[mother] = {'family': family, 'parents': [], 'children': []}
-
-        # Save sample relations
-        if father != '0':
-            samples[sample]['parents'].append(father)
-            samples[father]['children'].append(sample)
-        if mother != '0':
-            samples[sample]['parents'].append(mother)
-            samples[mother]['children'].append(sample)
+    with open(ped_file, "r") as ped_file_open:
+        for line in ped_file_open:
+            ped_data = line.strip().split()
+            try:
+                family, sample, father, mother, sex, phenotype = ped_data
+            except ValueError as error:
+                print(f"Failed to parse ped file data {line}; {error}")
+                exit(1)
+
+            # Create samples
+            if sample not in samples:
+                samples[sample] = {'family': family, 'parents': [], 'children': []}
+            if father != '0' and father not in samples:
+                samples[father] = {'family': family, 'parents': [], 'children': []}
+            if mother != '0' and mother not in samples:
+                samples[mother] = {'family': family, 'parents': [], 'children': []}
+
+            # Save sample relations
+            if father != '0':
+                samples[sample]['parents'].append(father)
+                samples[father]['children'].append(sample)
+            if mother != '0':
+                samples[sample]['parents'].append(mother)
+                samples[mother]['children'].append(sample)
     return samples
 
 
-def check_kinship(kinship_file, samples, kinship_setting):
-    kinship_errors = False
-    print_kinship('sample_1', 'sample_2', 'kinship', 'related', 'type', 'status')  # header
-    for line in kinship_file:
-        # Parse kinship data
-        if line.startswith('FID1'):
-            continue  # skip header line
-        kinship_data = line.strip().split()
-        sample_1, sample_2, kinship = kinship_data[1], kinship_data[3], float(kinship_data[7])
-
-        # Check kinship data
+def read_and_modify_kinship(kinship_file, kinship_min, kinship_max):
+    """
+    Read and modify kinship file content by renaming and adding columns.
+
+    Args:
+        kinship_file (string): File with retrieved kinship values as result of running the tool
+                                'KING' (Kinship-based INference for Gwas)
+        kinship_min (float): Minimum threshold to check if samples are kin.
+        kinship_max (float): Maximum threshold to check if samples are kin, without being self-self relationship.
+
+    Returns:
+        pandas DataFrame: Retrieved kinship data input with additional columns.
+    """
+    kinship_data = (
+        # Read kinship data specific columns
+        read_table(kinship_file, delimiter='\t', usecols=['FID1', 'FID2', 'Kinship'])
+        # Rename columns
+        .rename(columns={'FID1': 'sample_1', 'FID2': 'sample_2', 'Kinship': 'kinship'})
+        # Add columns with default values
+        .assign(
+            related=None, type=None, status=None,
+            thresholds=f"{kinship_min},{kinship_max}",
+            message=''
+        )
+    )
+    return kinship_data
+
+
+def check_and_annotate_kinship(kinship_data, samples, kinship_min, kinship_max):
+    """
+    Calculated kinship values are judged and results are added to the dataframe.
+    Results include:
+        related (boolean): Are samples related to each other, aka kin.
+        type (string): The relationship type in words, one of: unrelated, parent_parent, parent_child, sibling_sibling
+        status (string): Whether the kinship value is within the expected range: 'FAIL' or 'OK'
+        message (string): User friendly message to explain error if status equals 'FAIL'. Empty when status equals 'OK'.
+
+    Args:
+        kinship_data (pandas DataFrame): Retrieved kinship data input with additional columns.
+        samples (dict): Per sample (key) a metadata dict (values) with
+                        the family ID (string), parents (list) and children (list).
+        kinship_min (float): Minimum threshold to check if samples are kin.
+        kinship_max (float): Maximum threshold to check if samples are kin, without being self-self relationship.
+
+    Returns:
+        pandas DataFrame: Retrieved kinship data with annotated / judged results.
