diff --git a/.cookietemple.yml b/.cookietemple.yml
index 39a9dcd..0359ed3 100644
--- a/.cookietemple.yml
+++ b/.cookietemple.yml
@@ -15,5 +15,5 @@ full_name: Victor Giurcoiu
 email: victor.giurcoiu@tum.de
 project_name: spectrum_fundamentals
 project_short_description: Fundamentals public repo
-version: 0.5.1
+version: 0.5.2
 license: MIT
diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml
index 1193350..9ae5f09 100644
--- a/.github/release-drafter.yml
+++ b/.github/release-drafter.yml
@@ -1,5 +1,5 @@
-name-template: "0.5.1 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
-tag-template: 0.5.1 # <<COOKIETEMPLE_FORCE_BUMP>>
+name-template: "0.5.2 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
+tag-template: 0.5.2 # <<COOKIETEMPLE_FORCE_BUMP>>
 exclude-labels:
     - "skip-changelog"
 
diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml
index 2f4433f..f9d2ff1 100644
--- a/.github/workflows/build_package.yml
+++ b/.github/workflows/build_package.yml
@@ -16,7 +16,7 @@ jobs:
               name: Check out source-code repository
 
             - name: Setup Python
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: ${{ matrix.python }}
 
diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml
index c3b1f25..8ea1f8f 100644
--- a/.github/workflows/labeler.yml
+++ b/.github/workflows/labeler.yml
@@ -14,6 +14,6 @@ jobs:
               uses: actions/checkout@v2.3.3
 
             - name: Run Labeler
-              uses: crazy-max/ghaction-github-labeler@v3.1.1
+              uses: crazy-max/ghaction-github-labeler@v5.0.0
               with:
                   skip-delete: true
diff --git a/.github/workflows/main_master_branch_protection.yml b/.github/workflows/main_master_branch_protection.yml
index 06ffb21..0f9fb66 100644
--- a/.github/workflows/main_master_branch_protection.yml
+++ b/.github/workflows/main_master_branch_protection.yml
@@ -20,7 +20,7 @@ jobs:
             # NOTE - this may not work if the PR is coming from a fork, due to limitations in GitHub actions secrets
             - name: Post PR comment
               if: failure()
-              uses: mshick/add-pr-comment@v1
+              uses: mshick/add-pr-comment@v2
               with:
                   message: |
                       Hi @${{ github.event.pull_request.user.login }},
@@ -40,7 +40,7 @@ jobs:
         runs-on: ubuntu-latest
         steps:
             - name: Set up Python
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: "3.8"
             # PRs to the repository master branch are only ok if coming from any patch or release branch
@@ -61,7 +61,7 @@ jobs:
             # NOTE - this may not work if the PR is coming from a fork, due to limitations in GitHub actions secrets
             - name: Post PR comment
               if: failure()
-              uses: mshick/add-pr-comment@v1
+              uses: mshick/add-pr-comment@v2
               with:
                   message: |
                       Hi @${{ github.event.pull_request.user.login }},
diff --git a/.github/workflows/publish_docs.yml b/.github/workflows/publish_docs.yml
index cc0f2df..6f00229 100644
--- a/.github/workflows/publish_docs.yml
+++ b/.github/workflows/publish_docs.yml
@@ -11,7 +11,7 @@ jobs:
               name: Check out source-code repository
 
             - name: Setup Python
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: 3.8
 
diff --git a/.github/workflows/publish_package.yml b/.github/workflows/publish_package.yml
index ace72f0..19c7e0b 100644
--- a/.github/workflows/publish_package.yml
+++ b/.github/workflows/publish_package.yml
@@ -15,7 +15,7 @@ jobs:
                   fetch-depth: 2
 
             - name: Set up Python
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: "3.9"
 
@@ -29,7 +29,7 @@ jobs:
                   poetry build --ansi
 
             - name: Publish package on PyPI
-              uses: pypa/gh-action-pypi-publish@v1.6.4
+              uses: pypa/gh-action-pypi-publish@v1.8.14
               with:
                   # TODO COOKIETEMPLE: Configure your PyPI Token to enable automatic deployment to PyPi on releases
                   # https://help.github.com/en/actions/configuring-and-managing-workflows/creating-and-storing-encrypted-secrets
diff --git a/.github/workflows/run_cookietemple_lint.yml b/.github/workflows/run_cookietemple_lint.yml
index a83dadc..c3d0fbc 100644
--- a/.github/workflows/run_cookietemple_lint.yml
+++ b/.github/workflows/run_cookietemple_lint.yml
@@ -11,7 +11,7 @@ jobs:
               name: Check out source-code repository
 
             - name: Setup Python
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: 3.9
 
diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index 33747cd..63040a2 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -72,7 +72,7 @@ jobs:
               uses: actions/checkout@v2.3.4
 
             - name: Set up Python ${{ matrix.python-version }}
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: ${{ matrix.python-version }}
 
@@ -104,7 +104,7 @@ jobs:
                   print("::set-output name=result::{}".format(result))
 
             - name: Restore pre-commit cache
-              uses: actions/cache@v2.1.7
+              uses: actions/cache@v4.0.2
               if: matrix.session == 'pre-commit'
               with:
                   path: ~/.cache/pre-commit
@@ -137,7 +137,7 @@ jobs:
               uses: actions/checkout@v3
 
             - name: Set up Python 3.8
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: 3.8
 
diff --git a/.github/workflows/sync_project.yml b/.github/workflows/sync_project.yml
index 7d1d68b..e0f4e02 100644
--- a/.github/workflows/sync_project.yml
+++ b/.github/workflows/sync_project.yml
@@ -10,7 +10,7 @@ jobs:
         runs-on: ubuntu-latest
         steps:
             - name: Setup Python
-              uses: actions/setup-python@v4.3.0
+              uses: actions/setup-python@v5.1.0
               with:
                   python-version: 3.8
 
diff --git a/cookietemple.cfg b/cookietemple.cfg
index 74005fa..4271a2c 100644
--- a/cookietemple.cfg
+++ b/cookietemple.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.5.1
+current_version = 0.5.2
 
 [bumpversion_files_whitelisted]
 init_file = spectrum_fundamentals/__init__.py
diff --git a/docs/conf.py b/docs/conf.py
index ce936fd..6d98a8a 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -54,9 +54,9 @@
 # the built documents.
 #
 # The short X.Y version.
-version = "0.5.1"
+version = "0.5.2"
 # The full version, including alpha/beta/rc tags.
-release = "0.5.1"
+release = "0.5.2"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/noxfile.py b/noxfile.py
index a951746..433fb2f 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -36,8 +36,7 @@ def activate_virtualenv_in_precommit_hooks(session: Session) -> None:
     session's virtual environment. This allows pre-commit to locate hooks in
     that environment when invoked from git.
 
-    Args:
-        session: The Session object.
+    :param session: The Session object.
     """
     assert session.bin is not None  # noqa: S101
 
@@ -111,6 +110,7 @@ def precommit(session: Session) -> None:
         "flake8-docstrings",
         "flake8-rst-docstrings",
         "isort",
+        "darglint",
         "pep8-naming",
         "pre-commit",
         "pre-commit-hooks",
diff --git a/poetry.lock b/poetry.lock
index 4be2dac..6d66718 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
@@ -1824,13 +1824,13 @@ testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygm
 
 [[package]]
 name = "pytest-cov"
-version = "4.1.0"
+version = "5.0.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"},
-    {file = "pytest_cov-4.1.0-py3-none-any.whl", hash = "sha256:6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a"},
+    {file = "pytest-cov-5.0.0.tar.gz", hash = "sha256:5837b58e9f6ebd335b0f8060eecce69b662415b16dc503883a02f45dfeb14857"},
+    {file = "pytest_cov-5.0.0-py3-none-any.whl", hash = "sha256:4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652"},
 ]
 
 [package.dependencies]
@@ -1838,7 +1838,7 @@ coverage = {version = ">=5.2.1", extras = ["toml"]}
 pytest = ">=4.6"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "python-dateutil"
@@ -1904,6 +1904,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -2710,4 +2711,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.0,<3.11.0"
-content-hash = "a2701e31ce7843199c62329fae9efd93dee744b6181f23a832ea12f1baba1420"
+content-hash = "d200011cd83d83ad3e0a834c204c8b1e20f45505d64ad420b5478b81482fcbf7"
diff --git a/pyproject.toml b/pyproject.toml
index 4b4b8a4..077ef18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "spectrum_fundamentals"
-version = "0.5.1"  # <<COOKIETEMPLE_FORCE_BUMP>>
+version = "0.5.2"  # <<COOKIETEMPLE_FORCE_BUMP>>
 description = "Fundamental functions, annotation pipeline and constants for oktoberfest"
 authors = ["Wilhelmlab at Technical University of Munich"]
 license = "MIT"
@@ -32,7 +32,7 @@ moepy = "^1.1.4"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6.2.4"
-pytest-cov = "^4.0.0"
+pytest-cov = "^5.0.0"
 coverage = {extras = ["toml"], version = ">=5.3"}
 safety = ">=1.9.0"
 typeguard = ">=2.12.0"
diff --git a/spectrum_fundamentals/__init__.py b/spectrum_fundamentals/__init__.py
index df6b5fc..413d645 100644
--- a/spectrum_fundamentals/__init__.py
+++ b/spectrum_fundamentals/__init__.py
@@ -2,7 +2,7 @@
 
 __author__ = "Mario Picciani"
 __email__ = "mario.picciani@tum.de"
-__version__ = "0.5.1"
+__version__ = "0.5.2"
 
 import logging
 import logging.handlers
diff --git a/spectrum_fundamentals/__main__.py b/spectrum_fundamentals/__main__.py
index 06bc5de..75c0534 100644
--- a/spectrum_fundamentals/__main__.py
+++ b/spectrum_fundamentals/__main__.py
@@ -5,7 +5,7 @@
 
 
 @click.command()
-@click.version_option(version="0.5.1", message=click.style("spectrum_fundamentals Version: 0.5.1"))
+@click.version_option(version="0.5.2", message=click.style("spectrum_fundamentals Version: 0.5.2"))
 def main() -> None:
     """spectrum_fundamentals."""
 
diff --git a/spectrum_fundamentals/annotation/annotation.py b/spectrum_fundamentals/annotation/annotation.py
index 68e1dcf..98dbd81 100644
--- a/spectrum_fundamentals/annotation/annotation.py
+++ b/spectrum_fundamentals/annotation/annotation.py
@@ -5,7 +5,7 @@
 import pandas as pd
 
 from spectrum_fundamentals import constants
-from spectrum_fundamentals.fragments import initialize_peaks
+from spectrum_fundamentals.fragments import initialize_peaks, initialize_peaks_xl
 
 logger = logging.getLogger(__name__)
 
@@ -148,11 +148,126 @@ def annotate_spectra(
             continue
         raw_file_annotations.append(results)
     results_df = pd.DataFrame(raw_file_annotations)
-    results_df.columns = ["INTENSITIES", "MZ", "CALCULATED_MASS", "removed_peaks"]
 
+    if "CROSSLINKER_TYPE" not in index_columns:
+        results_df.columns = ["INTENSITIES", "MZ", "CALCULATED_MASS", "removed_peaks"]
+    else:
+        results_df.columns = [
+            "INTENSITIES_A",
+            "INTENSITIES_B",
+            "MZ_A",
+            "MZ_B",
+            "CALCULATED_MASS_A",
+            "CALCULATED_MASS_B",
+            "removed_peaks_a",
+            "removed_peaks_b",
+        ]
     return results_df
 
 
+def peak_pos_xl_cms2(unmod_seq: str, crosslinker_position: int) -> np.ndarray:
+    """
+    Determines the positions of all potential normal and xl fragments within the vector generated by generate_annotation_matrix.
+
+    This function is used only for cleavable crosslinked peptides.
+
+    :param unmod_seq: Unmodified peptide sequence
+    :param crosslinker_position: The position of the crosslinker
+    :raises ValueError: if peptides exceed a length of 30
+    :return: position of different fragments as list
+    """
+    xl_mask_array = np.full(constants.VEC_LENGTH_CMS2, -1.0)
+
+    if len(unmod_seq) < constants.SEQ_LEN + 1:
+        if crosslinker_position != 1:
+            # b peaks
+            peaks_b = np.tile([3, 4, 5], crosslinker_position - 1) + np.repeat(
+                np.arange(crosslinker_position - 1) * 6, 3
+            )
+            xl_mask_array[peaks_b] = 0.0
+
+            # ylong peaks
+            first_pos_ylong = (len(unmod_seq) - crosslinker_position) * 6 + 174  # first position for ylong
+            peaks_ylong = np.arange(first_pos_ylong, first_pos_ylong + 3)
+            peaks_ylong = np.tile(peaks_ylong, crosslinker_position - 1)
+            peaks_ylong += np.repeat(np.arange(crosslinker_position - 1) * 6, 3)
+
+            xl_mask_array[peaks_ylong] = 0.0
+
+            # yshort peaks
+            xl_mask_array[[x - 174 for x in peaks_ylong]] = 0.0
+
+        if len(unmod_seq) != crosslinker_position:
+
+            # y peaks
+            peaks_y = np.tile([0, 1, 2], len(unmod_seq) - crosslinker_position) + np.repeat(
+                np.arange(len(unmod_seq) - crosslinker_position) * 6, 3
+            )
+            xl_mask_array[peaks_y] = 0.0
+
+            # blong peaks
+            first_pos_blong = (crosslinker_position - 1) * 6 + 174 + 3  # first position for blong
+            peaks_blong = np.arange(first_pos_blong, first_pos_blong + 3)
+            peaks_blong = np.tile(peaks_blong, len(unmod_seq) - crosslinker_position)
+            peaks_blong += np.repeat(np.arange(len(unmod_seq) - crosslinker_position) * 6, 3)
+            xl_mask_array[peaks_blong] = 0.0
+
+            # bshort peaks
+            xl_mask_array[[x - 174 for x in peaks_blong]] = 0.0
+
+    else:
+        raise ValueError(f"Peptides exceeding a length of 30 are not supported: {len(unmod_seq)}")
+
+    return xl_mask_array
+
+
+def generate_annotation_matrix_xl(
+    matched_peaks: pd.DataFrame, unmod_seq: str, crosslinker_position: int
+) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Generate the annotation matrix in the xl_prosit format from matched peaks.
+
+    :param matched_peaks: matched peaks needed to be converted
+    :param unmod_seq: unmodified peptide sequence
+    :param crosslinker_position: position of crosslinker
+    :return: numpy array of intensities and numpy array of masses
+    """
+    intensity = peak_pos_xl_cms2(unmod_seq, crosslinker_position)
+    mass = intensity.copy()
+
+    ion_type = matched_peaks.columns.get_loc("ion_type")
+    no_col = matched_peaks.columns.get_loc("no")
+    charge_col = matched_peaks.columns.get_loc("charge")
+    intensity_col = matched_peaks.columns.get_loc("intensity")
+    exp_mass_col = matched_peaks.columns.get_loc("exp_mass")
+
+    for peak in matched_peaks.values:
+        if peak[ion_type] == "y":
+            peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1)
+        elif peak[ion_type] == "b":
+            peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1) + 3
+        elif peak[ion_type] == "y-short":
+            peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1)
+        elif peak[ion_type] == "b-short":
+            peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1) + 3
+        elif peak[ion_type] == "y-long":
+            peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1) + 174
+        else:
+            peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1) + 174 + 3
+
+        if peak_pos >= constants.VEC_LENGTH_CMS2:
+            continue
+        intensity[peak_pos] = peak[intensity_col]
+        mass[peak_pos] = peak[exp_mass_col]
+
+    # convert elements representing charge 3 to -1 (we do not anotate +3)
+    index_charge_3 = range(2, constants.VEC_LENGTH_CMS2, 3)
+    intensity[index_charge_3] = -1
+    mass[index_charge_3] = -1
+
+    return intensity, mass
+
+
 def generate_annotation_matrix(
     matched_peaks: pd.DataFrame, unmod_seq: str, charge: int
 ) -> Tuple[np.ndarray, np.ndarray]:
@@ -190,7 +305,7 @@ def generate_annotation_matrix(
     exp_mass_col = matched_peaks.columns.get_loc("exp_mass")
 
     for peak in matched_peaks.values:
-        if peak[ion_type] == "y":
+        if peak[ion_type].startswith("y"):
             peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1)
         else:
             peak_pos = ((peak[no_col] - 1) * 6) + (peak[charge_col] - 1) + 3
@@ -210,17 +325,23 @@ def generate_annotation_matrix(
 
 def parallel_annotate(
     spectrum: np.ndarray,
-    index_columns: dict,
+    index_columns: Dict[str, int],
     mass_tolerance: Optional[float] = None,
     unit_mass_tolerance: Optional[str] = None,
-) -> Optional[Tuple[np.ndarray, np.ndarray, float, int]]:
+) -> Optional[
+    Union[
+        Tuple[np.ndarray, np.ndarray, float, int],
+        Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, float, int, int],
+    ]
+]:
     """
     Perform parallel annotation of a spectrum.
 
-    This function takes a spectrum and its index columns and performs parallel annotation of the spectrum. It starts by
-    initializing the peaks and extracting necessary data from the spectrum. It then matches the peaks to the spectrum and
-    generates an annotation matrix based on the matched peaks. If there are multiple matches found, it removes the redundant
-    matches. Finally, it returns annotated spectrum with meta data including intensity values, masses, calculated masses,
+    This function takes a spectrum and its index columns and performs parallel annotation of the spectrum.
+    It starts by initializing the peaks and extracting necessary data from the spectrum.
+    It then matches the peaks to the spectrum and generates an annotation matrix based on the matched peaks.
+    If there are multiple matches found, it removes the redundant matches.
+    Finally, it returns annotated spectrum with meta data including intensity values, masses, calculated masses,
     and any peaks that were removed. The function is designed to run in different threads to speed up the annotation pipeline.
 
     :param spectrum: a np.ndarray that contains the spectrum to be annotated
@@ -230,10 +351,37 @@ def parallel_annotate(
     :return: a tuple containing intensity values (np.ndarray), masses (np.ndarray), calculated mass (float),
              and any removed peaks (List[str])
     """
+    xl_type_col = index_columns.get("CROSSLINKER_TYPE")
+    if xl_type_col is None:
+        if spectrum[index_columns["PEPTIDE_LENGTH"]] > 30:  # this was in initialize peaks but can be checked prior
+            return None
+        return _annotate_linear_spectrum(spectrum, index_columns, mass_tolerance, unit_mass_tolerance)
+
+    if (spectrum[index_columns["PEPTIDE_LENGTH_A"]] > 30) or (spectrum[index_columns["PEPTIDE_LENGTH_B"]] > 30):
+        return None
+    return _annotate_crosslinked_spectrum(
+        spectrum, index_columns, spectrum[xl_type_col], mass_tolerance, unit_mass_tolerance
+    )
+
+
+def _annotate_linear_spectrum(
+    spectrum: np.ndarray,
+    index_columns: Dict[str, int],
+    mass_tolerance: Optional[float],
+    unit_mass_tolerance: Optional[str],
+):
+    """
+    Annotate a linear peptide spectrum.
+
+    :param spectrum: Spectrum to be annotated
+    :param index_columns: Index columns of the spectrum
+    :param mass_tolerance: Mass tolerance for calculating min and max mass
+    :param unit_mass_tolerance: Unit for the mass tolerance (da or ppm)
+    :return: Annotated spectrum
+    """
     mod_seq_column = "MODIFIED_SEQUENCE"
     if "MODIFIED_SEQUENCE_MSA" in index_columns:
         mod_seq_column = "MODIFIED_SEQUENCE_MSA"
-
     fragments_meta_data, tmt_n_term, unmod_sequence, calc_mass = initialize_peaks(
         spectrum[index_columns[mod_seq_column]],
         spectrum[index_columns["MASS_ANALYZER"]],
@@ -241,8 +389,6 @@ def parallel_annotate(
         mass_tolerance,
         unit_mass_tolerance,
     )
-    if not unmod_sequence:
-        return None
     matched_peaks = match_peaks(
         fragments_meta_data,
         spectrum[index_columns["INTENSITIES"]],
@@ -251,12 +397,88 @@ def parallel_annotate(
         unmod_sequence,
         spectrum[index_columns["PRECURSOR_CHARGE"]],
     )
+
     if len(matched_peaks) == 0:
         intensity = np.full(174, 0.0)
         mass = np.full(174, 0.0)
         return intensity, mass, calc_mass, 0
+
     matched_peaks, removed_peaks = handle_multiple_matches(matched_peaks)
     intensities, mass = generate_annotation_matrix(
         matched_peaks, unmod_sequence, spectrum[index_columns["PRECURSOR_CHARGE"]]
     )
     return intensities, mass, calc_mass, removed_peaks
+
+
+def _annotate_crosslinked_spectrum(
+    spectrum: np.ndarray,
+    index_columns: Dict[str, int],
+    crosslinker_type: str,
+    mass_tolerance: Optional[float] = None,
+    unit_mass_tolerance: Optional[str] = None,
+):
+    """
+    Annotate a crosslinked peptide spectrum.
+
+    :param spectrum: Spectrum to be annotated
+    :param index_columns: Index columns of the spectrum
+    :param crosslinker_type: Type of crosslinker used
+    :param mass_tolerance: Mass tolerance for calculating min and max mass
+    :param unit_mass_tolerance: Unit for the mass tolerance (da or ppm)
+    :raises ValueError: if unsupported crosslinker type was supplied.
+
+    :return: Annotated spectrum
+    """
+    if crosslinker_type in ["BS3", "DSS"]:
+        non_cl_xl = True
+    elif crosslinker_type in ["DSSO", "DSBU", "BUURBU"]:
+        non_cl_xl = False
+    else:
+        raise ValueError(f"Unsupported crosslinker type provided: {crosslinker_type}")
+
+    def _xl_annotation_workflow(seq_id: str, non_cl_xl: bool):
+        inputs = [
+            spectrum[index_columns[f"MODIFIED_SEQUENCE_{seq_id[0]}"]],
+            spectrum[index_columns["MASS_ANALYZER"]],
+            spectrum[index_columns[f"CROSSLINKER_POSITION_{seq_id[0]}"]],
+            crosslinker_type,
+            mass_tolerance,
+            unit_mass_tolerance,
+        ]
+        if non_cl_xl:
+            inputs.append(spectrum[index_columns[f"MODIFIED_SEQUENCE_{seq_id[1]}"]])
+            array_size = 174
+
+        else:
+            array_size = 348
+        fragments_meta_data, tmt_n_term, unmod_sequence, calc_mass = initialize_peaks_xl(*inputs)
+        matched_peaks = match_peaks(
+            fragments_meta_data,
+            np.array(spectrum[index_columns["INTENSITIES"]]),
+            np.array(spectrum[index_columns["MZ"]]),  # Convert to numpy array
+            tmt_n_term,
+            unmod_sequence,
+            spectrum[index_columns["PRECURSOR_CHARGE"]],
+        )
+
+        if len(matched_peaks) == 0:
+            intensities = np.full(array_size, 0.0)
+            mass = np.full(array_size, 0.0)
+            removed_peaks = 0
+        else:
+            matched_peaks, removed_peaks = handle_multiple_matches(matched_peaks)
+            if non_cl_xl:
+                intensities, mass = generate_annotation_matrix(
+                    matched_peaks, unmod_sequence, spectrum[index_columns["PRECURSOR_CHARGE"]]
+                )
+            else:
+                intensities, mass = generate_annotation_matrix_xl(
+                    matched_peaks, unmod_sequence, spectrum[index_columns[f"CROSSLINKER_POSITION_{seq_id[0]}"]]
+                )
+
+        return intensities, mass, removed_peaks, calc_mass
+
+    intensities_a, mass_a, removed_peaks_a, calc_mass_a = _xl_annotation_workflow(seq_id="AB", non_cl_xl=non_cl_xl)
+    intensities_b, mass_b, removed_peaks_b, calc_mass_b = _xl_annotation_workflow(seq_id="BA", non_cl_xl=non_cl_xl)
+
+    return intensities_a, intensities_b, mass_a, mass_b, calc_mass_a, calc_mass_b, removed_peaks_a, removed_peaks_b
diff --git a/spectrum_fundamentals/constants.py b/spectrum_fundamentals/constants.py
index 593627b..5f48777 100644
--- a/spectrum_fundamentals/constants.py
+++ b/spectrum_fundamentals/constants.py
@@ -14,7 +14,9 @@
 VEC_LENGTH = (
     (SEQ_LEN - 1) * 2 * 3
 )  # peptide of length 30 has 29 b and y-ions, each with charge 1+, 2+ and 3+, for a total of 174 fragments
-
+VEC_LENGTH_CMS2 = (SEQ_LEN - 1) * 2 * 3 * 2
+# peptide of length 30 can have 29 b, y, b_short, y_short, b_long and y_long ions, each with charge 1+, 2+ and 3+
+# we do not annotate fragments wth charge 3+. All fragmets with charge 3+ convert to -1
 #############
 # ALPHABETS #
 #############
@@ -51,6 +53,13 @@
     "K[UNIMOD:2016]": 22,
     "K[UNIMOD:214]": 22,
     "K[UNIMOD:730]": 22,
+    "K[UNIMOD:1896]": 22,
+    "K[UNIMOD:1898]": 22,
+    "K[UNIMOD:1884]": 23,
+    "K[UNIMOD:1881]": 24,
+    "K[UNIMOD:1882]": 25,
+    "K[UNIMOD:1885]": 26,
+    "K[UNIMOD:1886]": 27,
     "S[UNIMOD:21]": 25,
     "T[UNIMOD:21]": 26,
     "Y[UNIMOD:21]": 27,
@@ -173,12 +182,57 @@
     "[UNIMOD:730]": 304.205360,  # iTRAQ8
     "[UNIMOD:259]": 8.014199,  # SILAC Lysine
     "[UNIMOD:267]": 10.008269,  # SILAC Arginine
-    "[UNIMOD:21]": 79.966331,  # Phospho
-    "[UNIMOD:23]": -18.010565,  # Dehydration after phospho loss
-    "[UNIMOD:4]": 57.02146,  # Carbamidomethyl
-    "[UNIMOD:35]": 15.9949146,  # Oxidation
+    "[]": 0.0,
     "[UNIMOD:1]": 42.010565,  # Acetylation
+    "[UNIMOD:1896]": 158.003765,  # DSSO-crosslinker
+    "[UNIMOD:1881]": 54.010565,  # Alkene short fragment of DSSO-crosslinker
+    "[UNIMOD:1882]": 85.982635,  # Thiol long fragment of DSSO-crosslinker
+    "[UNIMOD:1884]": 196.084792,  # BuUrBu (DSBU)-crosslinker
+    "[UNIMOD:1885]": 111.032028,  # BuUr long fragment of BuUrBu (DSBU)-crosslinker
+    "[UNIMOD:1886]": 85.052764,  # Bu short fragment of BuUrBu (DSBU)-crosslinker
+    "[UNIMOD:1898]": 138.068080,  # DSS and BS3 non-cleavable crosslinker
+    "[UNIMOD:122]": 27.994915,  # Formylation
+    "[UNIMOD:1289]": 70.041865,  # Butyrylation
+    "[UNIMOD:1363]": 68.026215,  # Crotonylation
+    "[UNIMOD:1848]": 114.031694,  # Glutarylation
+    "[UNIMOD:1914]": -32.008456,  # Oxidation and then loss of oxidized M side chain
+    "[UNIMOD:2]": -0.984016,  # Amidation
+    "[UNIMOD:21]": 79.966331,  # Phosphorylation
+    "[UNIMOD:213]": 541.06111,  # ADP-ribosylation
+    "[UNIMOD:23]": -18.010565,  # Water Loss
+    "[UNIMOD:24]": 71.037114,  # Propionamidation
+    "[UNIMOD:354]": 44.985078,  # Nitrosylation
+    "[UNIMOD:28]": -17.026549,  # Glu to PyroGlu
+    "[UNIMOD:280]": 28.0313,  # Ethylation
+    "[UNIMOD:299]": 43.989829,  # Carboxylation
+    "[UNIMOD:3]": 226.077598,  # Biotinylation
+    "[UNIMOD:34]": 14.01565,  # Methylation
+    "[UNIMOD:345]": 47.984744,  # Trioxidation
+    "[UNIMOD:35]": 15.994915,  # Hydroxylation
+    "[UNIMOD:351]": 3.994915,  # Oxidation to Kynurenine
+    "[UNIMOD:36]": 28.0313,  # Dimethylation
+    "[UNIMOD:360]": -30.010565,  # Pyrrolidinone
+    "[UNIMOD:368]": -33.987721,  # Dehydroalanine
+    "[UNIMOD:37]": 42.04695,  # Trimethylation
+    "[UNIMOD:385]": -17.026549,  # Ammonia loss
+    "[UNIMOD:392]": 29.974179,  # Quinone
+    "[UNIMOD:4]": 57.021464,  # Carbamidomethyl
+    "[UNIMOD:40]": 79.956815,  # Sulfonation
+    "[UNIMOD:401]": -2.01565,  # Didehydro
+    "[UNIMOD:425]": 31.989829,  # Dioxidation
+    "[UNIMOD:43]": 203.079373,  # HexNAc
+    "[UNIMOD:44]": 204.187801,  # Farnesylation
+    "[UNIMOD:447]": -15.994915,  # Reduction
+    "[UNIMOD:46]": 229.014009,  # Pyridoxal phosphate
+    "[UNIMOD:47]": 238.229666,  # Palmitoylation
+    "[UNIMOD:5]": 43.005814,  # Carbamyl
+    "[UNIMOD:58]": 56.026215,  # Propionylation
+    "[UNIMOD:6]": 58.005479,  # Carboxymethylation
+    "[UNIMOD:64]": 100.016044,  # Succinylation
+    "[UNIMOD:7]": 0.984016,  # Deamidation
+    "[UNIMOD:747]": 86.000394,  # Malonylation
 }
+
 MOD_MASSES_SAGE = {
     229.1629: "[UNIMOD:737]",
     304.2071: "[UNIMOD:2016]",
@@ -206,6 +260,13 @@
     "S[UNIMOD:23]": AA_MASSES["S"],  # + MOD_MASSES['[UNIMOD:23]'],
     "T[UNIMOD:23]": AA_MASSES["T"],  # + MOD_MASSES['[UNIMOD:23]'],
     "Y[UNIMOD:23]": AA_MASSES["Y"],  # + MOD_MASSES['[UNIMOD:23]'],
+    "K[UNIMOD:1896]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1896]"],
+    "K[UNIMOD:1881]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1881]"],
+    "K[UNIMOD:1882]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1882]"],
+    "K[UNIMOD:1884]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1884]"],
+    "K[UNIMOD:1885]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1885]"],
+    "K[UNIMOD:1886]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1886]"],
+    "K[UNIMOD:1898]": AA_MASSES["K"] + MOD_MASSES["[UNIMOD:1898]"],
     "[UNIMOD:1]-": MASSES["N_TERMINUS"] + MOD_MASSES["[UNIMOD:1]"],
     "K[UNIMOD:259]": AA_MASSES[
         "K"
@@ -235,6 +296,30 @@
 DOUBLE_CHARGED_MASK = np.tile([0, 1, 0, 0, 1, 0], SEQ_LEN - 1)
 TRIPLE_CHARGED_MASK = np.tile([0, 0, 1, 0, 0, 1], SEQ_LEN - 1)
 