+    """
+    for index, row in kinship_data.iterrows():
+        status = 'OK'
+        message = ''
         # Related
-        if samples[sample_1]['family'] == samples[sample_2]['family']:
+        if samples[row.sample_1]['family'] == samples[row.sample_2]['family']:
+            related = True
             # Parent - child
-            if sample_2 in samples[sample_1]['parents'] or sample_1 in samples[sample_2]['parents']:
-                if kinship > kinship_setting[0] and kinship < kinship_setting[1]:
-                    print_kinship(sample_1, sample_2, kinship, True, 'parent_child', 'OK')
-                else:
-                    print_kinship(sample_1, sample_2, kinship, True, 'parent_child', 'FAIL')
-                    kinship_errors = True
+            if row.sample_2 in samples[row.sample_1]['parents'] or row.sample_1 in samples[row.sample_2]['parents']:
+                type = 'parent_child'
+                if row.kinship <= kinship_min or row.kinship >= kinship_max:
+                    status = 'FAIL'
+                    expected_value_range = f"> {kinship_min} and < {kinship_max}"
             # Parent - Parent -> both samples have the same children
-            elif samples[sample_1]['children'] and samples[sample_1]['children'] == samples[sample_2]['children']:
-                if kinship <= kinship_setting[0]:
-                    print_kinship(sample_1, sample_2, kinship, True, 'parent_parent', 'OK')
-                else:
-                    print_kinship(sample_1, sample_2, kinship, True, 'parent_parent', 'FAIL')
-                    kinship_errors = True
-            # Aassume siblings
+            elif samples[row.sample_1]['children'] and samples[row.sample_1]['children'] == samples[row.sample_2]['children']:
+                type = 'parent_parent'
+                if row.kinship > kinship_min:
+                    status = 'FAIL'
+                    expected_value_range = f"<= {kinship_min}"
+            # Assume siblings
             else:
-                if kinship > kinship_setting[0] and kinship < kinship_setting[1]:
-                    print_kinship(sample_1, sample_2, kinship, True, 'sibling_sibling', 'OK')
-                else:
-                    print_kinship(sample_1, sample_2, kinship, True, 'sibling_sibling', 'FAIL')
-                    kinship_errors = True
+                type = 'sibling_sibling'
+                if row.kinship <= kinship_min or row.kinship >= kinship_max:
+                    status = 'FAIL'
+                    expected_value_range = f"> {kinship_min} and < {kinship_max}"
         # Unrelated
         else:
-            if kinship <= kinship_setting[0]:
-                print_kinship(sample_1, sample_2, kinship, False, 'NA', 'OK')
-            else:
-                print_kinship(sample_1, sample_2, kinship, False, 'NA', 'FAIL')
-                kinship_errors = True
-
-    return kinship_errors
+            related = False
+            type = 'unrelated'
+            if row.kinship > kinship_min:
+                status = 'FAIL'
+                expected_value_range = f"<= {kinship_min}"
+
+        # Create end user message if status is fail
+        if status == 'FAIL':
+            message = (
+                f"Kinship value {row.kinship} between "
+                f"{row.sample_1} ({samples[row.sample_1]['family']}) and {row.sample_2} ({samples[row.sample_2]['family']}) "
+                f"is not between expected values for {type}: {expected_value_range}"
+            )
+        # Update row with retrieved related (boolean), relationship type, status (OK / FAIL) and message.
+        kinship_data.loc[index, ['related', 'type', 'status', 'message']] = related, type, status, message
+    return kinship_data
+
+
+def write_kinship(df_kinship_out, output_path, output_prefix):
+    """
+    Write the retrieved and annoted kinship data to file or stdout. Include comments as header.
+
+    Args:
+        df_kinship_out (pandas DataFrame): Retrieved kinship data with annotated / judged results.
+        output_path (string): Path to output dir where outputfile is stored.
+        output_prefix (string): Prefix to use output filename.
+    """
+    # Collect all comments
+    comments = []
+    if any(df_kinship_out.status == 'FAIL'):
+        comments.append('# WARNING: Kinship errors found.\n')
+    else:
+        comments.append('# No kinship errors found.\n')
+    # Assume all row values of column thresholds are the same.
+    comments.append(f"# Used kinship check settings: {df_kinship_out.loc[0, 'thresholds']}\n")
 
+    # Write to provided output settings or to a tempfile
+    if output_path and output_prefix:
+        file_out = open(f"{output_path}/{output_prefix}.kinship_check.out", 'a+')
+    else:
+        file_out = tempfile.TemporaryFile(mode='a+')
 
-def print_kinship(sample_1, sample_2, kinship, fam_status, relation_status, kinship_status):
-    print('{0}\t{1}\t{2}\t{3}\t{4}\t{5}'.format(sample_1, sample_2, kinship, fam_status, relation_status, kinship_status))
+    # Write comments as header
+    file_out.writelines(comments)
+    # Append annotated kinship results
+    df_kinship_out.to_csv(file_out, sep='\t', index=False, header=True)
+    # Decide if file should be printed to stdout instead
+    if not output_path or not output_prefix:
+        file_out.seek(0)
+        print(file_out.read())
+    # Closing a tempfile will delete it as well
+    file_out.close()
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='Check kinship output based on ped file.')
-    parser.add_argument('kinship_file', type=argparse.FileType('r'), help='Kinship file')
-    parser.add_argument('ped_file', type=argparse.FileType('r'), help='PED file')
-    arguments = parser.parse_args()
+if __name__ == '__main__':
+    arguments = parse_arguments_and_check(args_in=argv[1:])
+    kinship_min, kinship_max = arguments.kinship_settings
 
-    # settings
-    kinship_setting = [0.177, 0.354]
-
-    # Parse ped file and check kinship
     samples = parse_ped(arguments.ped_file)
-    kinship_errors = check_kinship(arguments.kinship_file, samples, kinship_setting)
-
-    # Print summary
-    if kinship_errors:
-        print("\n# WARNING: Kinship errors found.")
-    else:
-        print("\n# No kinship errors found.")
-    print("# Used kinship check settings: {0}".format(kinship_setting))
+    df_kinship_in = read_and_modify_kinship(arguments.kinship_file, kinship_min, kinship_max)
+    df_kinship_out = check_and_annotate_kinship(df_kinship_in, samples, kinship_min, kinship_max)
+    write_kinship(df_kinship_out, arguments.output_path, arguments.output_prefix)
diff --git a/Kinship/poetry.lock b/Kinship/poetry.lock
new file mode 100644
index 0000000..a6bcba0
--- /dev/null
+++ b/Kinship/poetry.lock
@@ -0,0 +1,278 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+files = [
+    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
+    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
+]
+
+[[package]]
+name = "iniconfig"
+version = "2.0.0"
+description = "brain-dead simple config-ini parsing"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
+    {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
+]
+
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
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+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "a3517d391acbd94caec47c2dca39bc638a86888ade39588f514f467486362547"
diff --git a/Kinship/pyproject.toml b/Kinship/pyproject.toml
new file mode 100644
index 0000000..0214f6d
--- /dev/null
+++ b/Kinship/pyproject.toml
@@ -0,0 +1,25 @@
+[tool.poetry]
+name = "kinship"
+version = "1.1.1"
+description = ""
+authors = ["Bioinformatica Genetica <bioinformatica-genetica@umcutrecht.nl>"]
+license = "MIT"
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "^3.11"
+pandas = "2.1.4"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.3.3"
+pytest-unordered = "0.5.2"
+pytest-datadir = "^1.5.0"
+pytest-mock = "^3.14.0"
+
+[tool.ruff]
+line-length = 127
+indent-width = 4
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/Kinship/test_check_kinship.py b/Kinship/test_check_kinship.py
new file mode 100644
index 0000000..c1dcd36
--- /dev/null
+++ b/Kinship/test_check_kinship.py
@@ -0,0 +1,316 @@
+#!/usr/bin/env python
+# Import statements, alphabetic order of main package.
+from pathlib import Path
+
+
+# Third party libraries alphabetic order of main package.
+from pandas import DataFrame
+import pytest
+from pytest_unordered import unordered
+
+# Custom libraries alphabetic order of main package.