+B_ION_MASK_XL = np.tile([0, 0, 0, 1, 1, 1], (SEQ_LEN - 1) * 2)
+Y_ION_MASK_XL = np.tile([1, 1, 1, 0, 0, 0], (SEQ_LEN - 1) * 2)
+SINGLE_CHARGED_MASK_XL = np.tile([1, 0, 0, 1, 0, 0], (SEQ_LEN - 1) * 2)
+DOUBLE_CHARGED_MASK_XL = np.tile([0, 1, 0, 0, 1, 0], (SEQ_LEN - 1) * 2)
+TRIPLE_CHARGED_MASK_XL = np.tile([0, 0, 1, 0, 0, 1], (SEQ_LEN - 1) * 2)
+
+
+MASK_DICT = {
+    1: SINGLE_CHARGED_MASK,
+    2: DOUBLE_CHARGED_MASK,
+    3: TRIPLE_CHARGED_MASK,
+    4: B_ION_MASK,
+    5: Y_ION_MASK,
+}
+
+
+MASK_DICT_XL = {
+    1: SINGLE_CHARGED_MASK_XL,
+    2: DOUBLE_CHARGED_MASK_XL,
+    3: TRIPLE_CHARGED_MASK_XL,
+    4: B_ION_MASK_XL,
+    5: Y_ION_MASK_XL,
+}
+
 
 SHARED_DATA_COLUMNS = ["RAW_FILE", "SCAN_NUMBER"]
 META_DATA_ONLY_COLUMNS = [
diff --git a/spectrum_fundamentals/fragments.py b/spectrum_fundamentals/fragments.py
index 55fe022..b781e75 100644
--- a/spectrum_fundamentals/fragments.py
+++ b/spectrum_fundamentals/fragments.py
@@ -1,69 +1,49 @@
 import logging
+import re
 from operator import itemgetter
 from typing import Dict, List, Optional, Tuple
 
 import numpy as np
+import pandas as pd
 
 from . import constants as constants
+from .mod_string import internal_without_mods
 
 logger = logging.getLogger(__name__)
 
 
-def _get_modifications(peptide_sequence: str) -> Optional[Tuple[Dict[int, float], int, str]]:
+def _get_modifications(peptide_sequence: str) -> Dict[int, float]:
     """
     Get modification masses and position in a peptide sequence.
 
+    This function expects a peptide sequence in unimod format, parses the modifications and stores
+    the mass deltas for each position off aa in a dictionary where keys are the position in the
+    unmodified sequence and values are the masses of the modifications attached to the aa at that
+    position. In case of an n-terminal modification, it is stored at position -2 (technical reasons)
+    The mass deltas along with the unmodified sequence and information about whether an n-terminal
+    modification was present are returned.
+
     :param peptide_sequence: Modified peptide sequence
-    :return: tuple with - dictionary of modification_position => mod_mass
-                        - 2 if there is an isobaric tag on the n-terminal, else 1
-                        - sequence without modifications
+    :return: modification_deltas
     """
     modification_deltas = {}
-    tmt_n_term = 1
-    modifications = constants.MOD_MASSES.keys()
-    modification_mass = constants.MOD_MASSES
-    # Handle terminal modifications here
-    for possible_tmt_mod in constants.TMT_MODS.values():
-        n_term_tmt = possible_tmt_mod + "-"
-        if peptide_sequence.startswith(n_term_tmt):
-            tmt_n_term = 2
-            modification_deltas.update({0: constants.MOD_MASSES[possible_tmt_mod]})
-            peptide_sequence = peptide_sequence[len(n_term_tmt) :]
-            break
-
-    if "(" in peptide_sequence:
-        logger.info(
-            "Error Modification "
-            + peptide_sequence[peptide_sequence.find("(") + 1 : peptide_sequence.find(")")]
-            + " not "
-            "found"
-        )
-        return None
+    offset = 1  # shift position of mod start in seq by one to the left to reflect position of aa
+    if peptide_sequence.startswith("["):  # n-term mod => seq must be [UNIMOD:xyz]-X...
+        offset = 2  # need to add one more offset, because of the dash '-', n_terminal stored at -1
 
-    while "[" in peptide_sequence:
-        found_modification = False
-        modification_index = peptide_sequence.index("[")
-        for mod in modifications:
-            if peptide_sequence[modification_index : modification_index + len(mod)] == mod:
-                if modification_index - 1 in modification_deltas:
-                    modification_deltas.update(
-                        {modification_index - 1: modification_deltas[modification_index - 1] + modification_mass[mod]}
-                    )
-                else:
-                    modification_deltas.update({modification_index - 1: modification_mass[mod]})
-                peptide_sequence = (
-                    peptide_sequence[0:modification_index] + peptide_sequence[modification_index + len(mod) :]
-                )
-                found_modification = True
-        if not found_modification:
-            logger.info(
-                "Error Modification "
-                + peptide_sequence[modification_index : peptide_sequence.find("]") + 1]
-                + " not found"
-            )
-            return None
+    # fastest regex for modification mathing without lookback, since we know it must be unimod syntax
+    # .{8} skips 8 positions entirely without checking greedily, that is len("UNIMOD:") + at least one digit
+    # [^\]*] matches anything but ] greedily till it finds the closing bracket, which is 1 step
+    pattern = re.compile(r"\[.{8}[^\]]*\]")
+    matches = pattern.finditer(peptide_sequence)
 
-    return modification_deltas, tmt_n_term, peptide_sequence
+    for match in matches:
+        start_pos = match.start()
+        end_pos = match.end()
+        modification_deltas[start_pos - offset] = constants.MOD_MASSES[peptide_sequence[start_pos:end_pos]]
+        offset += end_pos - start_pos
+
+    return modification_deltas
 
 
 def compute_peptide_mass(sequence: str) -> float:
@@ -71,40 +51,34 @@ def compute_peptide_mass(sequence: str) -> float:
     Compute the theoretical mass of the peptide sequence.
 
     :param sequence: Modified peptide sequence
-    :raises AssertionError: if an unknown modification has been found in the peptide sequence
     :return: Theoretical mass of the sequence
     """
-    peptide_sequence = sequence
-    modifications = _get_modifications(peptide_sequence)
-    if modifications is None:
-        raise AssertionError("Modification not found.")
-    else:
-        modification_deltas, tmt_n_term, peptide_sequence = modifications
+    terminal_masses = 2 * constants.ATOM_MASSES["H"] + constants.ATOM_MASSES["O"]  # add terminal masses HO- and H-
 
-    peptide_length = len(peptide_sequence)
-    if peptide_length > 30:
-        # return [], -1, ""
-        return -1.0
+    modification_deltas = _get_modifications(sequence)
+    if modification_deltas:  # there were modifictions
+        sequence = internal_without_mods([sequence])[0]
+        terminal_masses += modification_deltas.get(-2, 0.0)  # prime with n_term_mod delta if present
 
-    n_term_delta = 0.0
+    peptide_sum = sum([constants.AA_MASSES[c] + modification_deltas.get(i, 0.0) for i, c in enumerate(sequence)])
 
-    # get mass delta for the c-terminus
-    c_term_delta = 0.0
+    return terminal_masses + peptide_sum
 
-    n_term = constants.ATOM_MASSES["H"] + n_term_delta  # n-terminal delta [N]
-    c_term = constants.ATOM_MASSES["O"] + constants.ATOM_MASSES["H"] + c_term_delta  # c-terminal delta [C]
-    h = constants.ATOM_MASSES["H"]
 
-    ion_type_offsets = [n_term - h, c_term + h]
-
-    # calculation:
-    forward_sum = 0.0  # sum over all amino acids from left to right (neutral charge)
+def _xl_sanity_check(noncl_xl: int, peptide_beta_mass: float, xl_pos: float):
+    """
+    Checks input validity for initialize_peacks when used with xl mode.
 
-    for i in range(0, peptide_length):  # generate substrings
-        forward_sum += constants.AA_MASSES[peptide_sequence[i]]  # sum left to right
-        if i in modification_deltas:  # add mass of modification if present
-            forward_sum += modification_deltas[i]
-    return forward_sum + ion_type_offsets[0] + ion_type_offsets[1]
+    :param noncl_xl: whether the function is called with a non-cleavable xl modification
+    :param peptide_beta_mass: the mass of the second peptide to be considered for non-cleavable XL
+    :param xl_pos: the position of the crosslinker for non-cleavable XL
+    :raises ValueError: if non_cl_xl is 1 but no xl_pos and peptide_beta_mass has been supplied.
+    """
+    if noncl_xl == 1:
+        if peptide_beta_mass == 0.0:
+            raise ValueError("Peptide_beta_mass must be provided. Please check your input data.")
+        if xl_pos == -1:
+            raise ValueError("Crosslinker position must be provided if using non cleavable XL mode.")
 
 
 def initialize_peaks(
@@ -113,6 +87,9 @@ def initialize_peaks(
     charge: int,
     mass_tolerance: Optional[float] = None,
     unit_mass_tolerance: Optional[str] = None,
+    noncl_xl: bool = False,
+    peptide_beta_mass: float = 0.0,
+    xl_pos: int = -1,
 ) -> Tuple[List[dict], int, str, float]:
     """
     Generate theoretical peaks for a modified peptide sequence.
@@ -122,40 +99,15 @@ def initialize_peaks(
     :param charge: Precursor charge
     :param mass_tolerance: mass tolerance to calculate min and max mass
     :param unit_mass_tolerance: unit for the mass tolerance (da or ppm)
-    :raises AssertionError:  if peptide sequence contained an unknown modification. TODO do this within the get_mod func.
+    :param noncl_xl: whether the function is called with a non-cleavable xl modification
+    :param peptide_beta_mass: the mass of the second peptide to be considered for non-cleavable XL
+    :param xl_pos: the position of the crosslinker for non-cleavable XL
     :return: List of theoretical peaks, Flag to indicate if there is a tmt on n-terminus, Un modified peptide sequence
     """
-    peptide_sequence = sequence
-    modifications = _get_modifications(peptide_sequence)
-    if modifications is None:
-        raise AssertionError("Modification not found.")
-    else:
-        modification_deltas, tmt_n_term, peptide_sequence = modifications
+    _xl_sanity_check(noncl_xl, peptide_beta_mass, xl_pos)
 
-    peptide_length = len(peptide_sequence)
-    if peptide_length > 30:
-        return [{}], -1, "", 0.0
-
-    # initialize constants
-    if int(round(charge)) <= 3:
-        max_charge = int(round(charge))
-    else:
-        max_charge = 3
-
-    n_term_delta = 0.0
-
-    # get mass delta for the c-terminus
-    c_term_delta = 0.0
-
-    n_term = constants.ATOM_MASSES["H"] + n_term_delta  # n-terminal delta [N]
-    c_term = constants.ATOM_MASSES["O"] + constants.ATOM_MASSES["H"] + c_term_delta  # c-terminal delta [C]
-
-    # cho = constants.ATOM_MASSES["H"] + constants.ATOM_MASSES["C"] + constants.ATOM_MASSES["O"]
-    h = constants.ATOM_MASSES["H"]
-    # co = constants.ATOM_MASSES["C"] + constants.ATOM_MASSES["O"]
-    # nh2 = constants.ATOM_MASSES["N"] + constants.ATOM_MASSES["H"] * 2.0
-
-    ion_type_offsets = [n_term - h, c_term + h]
+    max_charge = min(3, charge)
+    ion_type_offsets = [0.0, constants.ATOM_MASSES["O"] + 2 * constants.ATOM_MASSES["H"]]
 