+import check_kinship
+
+
+@pytest.fixture(scope="module", autouse=True)
+def setup_test_path(tmp_path_factory):
+    test_tmp_path = str(tmp_path_factory.mktemp("test")) + "/"
+    open(str(test_tmp_path) + "/empty.txt", "a").close()
+    return test_tmp_path
+
+
+@pytest.fixture(scope="module", autouse=True)
+def kinship_settings():
+    return 0.177, 0.354
+
+class TestNonEmptyExistingPath():
+    def test_existing_dir(self, setup_test_path):
+        file_or_dir = check_kinship.validate_non_empty_existing_path(setup_test_path)
+        assert file_or_dir
+
+    def test_not_file_not_dir(self):
+        fake_string = "fake_string"
+        with pytest.raises(FileNotFoundError) as file_dir_error:
+            check_kinship.validate_non_empty_existing_path(fake_string)
+        assert fake_string in str(file_dir_error.value)
+
+    def test_empty_file(self, setup_test_path):
+        with pytest.raises(OSError) as empty_error:
+            check_kinship.validate_non_empty_existing_path(setup_test_path + "empty.txt")
+        assert f"File {setup_test_path}empty.txt is empty." in str(empty_error.value)
+
+    def test_append_suffix(self, setup_test_path):
+        dir_without_suffix = setup_test_path.rstrip("/")
+        dir_with_suffix = check_kinship.validate_non_empty_existing_path(dir_without_suffix)
+        assert dir_without_suffix[-1] != "/"
+        assert dir_with_suffix[-1] == "/"
+
+
+class TestParsePed():
+    @pytest.mark.parametrize("input_file,exp_dict_samples", [
+        (
+            "multi_family.ped",
+            {
+                "2024D00001": {'family': 'U000001', 'parents': [], 'children': ["2024D00003"]},
+                "2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00003"]},
+                "2024D00003": {'family': 'U000001', 'parents': ["2024D00001", "2024D00002"], 'children': []},
+                "2024D00004": {'family': 'U000002', 'parents': [], 'children': ["2024D00006"]},
+                "2024D00005": {'family': 'U000002', 'parents': [], 'children': ["2024D00006"]},
+                "2024D00006": {'family': 'U000002', 'parents': ["2024D00004", "2024D00005"], 'children': []}
+            },
+        ),
+        (
+            "multi_siblings.ped",
+            {
+                "2024D00001": {'family': 'U000001', 'parents': [], 'children': ["2024D00003", "2024D00004"]},
+                "2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00003", "2024D00004"]},
+                "2024D00003": {'family': 'U000001', 'parents': ["2024D00001", "2024D00002"], 'children': []},
+                "2024D00004": {'family': 'U000001', 'parents': ["2024D00001", "2024D00002"], 'children': []}
+            },
+        ),
+        (
+            "multi_unrelated_samples.ped",
+            {
+                "2024D00001": {'family': 'U000001', 'parents': [], 'children': []},
+                "2024D00002": {'family': 'U000002', 'parents': [], 'children': []}
+            },
+        ),
+        (
+            "single_family.ped",
+            {
+                "2024D00001": {'family': 'U000001', 'parents': [], 'children': ["2024D00003"]},
+                "2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00003"]},
+                "2024D00003": {'family': 'U000001', 'parents': ["2024D00001", "2024D00002"], 'children': []}
+            },
+        ),
+    ])
+    def test_parse_ped_ok(self, input_file, exp_dict_samples, datadir):
+        dict_samples = check_kinship.parse_ped(f"{datadir}/{input_file}")
+        assert dict_samples.keys() == unordered(exp_dict_samples.keys())
+        for sample, meta in dict_samples.items():
+            assert meta.get("family") == exp_dict_samples.get(sample).get("family")
+            assert meta.get("parents") == unordered(exp_dict_samples.get(sample).get("parents"))
+            assert meta.get("children") == unordered(exp_dict_samples.get(sample).get("children"))
+
+    @pytest.mark.parametrize("input_file,exp_data_line,exp_error", [
+        ("wrong_separator.ped", "U000001,2024D00001,0,0,1,1", "not enough values to unpack (expected 6, got 1)"),
+        ("missing_field.ped", "U000001\t2024D00002\t0\t0\t2", "not enough values to unpack (expected 6, got 5)"),
+
+    ])
+    def test_incorrect_ped(self, input_file, exp_data_line, exp_error, datadir, capsys, mocker):
+        with pytest.raises(SystemExit) as pytest_wrapped_e:
+            check_kinship.parse_ped(f"{datadir}/{input_file}")
+        assert pytest_wrapped_e.type is SystemExit
+        assert pytest_wrapped_e.value.code == 1
+
+        out, err = capsys.readouterr()
+        assert exp_data_line in out
+        assert exp_error in out
+
+
+def test_read_and_modify_kinship_trio(datadir, kinship_settings):
+    kinship_min, kinship_max = kinship_settings
+    df_out = check_kinship.read_and_modify_kinship(f"{datadir}/trio.kinship", kinship_min, kinship_max)
+    # Assert succeeded read_table
+    assert not df_out.empty
+    # Assert renaming columns
+    assert set(["sample_1", "sample_2", "kinship"]) < set(df_out.columns)
+    assert not set(["FID1", "FID2", "Kinship"]) <= set(df_out.columns)
+    # Assert added columns
+    assert set(["related", "type", "status", "thresholds", "message"]) < set(df_out.columns)
+    # Assert threshold added as string
+    assert df_out.thresholds.iloc[0] == f"{kinship_min},{kinship_max}" and isinstance(df_out.thresholds.iloc[0], str)
+
+
+class TestCheckAndAnnotateKinship():
+    @pytest.mark.parametrize("samples,file,related,type", [
+        # Type parent_child with kinship values between min and max threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': ["2024D00001-2024D00002"], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00003-2024D00004"]},
+            },
+            "single_row_kin.kinship", True, 'parent_child'
+        ),
+        # Type parent_parent with kinship values lower than min threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': ["2024D00005-2024D00006"]},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00005-2024D00006"]},
+            },
+            "single_row.kinship", True, 'parent_parent'
+        ),
+        # Type sibling_sibling with kinship values between min and max threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': []},
+            },
+            "single_row_kin.kinship", True, 'sibling_sibling'
+        ),
+        # Type unrelated with kinship values lower than min threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000002', 'parents': [], 'children': []},
+            },
+            "single_row.kinship", False, 'unrelated'
+        ),
+    ])
+    def test_single_rows_ok(self, samples, file, related, type, datadir, kinship_settings):
+        kinship_min, kinship_max = kinship_settings
+        df_in = check_kinship.read_and_modify_kinship(f"{datadir}/{file}", kinship_min, kinship_max)
+        df_out = check_kinship.check_and_annotate_kinship(df_in, samples, kinship_min, kinship_max)
+        assert df_out.shape == (1, 8)
+        assert df_out.loc[0, "related"] == related
+        assert df_out.loc[0, "type"] == type
+        assert df_out.loc[0, "status"] == "OK"
+        assert df_out.loc[0, "message"] == ''
+
+    @pytest.mark.parametrize("samples,file,related,type,msg", [
+        # Type parent_child with kinship values lower than min threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': ["2024D00001-2024D00002"], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00003-2024D00004"]},
+            },
+            "single_row.kinship", True, 'parent_child', '> 0.177 and < 0.354'
+        ),
+        # Type parent_child with kinship values higher than min threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': ["2024D00001-2024D00002"], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00003-2024D00004"]},
+            },
+            "single_row_duplo.kinship", True, 'parent_child', '> 0.177 and < 0.354'