     # tmp place holder
     ion_type_masses = [0.0, 0.0]
@@ -163,14 +115,27 @@ def initialize_peaks(
 
     number_of_ion_types = len(ion_type_offsets)
     fragments_meta_data = []
-    # calculation:
+
+    modification_deltas = _get_modifications(sequence)
     forward_sum = 0.0  # sum over all amino acids from left to right (neutral charge)
     backward_sum = 0.0  # sum over all amino acids from right to left (neutral charge)
-    for i in range(0, peptide_length):  # generate substrings
-        forward_sum += constants.AA_MASSES[peptide_sequence[i]]  # sum left to right
+    n_term_mod = 1
+    if modification_deltas:  # there were modifictions
+        sequence = internal_without_mods([sequence])[0]
+        n_term_delta = modification_deltas.get(-2, 0.0)
+        if n_term_delta != 0:
+            n_term_mod = 2
+            # add n_term mass to first aa for easy processing in the following calculation
+            modification_deltas[0] = modification_deltas.get(0, 0.0) + n_term_delta
+
+    # calculation:
+
+    peptide_length = len(sequence)
+    for i in range(peptide_length):  # generate substrings
+        forward_sum += constants.AA_MASSES[sequence[i]]  # sum left to right
         if i in modification_deltas:  # add mass of modification if present
             forward_sum += modification_deltas[i]
-        backward_sum += constants.AA_MASSES[peptide_sequence[peptide_length - i - 1]]  # sum right to left
+        backward_sum += constants.AA_MASSES[sequence[peptide_length - i - 1]]  # sum right to left
         if peptide_length - i - 1 in modification_deltas:  # add mass of modification if present
             backward_sum += modification_deltas[peptide_length - i - 1]
 
@@ -181,22 +146,166 @@ def initialize_peaks(
         for charge in range(constants.MIN_CHARGE, max_charge + 1):  # generate ion in different charge states
             # positive charge is introduced by protons (or H - ELECTRON_MASS)
             charge_delta = charge * constants.PARTICLE_MASSES["PROTON"]
-            for ion_type in range(0, number_of_ion_types):  # generate all ion types
-                # Check for neutral loss here
-                mass = (ion_type_masses[ion_type] + charge_delta) / charge
-                min_mass, max_mass = get_min_max_mass(mass_analyzer, mass, mass_tolerance, unit_mass_tolerance)
+            for ion_type in range(number_of_ion_types):  # generate all ion types
+                mass = _compute_ion_mass(
+                    ion_mass=ion_type_masses[ion_type],
+                    noncl_xl=noncl_xl,
+                    ion_type=ion_type,
+                    xl_pos=xl_pos,
+                    peptide_beta_mass=peptide_beta_mass,
+                    peptide_length=peptide_length,
+                    i=i,
+                )
+                mz = (mass + charge_delta) / charge
+                min_mz, max_mz = get_min_max_mass(mass_analyzer, mz, mass_tolerance, unit_mass_tolerance)
+
                 fragments_meta_data.append(
                     {
                         "ion_type": ion_types[ion_type],  # ion type
                         "no": i + 1,  # no
                         "charge": charge,  # charge
-                        "mass": mass,  # mass
-                        "min_mass": min_mass,  # min mass
-                        "max_mass": max_mass,  # max mass
+                        "mass": mz,  # mz
+                        "min_mass": min_mz,  # min mz
+                        "max_mass": max_mz,  # max mz
                     }
                 )
     fragments_meta_data = sorted(fragments_meta_data, key=itemgetter("mass"))
-    return fragments_meta_data, tmt_n_term, peptide_sequence, (forward_sum + ion_type_offsets[0] + ion_type_offsets[1])
+    return fragments_meta_data, n_term_mod, sequence, (forward_sum + ion_type_offsets[0] + ion_type_offsets[1])
+
+
+def _compute_ion_mass(
+    ion_mass: float, noncl_xl: bool, ion_type: int, xl_pos: int, peptide_beta_mass: float, peptide_length: int, i: int
+) -> float:
+    # Check for neutral loss here
+
+    if noncl_xl and ((ion_type == 0 and i + 1 >= xl_pos) or (ion_type == 1 and i >= peptide_length - xl_pos)):
+        mass = ion_mass + peptide_beta_mass
+    else:
+        mass = ion_mass
+
+    return mass
+
+
+def initialize_peaks_xl(
+    sequence: str,
+    mass_analyzer: str,
+    crosslinker_position: int,
+    crosslinker_type: str,
+    mass_tolerance: Optional[float] = None,
+    unit_mass_tolerance: Optional[str] = None,
+    sequence_beta: Optional[str] = None,
+) -> Tuple[List[dict], int, str, float]:
+    """
+    Generate theoretical peaks for a modified (potentially cleavable cross-linked) peptide sequence.
+
+    This function get only one modified peptide (peptide a or b))
+
+    :param sequence: Modified peptide sequence (peptide a or b)
+    :param mass_analyzer: Type of mass analyzer used eg. FTMS, ITMS
+    :param crosslinker_position: The position of crosslinker
+    :param crosslinker_type: Can be either DSSO, DSBU or BuUrBU
+    :param mass_tolerance: mass tolerance to calculate min and max mass
+    :param unit_mass_tolerance: unit for the mass tolerance (da or ppm)
+    :param sequence_beta: optional second peptide to be considered for non-cleavable XL
+    :raises ValueError: if crosslinker_type is unkown
+    :raises AssertionError: if the short and long XL sequence (the one with the short / long crosslinker mod)
+        has a tmt n term while the other one does not
+    :return: List of theoretical peaks, flag to indicate if there is a tmt on n-terminus, unmodified peptide
+        sequence, therotical mass of modified peptide (without considering mass of crosslinker)
+    """
+    crosslinker_type = crosslinker_type.upper()
+
+    if crosslinker_type in ["DSSO", "DSBU", "BUURBU"]:  # cleavable XL
+        charge = 2  # generate only peaks with charge 1 and 2
+        if crosslinker_type == "DSSO":
+            dsso = "[UNIMOD:1896]"
+            dsso_s = "[UNIMOD:1881]"
+            dsso_l = "[UNIMOD:1882]"
+            sequence_s = sequence.replace(dsso, dsso_s)
+            sequence_l = sequence.replace(dsso, dsso_l)
+            sequence_without_crosslinker = sequence.replace(dsso, "")
+
+        elif crosslinker_type in ["DSBU", "BUURBU"]:
+            dsbu = "[UNIMOD:1884]"
+            dsbu_s = "[UNIMOD:1886]"
+            dsbu_l = "[UNIMOD:1885]"
+            sequence_s = sequence.replace(dsbu, dsbu_s)
+            sequence_l = sequence.replace(dsbu, dsbu_l)
+            sequence_without_crosslinker = sequence.replace(dsbu, "")
+
+        # TODO: this needs to be done more efficiently:
+        # currently calculating the non-cleaved part until the xl_pos of the ions two times!
+        # also the peptide sequence, mass and modifications are called twice
+        # need to separate all of these functions better!
+        # for XL, we actually don't need mass_s / mass_l at the moment because only one mass, without
+        # the crosslinker is returned! This needs to be fixed, because mass is used as CALCULATED_MASS in
+        # percolator!
+
+        list_out_s, tmt_n_term_s, peptide_sequence, _ = initialize_peaks(
+            sequence_s, mass_analyzer, charge, mass_tolerance, unit_mass_tolerance
+        )
+        list_out_l, tmt_n_term_l, peptide_sequence, _ = initialize_peaks(
+            sequence_l, mass_analyzer, charge, mass_tolerance, unit_mass_tolerance
+        )
+
+        tmt_n_term = tmt_n_term_s
+        if tmt_n_term_s ^ tmt_n_term_l:
+            raise AssertionError("tmt_mod is {tmt_n_term_s} for short sequence but {tmt_n_term_l} for long sequence!")
+
+        df_out_s = pd.DataFrame(list_out_s)
+        df_out_l = pd.DataFrame(list_out_l)
+
+        threshold_b = crosslinker_position
+        threshold_y = len(peptide_sequence) - crosslinker_position + 1
+
+        df_out_s.loc[(df_out_s["no"] >= threshold_b) & (df_out_s["ion_type"] == "b"), "ion_type"] = "b-short"
+        df_out_s.loc[(df_out_s["no"] >= threshold_y) & (df_out_s["ion_type"] == "y"), "ion_type"] = "y-short"
+        df_out_l.loc[(df_out_l["no"] >= threshold_b) & (df_out_l["ion_type"] == "b"), "ion_type"] = "b-long"
+        df_out_l.loc[(df_out_l["no"] >= threshold_y) & (df_out_l["ion_type"] == "y"), "ion_type"] = "y-long"
+
+        concatenated_df = pd.concat([df_out_s, df_out_l])
+        unique_df = concatenated_df.drop_duplicates()  # TODO: this is where the duplicate calculations are removed
+        df_out = unique_df.sort_values("mass")
+        mass = compute_peptide_mass(sequence_without_crosslinker)
+
+    elif crosslinker_type in ["BS3", "DSS"]:  # non-cleavable XL
+        charge = 3  # generate only peaks with charge 1, 2 and 3
+
+        sequence_without_crosslinker = sequence.replace("[UNIMOD:1898]", "")
+        if sequence_beta is not None:
+            sequence_beta_without_crosslinker = sequence_beta.replace("[UNIMOD:1898]", "")
+        else:
+            raise ValueError("sequence_beta cannot be None. Please check your input data.")
+        sequence_mass = compute_peptide_mass(sequence_without_crosslinker)
+        sequence_beta_mass = compute_peptide_mass(sequence_beta_without_crosslinker)
+
+        list_out, tmt_n_term, peptide_sequence, _ = initialize_peaks(
+            sequence,
+            mass_analyzer,
+            charge,
+            mass_tolerance,
+            unit_mass_tolerance,
+            True,
+            sequence_beta_mass if sequence_beta_mass is not None else None,
+            crosslinker_position,
+        )
+        df_out = pd.DataFrame(list_out)
+
+        threshold_b_alpha = crosslinker_position
+        threshold_y_alpha = len(peptide_sequence) - crosslinker_position + 1
+
+        df_out.loc[(df_out["no"] >= threshold_b_alpha) & (df_out["ion_type"] == "b"), "ion_type"] = "b-xl"
+        df_out.loc[(df_out["no"] >= threshold_y_alpha) & (df_out["ion_type"] == "y"), "ion_type"] = "y-xl"
+
+        df_out = df_out.sort_index()
+        unique_df = df_out.drop_duplicates()
+        df_out = unique_df.sort_values("mass")
+        mass = sequence_mass
+
+    else:
+        raise ValueError(f"Unkown crosslinker type: {crosslinker_type}")
+
+    return df_out.to_dict(orient="records"), tmt_n_term, peptide_sequence, mass
 
 
 def get_min_max_mass(
diff --git a/spectrum_fundamentals/metrics/fragments_ratio.py b/spectrum_fundamentals/metrics/fragments_ratio.py
index 71ff9ee..0c0de6f 100644
--- a/spectrum_fundamentals/metrics/fragments_ratio.py
+++ b/spectrum_fundamentals/metrics/fragments_ratio.py
@@ -31,7 +31,9 @@ class FragmentsRatio(Metric):
 
     @staticmethod
     def count_with_ion_mask(
-        boolean_array: scipy.sparse.csr_matrix, ion_mask: Optional[Union[np.ndarray, scipy.sparse.spmatrix]] = None
+        boolean_array: scipy.sparse.csr_matrix,
+        ion_mask: Optional[Union[np.ndarray, scipy.sparse.spmatrix]] = None,
+        xl: bool = False,
     ) -> np.ndarray:
         """
         Count the number of ions.
@@ -39,11 +41,18 @@ def count_with_ion_mask(
         :param boolean_array: boolean array with True for observed/predicted peaks and \
                               False for missing observed/predicted peaks, array of length 174
         :param ion_mask: mask with 1s for the ions that should be counted and 0s for ions that should be ignored, \
-                         integer array of length 174
+                         integer array of length 174 for linear and 348 for crosslinked peptides, or a list of integers,
+                         or a scipy.sparse.csr_matrix or scipy.sparse._csc.csc_matrix.
+        :param xl: whether to process with crosslinked or linear peptides
         :return: number of observed/predicted peaks not masked by ion_mask
         """
+        if xl:
+            array_size = 348
+        else:
+            array_size = 174
+
         if ion_mask is None:
-            ion_mask = scipy.sparse.csr_matrix(np.ones((174, 1)))
+            ion_mask = scipy.sparse.csr_matrix(np.ones((array_size, 1)))
         else:
             ion_mask = scipy.sparse.csr_matrix(ion_mask).T
         return scipy.sparse.csr_matrix.dot(boolean_array, ion_mask).toarray().flatten()
@@ -53,6 +62,7 @@ def count_observation_states(
         observation_state: scipy.sparse.csr_matrix,
         test_state: int,
         ion_mask: Optional[Union[np.ndarray, scipy.sparse.csr_matrix]] = None,
+        xl: bool = False,
     ) -> np.ndarray:
         """
         Count the number of observation states.
@@ -61,10 +71,11 @@ def count_observation_states(
         :param test_state: integer for the test observation state
         :param ion_mask: mask with 1s for the ions that should be counted and 0s for ions that should be ignored, \
                          integer array of length 174
+        :param xl: whether or not the function is executed with xl mode
         :return: number of observation states equal to test_state per row
         """
         state_boolean = observation_state == test_state
-        return FragmentsRatio.count_with_ion_mask(state_boolean, ion_mask)
+        return FragmentsRatio.count_with_ion_mask(state_boolean, ion_mask, xl=xl)
 
     @staticmethod
     def get_mask_observed_valid(observed_mz: scipy.sparse.csr_matrix) -> scipy.sparse.csr_matrix:
@@ -115,7 +126,6 @@ def get_observation_state(
         """
         if scipy.sparse.issparse(observed_boolean):
             observation_state = scipy.sparse.csr_matrix(observed_boolean.shape, dtype=int)
-            print(observation_state)
         else:
             observation_state = np.zeros_like(observed_boolean, dtype=int)
         observation_state += observed_boolean.multiply(predicted_boolean) * int(ObservationState.OBS_AND_PRED)
@@ -126,166 +136,448 @@ def get_observation_state(
         )
         return observation_state
 
-    def calc(self):
+    def calc(self, xl: bool = False):
         """Adds columns with count, fraction and fraction_predicted features to metrics_val dataframe."""
-        mask_observed_valid = FragmentsRatio.get_mask_observed_valid(self.true_intensities)
-        observed_boolean = FragmentsRatio.make_boolean(self.true_intensities, mask_observed_valid)
-        predicted_boolean = FragmentsRatio.make_boolean(self.pred_intensities, mask_observed_valid, cutoff=0.05)
-        observation_state = FragmentsRatio.get_observation_state(
-            observed_boolean, predicted_boolean, mask_observed_valid
-        )
-        valid_ions = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid))
-        valid_ions_b = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid, constants.B_ION_MASK))
-        valid_ions_y = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid, constants.Y_ION_MASK))
-
-        # counting metrics
-        self.metrics_val["count_predicted"] = FragmentsRatio.count_with_ion_mask(predicted_boolean)
-        self.metrics_val["count_predicted_b"] = FragmentsRatio.count_with_ion_mask(
-            predicted_boolean, constants.B_ION_MASK
-        )
-        self.metrics_val["count_predicted_y"] = FragmentsRatio.count_with_ion_mask(
-            predicted_boolean, constants.Y_ION_MASK
-        )
-
-        self.metrics_val["count_observed"] = FragmentsRatio.count_with_ion_mask(observed_boolean)
-        self.metrics_val["count_observed_b"] = FragmentsRatio.count_with_ion_mask(
-            observed_boolean, constants.B_ION_MASK
-        )
-        self.metrics_val["count_observed_y"] = FragmentsRatio.count_with_ion_mask(
-            observed_boolean, constants.Y_ION_MASK
-        )
-
-        self.metrics_val["count_observed_and_predicted"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.OBS_AND_PRED
-        )
-        self.metrics_val["count_observed_and_predicted_b"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.OBS_AND_PRED, constants.B_ION_MASK
-        )
-        self.metrics_val["count_observed_and_predicted_y"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.OBS_AND_PRED, constants.Y_ION_MASK
-        )
-
-        self.metrics_val["count_not_observed_and_not_predicted"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.NOT_OBS_AND_NOT_PRED
-        )
-        self.metrics_val["count_not_observed_and_not_predicted_b"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.NOT_OBS_AND_NOT_PRED, constants.B_ION_MASK
-        )
-        self.metrics_val["count_not_observed_and_not_predicted_y"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.NOT_OBS_AND_NOT_PRED, constants.Y_ION_MASK
-        )
-
-        self.metrics_val["count_observed_but_not_predicted"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.OBS_BUT_NOT_PRED
-        )
-        self.metrics_val["count_observed_but_not_predicted_b"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.OBS_BUT_NOT_PRED, constants.B_ION_MASK
-        )
-        self.metrics_val["count_observed_but_not_predicted_y"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.OBS_BUT_NOT_PRED, constants.Y_ION_MASK
-        )
-
-        self.metrics_val["count_not_observed_but_predicted"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.NOT_OBS_BUT_PRED
-        )
-        self.metrics_val["count_not_observed_but_predicted_b"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.NOT_OBS_BUT_PRED, constants.B_ION_MASK
-        )
-        self.metrics_val["count_not_observed_but_predicted_y"] = FragmentsRatio.count_observation_states(
-            observation_state, ObservationState.NOT_OBS_BUT_PRED, constants.Y_ION_MASK
-        )
-
-        # fractional count metrics
-        self.metrics_val["fraction_predicted"] = self.metrics_val["count_predicted"].values / valid_ions
-        self.metrics_val["fraction_predicted_b"] = self.metrics_val["count_predicted_b"] / valid_ions_b
-        self.metrics_val["fraction_predicted_y"] = self.metrics_val["count_predicted_y"] / valid_ions_y
-
-        self.metrics_val["fraction_observed"] = self.metrics_val["count_observed"] / valid_ions
-        self.metrics_val["fraction_observed_b"] = self.metrics_val["count_observed_b"] / valid_ions_b
-        self.metrics_val["fraction_observed_y"] = self.metrics_val["count_observed_y"] / valid_ions_y
-
-        self.metrics_val["fraction_observed_and_predicted"] = (
-            self.metrics_val["count_observed_and_predicted"] / valid_ions
-        )
-        self.metrics_val["fraction_observed_and_predicted_b"] = (
-            self.metrics_val["count_observed_and_predicted_b"] / valid_ions_b
-        )
-        self.metrics_val["fraction_observed_and_predicted_y"] = (
-            self.metrics_val["count_observed_and_predicted_y"] / valid_ions_y
-        )
-
-        self.metrics_val["fraction_not_observed_and_not_predicted"] = (
-            self.metrics_val["count_not_observed_and_not_predicted"] / valid_ions
-        )
-        self.metrics_val["fraction_not_observed_and_not_predicted_b"] = (
-            self.metrics_val["count_not_observed_and_not_predicted_b"] / valid_ions_b
-        )
-        self.metrics_val["fraction_not_observed_and_not_predicted_y"] = (
-            self.metrics_val["count_not_observed_and_not_predicted_y"] / valid_ions_y
-        )
-
-        self.metrics_val["fraction_observed_but_not_predicted"] = (
-            self.metrics_val["count_observed_but_not_predicted"] / valid_ions
-        )
-        self.metrics_val["fraction_observed_but_not_predicted_b"] = (
-            self.metrics_val["count_observed_but_not_predicted_b"] / valid_ions_b
-        )
-        self.metrics_val["fraction_observed_but_not_predicted_y"] = (
-            self.metrics_val["count_observed_but_not_predicted_y"] / valid_ions_y
-        )
-
-        self.metrics_val["fraction_not_observed_but_predicted"] = (
-            self.metrics_val["count_not_observed_but_predicted"] / valid_ions
-        )
-        self.metrics_val["fraction_not_observed_but_predicted_b"] = (
-            self.metrics_val["count_not_observed_but_predicted_b"] / valid_ions_b
-        )
-        self.metrics_val["fraction_not_observed_but_predicted_y"] = (
-            self.metrics_val["count_not_observed_but_predicted_y"] / valid_ions_y
-        )
-
-        # fractional count metrics relative to predictions
-        num_predicted_ions = np.maximum(1, self.metrics_val["count_predicted"])
-        num_predicted_ions_b = np.maximum(1, self.metrics_val["count_predicted_b"])
-        num_predicted_ions_y = np.maximum(1, self.metrics_val["count_predicted_y"])
-
-        self.metrics_val["fraction_observed_and_predicted_vs_predicted"] = (
-            self.metrics_val["count_observed_and_predicted"] / num_predicted_ions
-        )
-        self.metrics_val["fraction_observed_and_predicted_b_vs_predicted_b"] = (
-            self.metrics_val["count_observed_and_predicted_b"] / num_predicted_ions_b
-        )
-        self.metrics_val["fraction_observed_and_predicted_y_vs_predicted_y"] = (
-            self.metrics_val["count_observed_and_predicted_y"] / num_predicted_ions_y
-        )
-
-        self.metrics_val["fraction_not_observed_and_not_predicted_vs_predicted"] = (
-            self.metrics_val["count_not_observed_and_not_predicted"] / num_predicted_ions
-        )
-        self.metrics_val["fraction_not_observed_and_not_predicted_b_vs_predicted_b"] = (
-            self.metrics_val["count_not_observed_and_not_predicted_b"] / num_predicted_ions_b
-        )
-        self.metrics_val["fraction_not_observed_and_not_predicted_y_vs_predicted_y"] = (
-            self.metrics_val["count_not_observed_and_not_predicted_y"] / num_predicted_ions_y
-        )
+        if self.true_intensities is None or self.pred_intensities is None:
+            return None
+        if xl:
+            true_intensities_a = self.true_intensities[:, 0:348]
+            true_intensities_b = self.true_intensities[:, 348:]
+            pred_intensities_a = self.pred_intensities[:, 0:348]
+            pred_intensities_b = self.pred_intensities[:, 348:]
+            mask_observed_valid_a = FragmentsRatio.get_mask_observed_valid(true_intensities_a)
+            mask_observed_valid_b = FragmentsRatio.get_mask_observed_valid(true_intensities_b)
+            observed_boolean_a = FragmentsRatio.make_boolean(true_intensities_a, mask_observed_valid_a)
+            observed_boolean_b = FragmentsRatio.make_boolean(true_intensities_b, mask_observed_valid_b)
+            predicted_boolean_a = FragmentsRatio.make_boolean(pred_intensities_a, mask_observed_valid_a, cutoff=0.05)
+            predicted_boolean_b = FragmentsRatio.make_boolean(pred_intensities_b, mask_observed_valid_b, cutoff=0.05)
+            observation_state_a = FragmentsRatio.get_observation_state(
+                observed_boolean_a, predicted_boolean_a, mask_observed_valid_a
+            )
+            observation_state_b = FragmentsRatio.get_observation_state(
+                observed_boolean_b, predicted_boolean_b, mask_observed_valid_b
+            )
+            valid_ions_a = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid_a, xl=True))
+            valid_ions_b = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid_b, xl=True))
+            valid_ions_b_a = np.maximum(
+                1, FragmentsRatio.count_with_ion_mask(mask_observed_valid_a, constants.B_ION_MASK_XL, xl=True)
+            )
+            valid_ions_b_b = np.maximum(
+                1, FragmentsRatio.count_with_ion_mask(mask_observed_valid_b, constants.B_ION_MASK_XL, xl=True)
+            )
+            valid_ions_y_a = np.maximum(
+                1, FragmentsRatio.count_with_ion_mask(mask_observed_valid_a, constants.Y_ION_MASK_XL, xl=True)
+            )
+            valid_ions_y_b = np.maximum(
+                1, FragmentsRatio.count_with_ion_mask(mask_observed_valid_b, constants.Y_ION_MASK_XL, xl=True)
+            )
+            # counting metrics
+            self.metrics_val["count_predicted_a"] = FragmentsRatio.count_with_ion_mask(predicted_boolean_a, xl=True)
+            self.metrics_val["count_predicted_b"] = FragmentsRatio.count_with_ion_mask(predicted_boolean_b, xl=True)
+            self.metrics_val["count_predicted_b_a"] = FragmentsRatio.count_with_ion_mask(
+                predicted_boolean_a, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_predicted_b_b"] = FragmentsRatio.count_with_ion_mask(
+                predicted_boolean_b, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_predicted_y_a"] = FragmentsRatio.count_with_ion_mask(
+                predicted_boolean_a, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_predicted_y_b"] = FragmentsRatio.count_with_ion_mask(
+                predicted_boolean_b, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_a"] = FragmentsRatio.count_with_ion_mask(observed_boolean_a, xl=True)
+            self.metrics_val["count_observed_b"] = FragmentsRatio.count_with_ion_mask(observed_boolean_b, xl=True)
+            self.metrics_val["count_observed_b_a"] = FragmentsRatio.count_with_ion_mask(
+                observed_boolean_a, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_b_b"] = FragmentsRatio.count_with_ion_mask(
+                observed_boolean_b, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_y_a"] = FragmentsRatio.count_with_ion_mask(
+                observed_boolean_a, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_y_b"] = FragmentsRatio.count_with_ion_mask(
+                observed_boolean_b, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_and_predicted_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.OBS_AND_PRED, xl=True
+            )
+            self.metrics_val["count_observed_and_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.OBS_AND_PRED, xl=True
+            )
+            self.metrics_val["count_observed_and_predicted_b_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.OBS_AND_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_and_predicted_b_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.OBS_AND_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_and_predicted_y_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.OBS_AND_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_and_predicted_y_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.OBS_AND_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.NOT_OBS_AND_NOT_PRED, xl=True
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.NOT_OBS_AND_NOT_PRED, xl=True
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_b_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.NOT_OBS_AND_NOT_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_b_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.NOT_OBS_AND_NOT_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_y_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.NOT_OBS_AND_NOT_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_y_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.NOT_OBS_AND_NOT_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_but_not_predicted_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.OBS_BUT_NOT_PRED, xl=True
+            )
+            self.metrics_val["count_observed_but_not_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.OBS_BUT_NOT_PRED, xl=True
+            )
+            self.metrics_val["count_observed_but_not_predicted_b_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.OBS_BUT_NOT_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_but_not_predicted_b_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.OBS_BUT_NOT_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_but_not_predicted_y_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.OBS_BUT_NOT_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_observed_but_not_predicted_y_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.OBS_BUT_NOT_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_but_predicted_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.NOT_OBS_BUT_PRED, xl=True
+            )
+            self.metrics_val["count_not_observed_but_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.NOT_OBS_BUT_PRED, xl=True
+            )
+            self.metrics_val["count_not_observed_but_predicted_b_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.NOT_OBS_BUT_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_but_predicted_b_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.NOT_OBS_BUT_PRED, constants.B_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_but_predicted_y_a"] = FragmentsRatio.count_observation_states(
+                observation_state_a, ObservationState.NOT_OBS_BUT_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            self.metrics_val["count_not_observed_but_predicted_y_b"] = FragmentsRatio.count_observation_states(
+                observation_state_b, ObservationState.NOT_OBS_BUT_PRED, constants.Y_ION_MASK_XL, xl=True
+            )
+            # fractional count metrics
+            self.metrics_val["fraction_predicted_a"] = self.metrics_val["count_predicted_a"].values / valid_ions_a
+            self.metrics_val["fraction_predicted_b"] = self.metrics_val["count_predicted_b"].values / valid_ions_b
+            self.metrics_val["fraction_predicted_b_a"] = self.metrics_val["count_predicted_b_a"] / valid_ions_b_a
+            self.metrics_val["fraction_predicted_b_b"] = self.metrics_val["count_predicted_b_b"] / valid_ions_b_b
+            self.metrics_val["fraction_predicted_y_a"] = self.metrics_val["count_predicted_y_a"] / valid_ions_y_a
+            self.metrics_val["fraction_predicted_y_b"] = self.metrics_val["count_predicted_y_b"] / valid_ions_y_b
+            self.metrics_val["fraction_observed_a"] = self.metrics_val["count_observed_a"] / valid_ions_a
+            self.metrics_val["fraction_observed_b"] = self.metrics_val["count_observed_b"] / valid_ions_b
+            self.metrics_val["fraction_observed_b_a"] = self.metrics_val["count_observed_b_a"] / valid_ions_b_a
+            self.metrics_val["fraction_observed_b_b"] = self.metrics_val["count_observed_b_b"] / valid_ions_b_b
+            self.metrics_val["fraction_observed_y_a"] = self.metrics_val["count_observed_y_a"] / valid_ions_y_a
+            self.metrics_val["fraction_observed_y_b"] = self.metrics_val["count_observed_y_b"] / valid_ions_y_b
+            self.metrics_val["fraction_observed_and_predicted_a"] = (
+                self.metrics_val["count_observed_and_predicted_a"] / valid_ions_a
+            )
+            self.metrics_val["fraction_observed_and_predicted_b"] = (
+                self.metrics_val["count_observed_and_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_observed_and_predicted_b_a"] = (
+                self.metrics_val["count_observed_and_predicted_b_a"] / valid_ions_b_a
+            )
+            self.metrics_val["fraction_observed_and_predicted_b_b"] = (
+                self.metrics_val["count_observed_and_predicted_b_b"] / valid_ions_b_b
+            )
+            self.metrics_val["fraction_observed_and_predicted_y_a"] = (
+                self.metrics_val["count_observed_and_predicted_y_a"] / valid_ions_y_a
+            )
+            self.metrics_val["fraction_observed_and_predicted_y_b"] = (
+                self.metrics_val["count_observed_and_predicted_y_b"] / valid_ions_y_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_a"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_a"] / valid_ions_a
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b_a"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b_a"] / valid_ions_b_a
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b_b"] / valid_ions_b_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_y_a"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_y_a"] / valid_ions_y_a
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_y_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_y_b"] / valid_ions_y_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_a"] = (
+                self.metrics_val["count_observed_but_not_predicted_a"] / valid_ions_a
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b_a"] = (
+                self.metrics_val["count_observed_but_not_predicted_b_a"] / valid_ions_b_a
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_b_b"] / valid_ions_b_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_y_a"] = (
+                self.metrics_val["count_observed_but_not_predicted_y_a"] / valid_ions_y_a
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_y_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_y_b"] / valid_ions_y_b
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_a"] = (
+                self.metrics_val["count_not_observed_but_predicted_a"] / valid_ions_a
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_b"] = (
+                self.metrics_val["count_not_observed_but_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_b_a"] = (
+                self.metrics_val["count_not_observed_but_predicted_b_a"] / valid_ions_b_a
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_b_b"] = (
+                self.metrics_val["count_not_observed_but_predicted_b_b"] / valid_ions_b_b
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_y_a"] = (
+                self.metrics_val["count_not_observed_but_predicted_y_a"] / valid_ions_y_a
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_y_b"] = (
+                self.metrics_val["count_not_observed_but_predicted_y_b"] / valid_ions_y_b
+            )
+            # fractional count metrics relative to predictions
+            num_predicted_ions_a = np.maximum(1, self.metrics_val["count_predicted_a"])
+            num_predicted_ions_b = np.maximum(1, self.metrics_val["count_predicted_b"])
+            num_predicted_ions_b_a = np.maximum(1, self.metrics_val["count_predicted_b_a"])
+            num_predicted_ions_b_b = np.maximum(1, self.metrics_val["count_predicted_b_b"])
+            num_predicted_ions_y_a = np.maximum(1, self.metrics_val["count_predicted_y_a"])
+            num_predicted_ions_y_b = np.maximum(1, self.metrics_val["count_predicted_y_b"])
+            self.metrics_val["fraction_observed_and_predicted_vs_predicted_a"] = (
+                self.metrics_val["count_observed_and_predicted_a"] / num_predicted_ions_a
+            )
+            self.metrics_val["fraction_observed_and_predicted_vs_predicted_b"] = (
+                self.metrics_val["count_observed_and_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_observed_and_predicted_b_vs_predicted_b_a"] = (
+                self.metrics_val["count_observed_and_predicted_b_a"] / num_predicted_ions_b_a
+            )
+            self.metrics_val["fraction_observed_and_predicted_b_vs_predicted_b_b"] = (
+                self.metrics_val["count_observed_and_predicted_b_b"] / num_predicted_ions_b_b
+            )
+            self.metrics_val["fraction_observed_and_predicted_y_vs_predicted_y_a"] = (
+                self.metrics_val["count_observed_and_predicted_y_a"] / num_predicted_ions_y_a
+            )
+            self.metrics_val["fraction_observed_and_predicted_y_vs_predicted_y_b"] = (
+                self.metrics_val["count_observed_and_predicted_y_b"] / num_predicted_ions_y_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_vs_predicted_a"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_a"] / num_predicted_ions_a
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_vs_predicted_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b_vs_predicted_b_a"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b_a"] / num_predicted_ions_b_a
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b_vs_predicted_b_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b_b"] / num_predicted_ions_b_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_y_vs_predicted_y_a"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_y_a"] / num_predicted_ions_y_a
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_y_vs_predicted_y_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_y_b"] / num_predicted_ions_y_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_vs_predicted_a"] = (
+                self.metrics_val["count_observed_but_not_predicted_a"] / num_predicted_ions_a
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_vs_predicted_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b_vs_predicted_b_a"] = (
+                self.metrics_val["count_observed_but_not_predicted_b_a"] / num_predicted_ions_b_a
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b_vs_predicted_b_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_b_b"] / num_predicted_ions_b_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_y_vs_predicted_y_a"] = (
+                self.metrics_val["count_observed_but_not_predicted_y_a"] / num_predicted_ions_y_a
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_y_vs_predicted_y_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_y_b"] / num_predicted_ions_y_b
+            )
 