+        ),
+        # Type parent_parent with kinship values between min and max threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': ["2024D00005-2024D00006"]},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': ["2024D00005-2024D00006"]},
+            },
+            "single_row_kin.kinship", True, 'parent_parent', '<= 0.177'
+        ),
+        # Type sibling_sibling with kinship values lower than min threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': []},
+            },
+            "single_row.kinship", True, 'sibling_sibling', '> 0.177 and < 0.354'
+        ),
+        # Type sibling_sibling with kinship values higher than max threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000001', 'parents': [], 'children': []},
+            },
+            "single_row_duplo.kinship", True, 'sibling_sibling', '> 0.177 and < 0.354'
+        ),
+        # Type unrelated with kinship values between min and max threshold
+        (
+            {
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': []},
+                "2024D00001-2024D00002": {'family': 'U000002', 'parents': [], 'children': []},
+            },
+            "single_row_kin.kinship", False, 'unrelated', '<= 0.177'
+        ),
+    ])
+    def test_single_rows_fail(self, samples, file, related, type, msg, datadir, kinship_settings):
+        kinship_min, kinship_max = kinship_settings
+        df_in = check_kinship.read_and_modify_kinship(f"{datadir}/{file}", kinship_min, kinship_max)
+        df_out = check_kinship.check_and_annotate_kinship(df_in, samples, kinship_min, kinship_max)
+        assert df_out.shape == (1, 8)
+        assert df_out.loc[0, "related"] == related
+        assert df_out.loc[0, "type"] == type
+        assert df_out.loc[0, "status"] == "FAIL"
+
+        msg_out = df_out.loc[0, "message"]
+        assert str(df_in.loc[0, "kinship"]) in msg_out
+        for sample, meta in samples.items():
+            assert sample in msg_out
+            assert meta.get("family") in msg_out
+        assert type in msg_out
+        assert msg in msg_out
+
+    @pytest.mark.parametrize("samples,file", [
+        # OK: trio
+        (
+            {
+                "2024D00005-2024D00006": {'family': 'U000001', 'parents': [], 'children': ["2024D00001-2024D00002"]},
+                "2024D00003-2024D00004": {'family': 'U000001', 'parents': [], 'children': ["2024D00001-2024D00002"]},
+                "2024D00001-2024D00002": {
+                    'family': 'U000001', 'parents': ["2024D00003-2024D00004", "2024D00005-2024D00006"], 'children': []
+                },
+            },
+            "trio.kinship"
+        ),
+    ])
+    def test_multi_rows_trio_ok(self, samples, file, datadir, kinship_settings):
+        kinship_min, kinship_max = kinship_settings
+        df_in = check_kinship.read_and_modify_kinship(f"{datadir}/{file}", kinship_min, kinship_max)
+        df_out = check_kinship.check_and_annotate_kinship(df_in, samples, kinship_min, kinship_max)
+        assert df_out.shape == (3, 8)
+        assert "parent_child" in df_out.type.values
+        assert "parent_parent" in df_out.type.values
+        assert "OK" == df_out.status.unique()
+        assert not df_out.message.unique()
+
+
+class TestWriteKinship():
+    @pytest.mark.parametrize("input_dict,error_comment", [
+        (
+            {
+                "sample_1": ["2024D00001-2024D00002"],
+                "sample_2": ["2024D00003-2024D00004"],
+                "status": ["OK"],
+                "thresholds": ["0.177, 0.354"]
+            },
+            "# No kinship errors found."
+        ),
+        (
+            {
+                "sample_1": ["2024D00001-2024D00002"],
+                "sample_2": ["2024D00003-2024D00004"],
+                "status": ["FAIL"],
+                "thresholds": ["0.177, 0.354"]
+            },
+            "# WARNING: Kinship errors found."