-        self.metrics_val["fraction_observed_but_not_predicted_vs_predicted"] = (
-            self.metrics_val["count_observed_but_not_predicted"] / num_predicted_ions
-        )
-        self.metrics_val["fraction_observed_but_not_predicted_b_vs_predicted_b"] = (
-            self.metrics_val["count_observed_but_not_predicted_b"] / num_predicted_ions_b
-        )
-        self.metrics_val["fraction_observed_but_not_predicted_y_vs_predicted_y"] = (
-            self.metrics_val["count_observed_but_not_predicted_y"] / num_predicted_ions_y
-        )
-
-        # not needed, as these are simply (1 - fraction_observed_and_predicted_vs_predicted)
-        self.metrics_val["fraction_not_observed_but_predicted_vs_predicted"] = (
-            self.metrics_val["count_not_observed_but_predicted"] / num_predicted_ions
-        )
-        self.metrics_val["fraction_not_observed_but_predicted_b_vs_predicted"] = (
-            self.metrics_val["count_not_observed_but_predicted_b"] / num_predicted_ions_b
-        )
-        self.metrics_val["fraction_not_observed_but_predicted_y_vs_predicted"] = (
-            self.metrics_val["count_not_observed_but_predicted_y"] / num_predicted_ions_y
-        )
+        else:
+            mask_observed_valid = FragmentsRatio.get_mask_observed_valid(self.true_intensities)
+            observed_boolean = FragmentsRatio.make_boolean(self.true_intensities, mask_observed_valid)
+            predicted_boolean = FragmentsRatio.make_boolean(self.pred_intensities, mask_observed_valid, cutoff=0.05)
+            observation_state = FragmentsRatio.get_observation_state(
+                observed_boolean, predicted_boolean, mask_observed_valid
+            )
+            valid_ions = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid))
+            valid_ions_b = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid, constants.B_ION_MASK))
+            valid_ions_y = np.maximum(1, FragmentsRatio.count_with_ion_mask(mask_observed_valid, constants.Y_ION_MASK))
+
+            # counting metrics
+            self.metrics_val["count_predicted"] = FragmentsRatio.count_with_ion_mask(predicted_boolean)
+            self.metrics_val["count_predicted_b"] = FragmentsRatio.count_with_ion_mask(
+                predicted_boolean, constants.B_ION_MASK
+            )
+            self.metrics_val["count_predicted_y"] = FragmentsRatio.count_with_ion_mask(
+                predicted_boolean, constants.Y_ION_MASK
+            )
+
+            self.metrics_val["count_observed"] = FragmentsRatio.count_with_ion_mask(observed_boolean)
+            self.metrics_val["count_observed_b"] = FragmentsRatio.count_with_ion_mask(
+                observed_boolean, constants.B_ION_MASK
+            )
+            self.metrics_val["count_observed_y"] = FragmentsRatio.count_with_ion_mask(
+                observed_boolean, constants.Y_ION_MASK
+            )
+
+            self.metrics_val["count_observed_and_predicted"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.OBS_AND_PRED
+            )
+            self.metrics_val["count_observed_and_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.OBS_AND_PRED, constants.B_ION_MASK
+            )
+            self.metrics_val["count_observed_and_predicted_y"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.OBS_AND_PRED, constants.Y_ION_MASK
+            )
+
+            self.metrics_val["count_not_observed_and_not_predicted"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.NOT_OBS_AND_NOT_PRED
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.NOT_OBS_AND_NOT_PRED, constants.B_ION_MASK
+            )
+            self.metrics_val["count_not_observed_and_not_predicted_y"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.NOT_OBS_AND_NOT_PRED, constants.Y_ION_MASK
+            )
+
+            self.metrics_val["count_observed_but_not_predicted"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.OBS_BUT_NOT_PRED
+            )
+            self.metrics_val["count_observed_but_not_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.OBS_BUT_NOT_PRED, constants.B_ION_MASK
+            )
+            self.metrics_val["count_observed_but_not_predicted_y"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.OBS_BUT_NOT_PRED, constants.Y_ION_MASK
+            )
+
+            self.metrics_val["count_not_observed_but_predicted"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.NOT_OBS_BUT_PRED
+            )
+            self.metrics_val["count_not_observed_but_predicted_b"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.NOT_OBS_BUT_PRED, constants.B_ION_MASK
+            )
+            self.metrics_val["count_not_observed_but_predicted_y"] = FragmentsRatio.count_observation_states(
+                observation_state, ObservationState.NOT_OBS_BUT_PRED, constants.Y_ION_MASK
+            )
+
+            # fractional count metrics
+            self.metrics_val["fraction_predicted"] = self.metrics_val["count_predicted"].values / valid_ions
+            self.metrics_val["fraction_predicted_b"] = self.metrics_val["count_predicted_b"] / valid_ions_b
+            self.metrics_val["fraction_predicted_y"] = self.metrics_val["count_predicted_y"] / valid_ions_y
+
+            self.metrics_val["fraction_observed"] = self.metrics_val["count_observed"] / valid_ions
+            self.metrics_val["fraction_observed_b"] = self.metrics_val["count_observed_b"] / valid_ions_b
+            self.metrics_val["fraction_observed_y"] = self.metrics_val["count_observed_y"] / valid_ions_y
+
+            self.metrics_val["fraction_observed_and_predicted"] = (
+                self.metrics_val["count_observed_and_predicted"] / valid_ions
+            )
+            self.metrics_val["fraction_observed_and_predicted_b"] = (
+                self.metrics_val["count_observed_and_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_observed_and_predicted_y"] = (
+                self.metrics_val["count_observed_and_predicted_y"] / valid_ions_y
+            )
+
+            self.metrics_val["fraction_not_observed_and_not_predicted"] = (
+                self.metrics_val["count_not_observed_and_not_predicted"] / valid_ions
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_y"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_y"] / valid_ions_y
+            )
+
+            self.metrics_val["fraction_observed_but_not_predicted"] = (
+                self.metrics_val["count_observed_but_not_predicted"] / valid_ions
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_y"] = (
+                self.metrics_val["count_observed_but_not_predicted_y"] / valid_ions_y
+            )
+
+            self.metrics_val["fraction_not_observed_but_predicted"] = (
+                self.metrics_val["count_not_observed_but_predicted"] / valid_ions
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_b"] = (
+                self.metrics_val["count_not_observed_but_predicted_b"] / valid_ions_b
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_y"] = (
+                self.metrics_val["count_not_observed_but_predicted_y"] / valid_ions_y
+            )
+
+            # fractional count metrics relative to predictions
+            num_predicted_ions = np.maximum(1, self.metrics_val["count_predicted"])
+            num_predicted_ions_b = np.maximum(1, self.metrics_val["count_predicted_b"])
+            num_predicted_ions_y = np.maximum(1, self.metrics_val["count_predicted_y"])
+
+            self.metrics_val["fraction_observed_and_predicted_vs_predicted"] = (
+                self.metrics_val["count_observed_and_predicted"] / num_predicted_ions
+            )
+            self.metrics_val["fraction_observed_and_predicted_b_vs_predicted_b"] = (
+                self.metrics_val["count_observed_and_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_observed_and_predicted_y_vs_predicted_y"] = (
+                self.metrics_val["count_observed_and_predicted_y"] / num_predicted_ions_y
+            )
+
+            self.metrics_val["fraction_not_observed_and_not_predicted_vs_predicted"] = (
+                self.metrics_val["count_not_observed_and_not_predicted"] / num_predicted_ions
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_b_vs_predicted_b"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_not_observed_and_not_predicted_y_vs_predicted_y"] = (
+                self.metrics_val["count_not_observed_and_not_predicted_y"] / num_predicted_ions_y
+            )
+
+            self.metrics_val["fraction_observed_but_not_predicted_vs_predicted"] = (
+                self.metrics_val["count_observed_but_not_predicted"] / num_predicted_ions
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_b_vs_predicted_b"] = (
+                self.metrics_val["count_observed_but_not_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_observed_but_not_predicted_y_vs_predicted_y"] = (
+                self.metrics_val["count_observed_but_not_predicted_y"] / num_predicted_ions_y
+            )
+
+            # not needed, as these are simply (1 - fraction_observed_and_predicted_vs_predicted)
+            self.metrics_val["fraction_not_observed_but_predicted_vs_predicted"] = (
+                self.metrics_val["count_not_observed_but_predicted"] / num_predicted_ions
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_b_vs_predicted"] = (
+                self.metrics_val["count_not_observed_but_predicted_b"] / num_predicted_ions_b
+            )
+            self.metrics_val["fraction_not_observed_but_predicted_y_vs_predicted"] = (
+                self.metrics_val["count_not_observed_but_predicted_y"] / num_predicted_ions_y
+            )
diff --git a/spectrum_fundamentals/metrics/metric.py b/spectrum_fundamentals/metrics/metric.py
index 5d34d4d..773c210 100644
--- a/spectrum_fundamentals/metrics/metric.py
+++ b/spectrum_fundamentals/metrics/metric.py
@@ -20,6 +20,7 @@ def __init__(
         pred_intensities: Optional[Union[np.ndarray, scipy.sparse.csr_matrix]] = None,
         true_intensities: Optional[Union[np.ndarray, scipy.sparse.csr_matrix]] = None,
         mz: Optional[Union[np.ndarray, scipy.sparse.csr_matrix]] = None,
+        xl: bool = False,
     ):
         """
         Initialize a Metric object.
@@ -27,11 +28,13 @@ def __init__(
         :param pred_intensities: predicted intensities
         :param true_intensities: observed intensities
         :param mz: observed mz values
+        :param xl: whether the metric is used for crosslinked or linear peptides
         """
         self.pred_intensities = pred_intensities
         self.true_intensities = true_intensities
         self.mz = mz
         self.metrics_val = pd.DataFrame()
+        self.xl = xl
 
     @abstractmethod
     def calc(self, all_features: bool):
diff --git a/spectrum_fundamentals/metrics/percolator.py b/spectrum_fundamentals/metrics/percolator.py
index 3a71fba..1d4455d 100644
--- a/spectrum_fundamentals/metrics/percolator.py
+++ b/spectrum_fundamentals/metrics/percolator.py
@@ -64,6 +64,8 @@ def __init__(
         self.all_features_flag = all_features_flag
         self.regression_method = regression_method
         self.fdr_cutoff = fdr_cutoff
+        self.xl = "CROSSLINKER_TYPE" in self.metadata.columns
+
         super().__init__(pred_intensities, true_intensities, mz)
 
     @staticmethod
@@ -245,18 +247,27 @@ def get_target_decoy_label(reverse: bool):
 
     def add_common_features(self):
         """Add features used by both Andromeda and Prosit feature scoring sets."""
-        self.metrics_val["missedCleavages"] = self.metadata["SEQUENCE"].apply(Percolator.count_missed_cleavages)
-        self.metrics_val["KR"] = self.metadata["SEQUENCE"].apply(Percolator.count_arginines_and_lysines)
-        self.metrics_val["sequence_length"] = self.metadata["SEQUENCE"].apply(lambda x: len(x))
+        if self.xl:
+            self.metrics_val["missedCleavages_A"] = self.metadata["SEQUENCE_A"].apply(Percolator.count_missed_cleavages)
+            self.metrics_val["missedCleavages_B"] = self.metadata["SEQUENCE_B"].apply(Percolator.count_missed_cleavages)
+            self.metrics_val["KR_A"] = self.metadata["SEQUENCE_A"].apply(Percolator.count_arginines_and_lysines)
+            self.metrics_val["KR_B"] = self.metadata["SEQUENCE_B"].apply(Percolator.count_arginines_and_lysines)
+            self.metrics_val["sequence_length_a"] = self.metadata["SEQUENCE_A"].apply(lambda x: len(x))
+            self.metrics_val["sequence_length_b"] = self.metadata["SEQUENCE_B"].apply(lambda x: len(x))
+            for column_name in self.metadata.columns:
+                self.metrics_val[column_name] = self.metadata[column_name]
+        else:
+            self.metrics_val["missedCleavages"] = self.metadata["SEQUENCE"].apply(Percolator.count_missed_cleavages)
+            self.metrics_val["KR"] = self.metadata["SEQUENCE"].apply(Percolator.count_arginines_and_lysines)
+            self.metrics_val["sequence_length"] = self.metadata["SEQUENCE"].apply(lambda x: len(x))
+            self.metrics_val["Mass"] = self.metadata["CALCULATED_MASS"]  # this is the calculated mass used as a feature
 
-        self.metrics_val["Mass"] = self.metadata["CALCULATED_MASS"]  # this is the calculated mass used as a feature
         self.metrics_val["Charge1"] = (self.metadata["PRECURSOR_CHARGE"] == 1).astype(int)
         self.metrics_val["Charge2"] = (self.metadata["PRECURSOR_CHARGE"] == 2).astype(int)
         self.metrics_val["Charge3"] = (self.metadata["PRECURSOR_CHARGE"] == 3).astype(int)
         self.metrics_val["Charge4"] = (self.metadata["PRECURSOR_CHARGE"] == 4).astype(int)
         self.metrics_val["Charge5"] = (self.metadata["PRECURSOR_CHARGE"] == 5).astype(int)
         self.metrics_val["Charge6"] = (self.metadata["PRECURSOR_CHARGE"] == 6).astype(int)
-
         self.metrics_val["UnknownFragmentationMethod"] = (~self.metadata["FRAGMENTATION"].isin(["HCD", "CID"])).astype(
             int
         )
@@ -265,18 +276,28 @@ def add_common_features(self):
 
     def add_percolator_metadata_columns(self):
         """Add metadata columns needed by percolator, e.g. to identify a PSM."""
-        spec_id_cols = ["RAW_FILE", "SCAN_NUMBER", "MODIFIED_SEQUENCE", "PRECURSOR_CHARGE"]
+        if self.xl:
+            spec_id_cols = ["RAW_FILE", "SCAN_NUMBER", "MODIFIED_SEQUENCE_A", "MODIFIED_SEQUENCE_B", "PRECURSOR_CHARGE"]
+            self.metrics_val["Peptide"] = (
+                self.metadata["MODIFIED_SEQUENCE_A"] + "_" + self.metadata["MODIFIED_SEQUENCE_B"]
+            ).apply(lambda x: "_." + x + "._")
+            self.metrics_val["Proteins"] = (
+                self.metadata["MODIFIED_SEQUENCE_A"] + "_" + self.metadata["MODIFIED_SEQUENCE_B"]
+            )
+            self.metrics_val["Label"] = self.target_decoy_labels
+        else:
+            spec_id_cols = ["RAW_FILE", "SCAN_NUMBER", "MODIFIED_SEQUENCE", "PRECURSOR_CHARGE"]
+            self.metrics_val["Peptide"] = self.metadata["MODIFIED_SEQUENCE"].apply(lambda x: "_." + x + "._")
+            self.metrics_val["Proteins"] = self.metadata[
+                "MODIFIED_SEQUENCE"
+            ]  # we don't need the protein ID to get PSM / peptide results, fill with peptide sequence
+
         if "SCAN_EVENT_NUMBER" in self.metadata.columns:
             spec_id_cols.append("SCAN_EVENT_NUMBER")
         self.metrics_val["SpecId"] = self.metadata[spec_id_cols].apply(Percolator.get_specid, axis=1)
         self.metrics_val["Label"] = self.target_decoy_labels
         self.metrics_val["ScanNr"] = self.metadata["SCAN_NUMBER"]
         self.metrics_val["filename"] = self.metadata["RAW_FILE"]
-        self.metrics_val["Peptide"] = self.metadata["MODIFIED_SEQUENCE"].apply(lambda x: "_." + x + "._")
-
-        self.metrics_val["Proteins"] = self.metadata[
-            "MODIFIED_SEQUENCE"
-        ]  # we don't need the protein ID to get PSM / peptide results, fill with peptide sequence
 
     def apply_lda_and_get_indices_below_fdr(
         self, initial_scoring_feature: str = "spectral_angle", fdr_cutoff: float = 0.01
@@ -322,7 +343,6 @@ def get_indices_below_fdr(self, feature_name: str, fdr_cutoff: float = 0.01) ->
         logger.debug(scores_df.head(100))
 
         scores_df["fdr"] = Percolator.calculate_fdrs(scores_df["Label"])
-
         # filter for targets only
         scores_df = scores_df[scores_df["Label"] == TargetDecoyLabel.TARGET]
 
@@ -380,58 +400,63 @@ def _reorder_columns_for_percolator(self):
     def calc(self):
         """Adds percolator metadata and feature columns to metrics_val based on PSM metadata."""
         self.add_common_features()
-
         self.target_decoy_labels = self.metadata["REVERSE"].apply(Percolator.get_target_decoy_label).to_numpy()
-
         np.random.seed(1)
         # add Prosit or Andromeda features
         if self.input_type == "rescore":
             fragments_ratio = fr.FragmentsRatio(self.pred_intensities, self.true_intensities)
-            fragments_ratio.calc()
-
+            fragments_ratio.calc(xl=self.xl)
             similarity = sim.SimilarityMetrics(self.pred_intensities, self.true_intensities, self.mz)
-            similarity.calc(self.all_features_flag)
+            similarity.calc(self.all_features_flag, xl=self.xl)
 
             self.metrics_val = pd.concat(
                 [self.metrics_val, fragments_ratio.metrics_val, similarity.metrics_val], axis=1
             )
-
-            lda_failed = False
-            idxs_below_lda_fdr = self.apply_lda_and_get_indices_below_fdr(fdr_cutoff=self.fdr_cutoff)
-            current_fdr = self.fdr_cutoff
-            while len(idxs_below_lda_fdr) == 0:
-                current_fdr += 0.01
-                idxs_below_lda_fdr = self.apply_lda_and_get_indices_below_fdr(fdr_cutoff=current_fdr)
-                if current_fdr >= 0.1:
-                    lda_failed = True
-                    break
-
-            if lda_failed:
-                sampled_idxs = Percolator.sample_balanced_over_bins(self.metadata[["RETENTION_TIME", "PREDICTED_IRT"]])
+            if self.xl:
+                self.metrics_val["collision_energy_aligned"] = self.metadata["COLLISION_ENERGY"] / 100.0
             else:
-                sampled_idxs = Percolator.sample_balanced_over_bins(
-                    self.metadata[["RETENTION_TIME", "PREDICTED_IRT"]].iloc[idxs_below_lda_fdr, :]
-                )
-
-            file_sample = self.metadata.iloc[sampled_idxs].sort_values("PREDICTED_IRT")
-            aligned_predicted_rts = Percolator.get_aligned_predicted_retention_times(
-                file_sample["RETENTION_TIME"],
-                file_sample["PREDICTED_IRT"],
-                self.metadata["PREDICTED_IRT"],
-                self.regression_method,
-            )
-
-            self.metrics_val["RT"] = self.metadata["RETENTION_TIME"]
-            self.metrics_val["pred_RT"] = self.metadata["PREDICTED_IRT"]
-            self.metrics_val["iRT"] = aligned_predicted_rts
-            self.metrics_val["collision_energy_aligned"] = self.metadata["COLLISION_ENERGY"] / 100.0
-            self.metrics_val["abs_rt_diff"] = np.abs(self.metadata["RETENTION_TIME"] - aligned_predicted_rts)
+                lda_failed = False
+                idxs_below_lda_fdr = self.apply_lda_and_get_indices_below_fdr(fdr_cutoff=self.fdr_cutoff)
+                current_fdr = self.fdr_cutoff
+                while len(idxs_below_lda_fdr) == 0:
+                    current_fdr += 0.01
+                    idxs_below_lda_fdr = self.apply_lda_and_get_indices_below_fdr(fdr_cutoff=current_fdr)
+                    if current_fdr >= 0.1:
+                        lda_failed = True
+                        break
+                else:
+                    if lda_failed:
+                        sampled_idxs = Percolator.sample_balanced_over_bins(
+                            self.metadata[["RETENTION_TIME", "PREDICTED_IRT"]]
+                        )
+                    else:
+                        sampled_idxs = Percolator.sample_balanced_over_bins(
+                            self.metadata[["RETENTION_TIME", "PREDICTED_IRT"]].iloc[idxs_below_lda_fdr, :]
+                        )
+
+                    file_sample = self.metadata.iloc[sampled_idxs].sort_values("PREDICTED_IRT")
+                    aligned_predicted_rts = Percolator.get_aligned_predicted_retention_times(
+                        file_sample["RETENTION_TIME"],
+                        file_sample["PREDICTED_IRT"],
+                        self.metadata["PREDICTED_IRT"],
+                        self.regression_method,
+                    )
+
+                    self.metrics_val["RT"] = self.metadata["RETENTION_TIME"]
+                    self.metrics_val["pred_RT"] = self.metadata["PREDICTED_IRT"]
+                    self.metrics_val["iRT"] = aligned_predicted_rts
+                    self.metrics_val["collision_energy_aligned"] = self.metadata["COLLISION_ENERGY"] / 100.0
+                    self.metrics_val["abs_rt_diff"] = np.abs(self.metadata["RETENTION_TIME"] - aligned_predicted_rts)
+
+                    median_abs_error_lda_targets = np.median(self.metrics_val["abs_rt_diff"].iloc[idxs_below_lda_fdr])
+                    logger.info(
+                        "Median absolute error predicted vs observed retention time on targets < 1% FDR: "
+                        f"{median_abs_error_lda_targets}"
+                    )
+                    logger.debug(
+                        self.metrics_val[["RT", "pred_RT", "abs_rt_diff", "lda_scores"]].iloc[idxs_below_lda_fdr, :]
+                    )
 
-            median_abs_error_lda_targets = np.median(self.metrics_val["abs_rt_diff"].iloc[idxs_below_lda_fdr])
-            logger.info(
-                f"Median absolute error predicted vs observed retention time on targets < 1% FDR: {median_abs_error_lda_targets}"
-            )
-            logger.debug(self.metrics_val[["RT", "pred_RT", "abs_rt_diff", "lda_scores"]].iloc[idxs_below_lda_fdr, :])
         else:
             self.metrics_val["andromeda"] = self.metadata["SCORE"]
 
diff --git a/spectrum_fundamentals/metrics/similarity.py b/spectrum_fundamentals/metrics/similarity.py
index f99f617..930df61 100644
--- a/spectrum_fundamentals/metrics/similarity.py
+++ b/spectrum_fundamentals/metrics/similarity.py
@@ -17,7 +17,6 @@ def get_metric_func(metric: str):
     Return a callable function for a given metric shortcut.
 
     :param metric: a shortcut for the desired metric.
-
     :raises ValueError: if the provided metric is not known
 
     :return: callable metric function
@@ -47,6 +46,7 @@ def spectral_angle(
         observed_intensities: Union[scipy.sparse.csr_matrix, np.ndarray],
         predicted_intensities: Union[scipy.sparse.csr_matrix, np.ndarray],
         charge: int = 0,
+        xl: bool = False,
     ) -> np.ndarray:
         """
         Calculate spectral angle.
@@ -56,20 +56,15 @@ def spectral_angle(
                                      position >= peptide length), array of length 174
         :param predicted_intensities: predicted intensities, see observed_intensities for details, array of length 174
         :param charge: to filter by the peak charges, 0 means everything
+        :param xl: whether operating on cleavable crosslinked or linear peptides
+        :raises ValueError: if charge is smaller than 1 or larger than 5
         :return: SA values
         """
         if charge != 0:
-            if charge == 1:
-                boolean_array = constants.SINGLE_CHARGED_MASK
-            elif charge == 2:
-                boolean_array = constants.DOUBLE_CHARGED_MASK
-            elif charge == 3:
-                boolean_array = constants.TRIPLE_CHARGED_MASK
-            elif charge == 4:
-                boolean_array = constants.B_ION_MASK
-            else:
-                boolean_array = constants.Y_ION_MASK
-
+            if not 1 <= charge <= 5:
+                raise ValueError("Charge must be between 1 to 5.")
+            masks = constants.MASK_DICT_XL if xl else constants.MASK_DICT
+            boolean_array = masks[charge]
             boolean_array = scipy.sparse.csr_matrix(boolean_array)
             observed_intensities = scipy.sparse.csr_matrix(observed_intensities)
             predicted_intensities = scipy.sparse.csr_matrix(predicted_intensities)
@@ -204,6 +199,7 @@ def correlation(
         predicted_intensities: scipy.sparse.csr_matrix,
         charge: int = 0,
         method: str = "pearson",
+        xl: bool = False,
     ) -> List[float]:
         """
         Calculate correlation between observed and predicted.
@@ -214,23 +210,19 @@ def correlation(
         :param predicted_intensities: predicted intensities, see observed_intensities for details, array of length 174
         :param charge: to filter by the peak charges, 0 means everything
         :param method: either pearson or spearman
+        :param xl: wheter or not to use xl mode
+        :raises ValueError: if charge is smaller than 1 or larger than 5
+
         :return: calculated correlations
         """
         observed_intensities_array = observed_intensities.toarray()
         predicted_intensities_array = predicted_intensities.toarray()
 
         if charge != 0:
-            if charge == 1:
-                boolean_array = constants.SINGLE_CHARGED_MASK
-            elif charge == 2:
-                boolean_array = constants.DOUBLE_CHARGED_MASK
-            elif charge == 3:
-                boolean_array = constants.TRIPLE_CHARGED_MASK
-            elif charge == 4:
-                boolean_array = constants.B_ION_MASK
-            else:
-                boolean_array = constants.Y_ION_MASK
-
+            if not 1 <= charge <= 5:
+                raise ValueError("Charge must be between 1 to 5.")
+            masks = constants.MASK_DICT_XL if xl else constants.MASK_DICT
+            boolean_array = masks[charge]
             boolean_array = scipy.sparse.csr_matrix(boolean_array)
             observed_intensities_array = observed_intensities.multiply(boolean_array).toarray()
             predicted_intensities_array = predicted_intensities.multiply(boolean_array).toarray()
@@ -403,75 +395,147 @@ def modified_cosine(
 
         return cos_values
 
-    def calc(self, all_features: bool):
+    def calc(self, all_features: bool, xl: bool = False):
         """
         Adds columns with spectral angle feature to metrics_val dataframe.
 