+        ),
+    ])
+    def test_file(self, input_dict, error_comment, setup_test_path):
+        df_in = DataFrame(input_dict)
+        prefix = f"fake_prefix_{input_dict['status']}"
+        check_kinship.write_kinship(df_in, setup_test_path, prefix)
+
+        output_file = Path(f"{setup_test_path}/{prefix}.kinship_check.out")
+        assert output_file.exists() and output_file.is_file()
+        with open(output_file) as file:
+            file_content = file.read().rstrip().splitlines()
+        assert len(file_content) == 4
+        assert file_content[0] == error_comment
+        assert file_content[1] == "# Used kinship check settings: 0.177, 0.354"
+        assert file_content[2].split("\t") == df_in.columns.to_list()
+        assert file_content[3].split("\t") == df_in.iloc[0].tolist()
+
+    def test_stdout(self, kinship_settings, capsys):
+        kinship_min, kinship_max = kinship_settings
+        df_in = DataFrame({
+            "sample_1": ["2024D00001-2024D00002"],
+            "sample_2": ["2024D00003-2024D00004"],
+            "status": ["OK"],
+            "thresholds": [f"{kinship_min}, {kinship_max}"],
+        })
+
+        check_kinship.write_kinship(df_kinship_out=df_in, output_path=None, output_prefix=None)
+        out, err = capsys.readouterr()
+        lst_lines = out.rstrip().splitlines()
+        # Check number of expected lines
+        assert len(lst_lines) == 4
+        # Check comments / headers
+        assert lst_lines[0] == "# No kinship errors found."
+        assert lst_lines[1] == "# Used kinship check settings: 0.177, 0.354"
+        # Check table
+        assert lst_lines[2].split("\t") == df_in.columns.to_list()
+        assert lst_lines[3].split("\t") == df_in.iloc[0].tolist()
diff --git a/Kinship/test_check_kinship/missing_field.ped b/Kinship/test_check_kinship/missing_field.ped
new file mode 100644
index 0000000..1fe53df
--- /dev/null
+++ b/Kinship/test_check_kinship/missing_field.ped
@@ -0,0 +1,3 @@
+U000001	2024D00001	0	0	1	1
+U000001	2024D00002	0	0	2
+U000001	2024D00003	2024D00001	2024D00002	2	2
diff --git a/Kinship/test_check_kinship/multi_family.ped b/Kinship/test_check_kinship/multi_family.ped
new file mode 100644
index 0000000..8c720f0
--- /dev/null
+++ b/Kinship/test_check_kinship/multi_family.ped
@@ -0,0 +1,6 @@
+U000001 2024D00001  0	0	1	1
+U000001 2024D00002  0	0	2	1
+U000001 2024D00003	2024D00001	2024D00002	2	2
+U000002 2024D00004	0	0	1	1
+U000002 2024D00005	0	0	2	1
+U000002 2024D00006	2024D00004	2024D00005	2	2
diff --git a/Kinship/test_check_kinship/multi_siblings.ped b/Kinship/test_check_kinship/multi_siblings.ped
new file mode 100644
index 0000000..1136c6b
--- /dev/null
+++ b/Kinship/test_check_kinship/multi_siblings.ped
@@ -0,0 +1,4 @@
+U000001	2024D00001	0	0	1	1
+U000001	2024D00002	0	0	2	1
+U000001	2024D00003	2024D00001	2024D00002	2	2
+U000001	2024D00004	2024D00001	2024D00002	2	2
diff --git a/Kinship/test_check_kinship/multi_unrelated_samples.ped b/Kinship/test_check_kinship/multi_unrelated_samples.ped
new file mode 100644
index 0000000..9181d7a
--- /dev/null
+++ b/Kinship/test_check_kinship/multi_unrelated_samples.ped
@@ -0,0 +1,2 @@
+U000001	2024D00001	0	0	1	1
+U000002	2024D00002	0	0	2	1
diff --git a/Kinship/test_check_kinship/single_family.ped b/Kinship/test_check_kinship/single_family.ped
new file mode 100644
index 0000000..ffd5a3d
--- /dev/null
+++ b/Kinship/test_check_kinship/single_family.ped
@@ -0,0 +1,3 @@
+U000001	2024D00001	0	0	1	1
+U000001	2024D00002	0	0	2	1
+U000001	2024D00003	2024D00001	2024D00002	2	2
diff --git a/Kinship/test_check_kinship/single_row.kinship b/Kinship/test_check_kinship/single_row.kinship
new file mode 100644
index 0000000..0afc22e
--- /dev/null
+++ b/Kinship/test_check_kinship/single_row.kinship
@@ -0,0 +1,2 @@
+FID1	ID1	FID2	ID2	N_SNP	HetHet	IBS0	Kinship
+2024D00003-2024D00004	2024D00003-2024D00004	2024D00001-2024D00002	2024D00001-2024D00002	285313	0.1303	0.0521	0.0199
diff --git a/Kinship/test_check_kinship/single_row_duplo.kinship b/Kinship/test_check_kinship/single_row_duplo.kinship
new file mode 100644
index 0000000..d3ca8e9
--- /dev/null
+++ b/Kinship/test_check_kinship/single_row_duplo.kinship
@@ -0,0 +1,2 @@
+FID1	ID1	FID2	ID2	N_SNP	HetHet	IBS0	Kinship
+2024D00003-2024D00004	2024D00003-2024D00004	2024D00001-2024D00002	2024D00001-2024D00002	284880	0.1716	0.0065	0.451
diff --git a/Kinship/test_check_kinship/single_row_kin.kinship b/Kinship/test_check_kinship/single_row_kin.kinship
new file mode 100644
index 0000000..525af0f
--- /dev/null
+++ b/Kinship/test_check_kinship/single_row_kin.kinship
@@ -0,0 +1,2 @@
+FID1	ID1	FID2	ID2	N_SNP	HetHet	IBS0	Kinship
+2024D00003-2024D00004	2024D00003-2024D00004	2024D00001-2024D00002	2024D00001-2024D00002	284880	0.1716	0.0065	0.2435
diff --git a/Kinship/test_check_kinship/trio.kinship b/Kinship/test_check_kinship/trio.kinship
new file mode 100755
index 0000000..1de24eb
--- /dev/null
+++ b/Kinship/test_check_kinship/trio.kinship
@@ -0,0 +1,4 @@
+FID1	ID1	FID2	ID2	N_SNP	HetHet	IBS0	Kinship
+2024D00003-2024D00004	2024D00003-2024D00004	2024D00001-2024D00002	2024D00001-2024D00002	284880	0.1716	0.0065	0.2435
+2024D00003-2024D00004	2024D00003-2024D00004	2024D00005-2024D00006	2024D00005-2024D00006	285313	0.1303	0.0521	0.0199
+2024D00001-2024D00002	2024D00001-2024D00002	2024D00005-2024D00006	2024D00005-2024D00006	284396	0.1863	0.0063	0.2582
diff --git a/Kinship/test_check_kinship/wrong_separator.ped b/Kinship/test_check_kinship/wrong_separator.ped
new file mode 100644
index 0000000..e0622b8
--- /dev/null
+++ b/Kinship/test_check_kinship/wrong_separator.ped
@@ -0,0 +1,3 @@
+U000001,2024D00001,0,0,1,1
+U000001,2024D00002,0,0,2,1
+U000001,2024D00003,2024D00001,2024D00002,2,2
diff --git a/README.md b/README.md
index dcef68d..8072aa7 100644
--- a/README.md
+++ b/README.md
@@ -8,17 +8,17 @@ This repository contains custom nextflow processes and their dependent/linked fi
 Utility functions are dinstinguished and can be found in Utils.
 
 ### Designated folder
-When nextflow processes or their dependent files are linked to another git repository, 
+When nextflow processes or their dependent files are linked to another git repository,
 the files will be placed in a designated folder. For example:
 ClarityEpp folder and https://github.com/UMCUGenetics/clarity_epp
 
 If a set of files is not per se a utility and doesnot have a separate repository, it is allowed to create a designated folder as well.
 
 ## Docker files
-Build docker image for software dependencies. 
+Build docker image for software dependencies.
 - [Install Docker Desktop](https://docs.docker.com/desktop/mac/apple-silicon/)
     ```bash
     docker build -t organization_or_username/toolname:version -f path_to_dockerfile
     docker push organization_or_username/toolname:version
     ```
-- If changes are required to the dockerfile, manually update label version.
\ No newline at end of file
+- If changes are required to the dockerfile, manually update label version.