-        :param all_features: if True, calculcate all metrics
+        :param all_features: if True, calculate all metrics
+        :param xl: whether calculating for crosslinked or linear peptides
         """
-        self.metrics_val["spectral_angle"] = SimilarityMetrics.spectral_angle(
-            self.true_intensities, self.pred_intensities, 0
+        if xl:
+            if self.true_intensities is not None and self.pred_intensities is not None:
+                true_intensities_a = (
+                    self.true_intensities[:, :348] if self.true_intensities.shape[1] >= 348 else self.true_intensities
+                )
+                true_intensities_b = self.true_intensities[:, 348:] if self.true_intensities.shape[1] >= 348 else None
+                pred_intensities_a = (
+                    self.pred_intensities[:, :348] if self.pred_intensities.shape[1] >= 348 else self.pred_intensities
+                )
+                pred_intensities_b = self.pred_intensities[:, 348:] if self.pred_intensities.shape[1] >= 348 else None
+
+                if true_intensities_a is not None and pred_intensities_a is not None:
+                    self.metrics_val["spectral_angle_a"] = SimilarityMetrics.spectral_angle(
+                        true_intensities_a, pred_intensities_a, 0
+                    )
+                    self.metrics_val["pearson_corr_a"] = SimilarityMetrics.correlation(
+                        true_intensities_a, pred_intensities_a, 0
+                    )
+                    if all_features:
+                        self._calc_additional_metrics(true_intensities_a, pred_intensities_a, key_suffix="_a")
+
+                if true_intensities_b is not None and pred_intensities_b is not None:
+                    self.metrics_val["spectral_angle_b"] = SimilarityMetrics.spectral_angle(
+                        true_intensities_b, pred_intensities_b, 0
+                    )
+                    self.metrics_val["pearson_corr_b"] = SimilarityMetrics.correlation(
+                        true_intensities_b, pred_intensities_b, 0
+                    )
+                    if all_features:
+                        self._calc_additional_metrics(true_intensities_b, pred_intensities_b, key_suffix="_b")
+
+                if true_intensities_a is not None and true_intensities_b is not None:
+                    self.metrics_val["spectral_angle"] = (
+                        self.metrics_val["spectral_angle_a"] + self.metrics_val["spectral_angle_b"]
+                    ) / 2
+
+        else:
+            if self.true_intensities is not None and self.pred_intensities is not None:
+                self.metrics_val["spectral_angle"] = SimilarityMetrics.spectral_angle(
+                    self.true_intensities, self.pred_intensities, 0
+                )
+                self.metrics_val["pearson_corr"] = SimilarityMetrics.correlation(
+                    self.true_intensities, self.pred_intensities, 0, "pearson"
+                )
+                if all_features:
+                    self._calc_additional_metrics(self.true_intensities, self.pred_intensities)
+
+    def _calc_additional_metrics(
+        self,
+        true_intensities: Union[np.ndarray, scipy.sparse.spmatrix],
+        pred_intensities: Union[np.ndarray, scipy.sparse.spmatrix],
+        key_suffix: str = "",
+    ):
+        self.metrics_val[f"spectral_entropy_similarity{key_suffix}"] = SimilarityMetrics.spectral_entropy_similarity(
+            true_intensities, pred_intensities
         )
-        self.metrics_val["pearson_corr"] = SimilarityMetrics.correlation(
-            self.true_intensities, self.pred_intensities, 0, "pearson"
+        self.metrics_val[f"cos{key_suffix}"] = SimilarityMetrics.cos(true_intensities, pred_intensities)
+        self.metrics_val[f"mean_abs_diff{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "mean"
+        )
+        self.metrics_val[f"std_abs_diff{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "std"
+        )
+        self.metrics_val[f"abs_diff_Q3{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "q3"
+        )
+        self.metrics_val[f"abs_diff_Q2{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "q2"
+        )
+        self.metrics_val[f"abs_diff_Q1{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "q1"
+        )
+        self.metrics_val[f"min_abs_diff{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "min"
+        )
+        self.metrics_val[f"max_abs_diff{key_suffix}"] = SimilarityMetrics.abs_diff(
+            true_intensities, pred_intensities, "max"
+        )
+        self.metrics_val[f"mse{key_suffix}"] = SimilarityMetrics.abs_diff(true_intensities, pred_intensities, "mse")
+        self.metrics_val[f"modified_cosine{key_suffix}"] = SimilarityMetrics.modified_cosine(
+            true_intensities, pred_intensities, self.mz, self.mz
         )
-        if all_features:
-            self.metrics_val["modified_cosine"] = SimilarityMetrics.modified_cosine(
-                self.true_intensities, self.pred_intensities, self.mz, self.mz
-            )
-            self.metrics_val["spectral_entropy_similarity"] = SimilarityMetrics.spectral_entropy_similarity(
-                self.true_intensities, self.pred_intensities
-            )
 
-            col_names_spectral_angle = [
-                f"spectral_angle_{amount}_charge" for amount in ["single", "double", "triple"]
-            ] + ["spectral_angle_b_ions", "spectral_angle_y_ions"]
-            col_names_pearson_corr = [f"pearson_corr_{amount}_charge" for amount in ["single", "double", "triple"]] + [
-                "pearson_corr_b_ions",
-                "pearson_corr_y_ions",
-            ]
-            col_names_spearman_corr = [
-                f"spearman_corr_{amount}_charge" for amount in ["single", "double", "triple"]
-            ] + ["spearman_corr_b_ions", "spearman_corr_y_ions"]
+        col_names_spectral_angle = [
+            f"spectral_angle_{amount}_charge{key_suffix}" for amount in ["single", "double", "triple"]
+        ] + [f"spectral_angle_b_ions{key_suffix}", f"spectral_angle_y_ions{key_suffix}"]
+        col_names_pearson_corr = [
+            f"pearson_corr_{amount}_charge{key_suffix}" for amount in ["single", "double", "triple"]
+        ] + [
+            f"pearson_corr_b_ions{key_suffix}",
+            f"pearson_corr_y_ions{key_suffix}",
+        ]
+        col_names_spearman_corr = [
+            f"spearman_corr_{amount}_charge{key_suffix}" for amount in ["single", "double", "triple"]
+        ] + [f"spearman_corr_b_ions{key_suffix}", f"spearman_corr_y_ions{key_suffix}"]
+
+        if key_suffix != "":
+            # dirty fix, if the key_suffix is not "", that means we have XL mode.
+            # TODO: fix self.mz for XL mode
+
+            self.metrics_val[f"spearman_corr{key_suffix}"] = SimilarityMetrics.correlation(
+                true_intensities, pred_intensities, 0, "spearman", xl=True
+            )
 
             for i, col_name_spectral_angle in enumerate(col_names_spectral_angle):
                 self.metrics_val[col_name_spectral_angle] = SimilarityMetrics.spectral_angle(
-                    self.true_intensities, self.pred_intensities, i + 1
+                    true_intensities, pred_intensities, i + 1, xl=True
                 )
 
             for i, col_name_pearson_corr in enumerate(col_names_pearson_corr):
                 self.metrics_val[col_name_pearson_corr] = SimilarityMetrics.correlation(
-                    self.true_intensities, self.pred_intensities, i + 1, "pearson"
+                    true_intensities, pred_intensities, i + 1, "pearson", xl=True
                 )
 
-            self.metrics_val["cos"] = SimilarityMetrics.cos(self.true_intensities, self.pred_intensities)
-            self.metrics_val["mean_abs_diff"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "mean"
-            )
-            self.metrics_val["std_abs_diff"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "std"
-            )
-            self.metrics_val["abs_diff_Q3"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "q3"
-            )
-            self.metrics_val["abs_diff_Q2"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "q2"
-            )
-            self.metrics_val["abs_diff_Q1"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "q1"
-            )
-            self.metrics_val["min_abs_diff"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "min"
-            )
-            self.metrics_val["max_abs_diff"] = SimilarityMetrics.abs_diff(
-                self.true_intensities, self.pred_intensities, "max"
-            )
-            self.metrics_val["mse"] = SimilarityMetrics.abs_diff(self.true_intensities, self.pred_intensities, "mse")
-            self.metrics_val["spearman_corr"] = SimilarityMetrics.correlation(
-                self.true_intensities, self.pred_intensities, 0, "spearman"
+            for i, col_name_spearman_corr in enumerate(col_names_spearman_corr):
+                self.metrics_val[col_name_spearman_corr] = SimilarityMetrics.correlation(
+                    true_intensities, pred_intensities, i + 1, "spearman", xl=True
+                )
+        else:
+            self.metrics_val[f"spearman_corr{key_suffix}"] = SimilarityMetrics.correlation(
+                true_intensities, pred_intensities, 0, "spearman"
             )
 
+            for i, col_name_spectral_angle in enumerate(col_names_spectral_angle):
+                self.metrics_val[col_name_spectral_angle] = SimilarityMetrics.spectral_angle(
+                    true_intensities, pred_intensities, i + 1
+                )
+
+            for i, col_name_pearson_corr in enumerate(col_names_pearson_corr):
+                self.metrics_val[col_name_pearson_corr] = SimilarityMetrics.correlation(
+                    true_intensities, pred_intensities, i + 1, "pearson"
+                )
+
             for i, col_name_spearman_corr in enumerate(col_names_spearman_corr):
                 self.metrics_val[col_name_spearman_corr] = SimilarityMetrics.correlation(
-                    self.true_intensities, self.pred_intensities, i + 1, "spearman"
+                    true_intensities, pred_intensities, i + 1, "spearman"
                 )
diff --git a/spectrum_fundamentals/mod_string.py b/spectrum_fundamentals/mod_string.py
index d9bba91..3b1edbd 100644
--- a/spectrum_fundamentals/mod_string.py
+++ b/spectrum_fundamentals/mod_string.py
@@ -1,6 +1,6 @@
 import difflib
 import re
-from itertools import repeat
+from itertools import combinations, repeat
 from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -338,6 +338,33 @@ def split_modstring(sequence: str, r_pattern):
     return map(split_modstring, sequences, repeat(regex_pattern))
 
 
+def add_permutations(modified_sequence: str, unimod_id: int, residues: List[str]):
+    """
+    Generate different peptide sequences with moving the modification to all possible residues.
+
+    :param modified_sequence: Peptide sequence
+    :param unimod_id: modification unimod id to be used for generating different permutations.
+    :param residues: possible amino acids where this mod can exist
+    :return: list of possible sequence permutations
+    """
+    sequence = modified_sequence.replace("[UNIMOD:" + str(unimod_id) + "]", "")
+    modifications = len(re.findall("UNIMOD:" + str(unimod_id), modified_sequence))
+    if modifications == 0:
+        return modified_sequence
+    possible_positions = [i for i, ltr in enumerate(sequence) if ltr in residues]
+    possible_positions.sort(reverse=True)
+    all_combinations = [list(each_permutation) for each_permutation in combinations(possible_positions, modifications)]
+    modified_sequences_comb = []
+    for comb in all_combinations:
+        modified_sequence = sequence
+        for index in comb:
+            modified_sequence = (
+                modified_sequence[: index + 1] + "[unimod:" + str(unimod_id) + "]" + modified_sequence[index + 1 :]
+            )
+        modified_sequences_comb.append(modified_sequence)
+    return modified_sequences_comb
+
+
 def proteomicsdb_to_internal(sequence: str, mods_variable: str = "", mods_fixed: str = "") -> str:
     """
     Function to create a sequence with UNIMOD modifications from given sequence and it's varaible and fixed modifications.
diff --git a/tests/unit_tests/data/annotation_xl_cl_input.json b/tests/unit_tests/data/annotation_xl_cl_input.json
new file mode 100644
index 0000000..ac1001d
--- /dev/null
+++ b/tests/unit_tests/data/annotation_xl_cl_input.json
@@ -0,0 +1,242108 @@
+[
+    {
+        "run_name": "20210319_QExHFX2_RSLC3_DSSO_mainlib_rep3",
+        "scan_number": 2695,
+        "match_score": 0.5900369,
+        "link_pos_p1": 9,
+        "link_pos_p2": 1,
+        "decoy_p1": true,
+        "decoy_p2": false,
+        "base_sequence_p1": "IFLEILMEKPR",
+        "base_sequence_p2": "KMYRGALK",
+        "linked_aa_p1": "K",
+        "linked_aa_p2": "K",
+        "aa_len_p1": 11,
+        "aa_len_p2": 8,
+        "precursor_charge": 3,
+        "precursor_mass": 2511.3374866978,
+        "linear": false,
+        "crosslinker_name": "DSSO",
+        "mods_p1": null,
+        "mods_p2": null,
+        "mod_pos_p1": null,
+        "mod_pos_p2": null,
+        "decoy": true,
+        "RAW_FILE": "20210319_QExHFX2_RSLC3_DSSO_mainlib_rep3",
+        "MASS": 2511.3374866978,
+        "PRECURSOR_CHARGE": 3,
+        "CROSSLINKER_TYPE": "DSSO",
+        "SCORE": 0.5900369,
+        "REVERSE": true,
+        "SCAN_NUMBER": 2695,
+        "SEQUENCE_A": "IFLEILMEKPR",
+        "SEQUENCE_B": "KMYRGALK",
+        "Modifications_A": null,
+        "Modifications_B": null,
+        "CROSSLINKER_POSITION_A": 9,
+        "CROSSLINKER_POSITION_B": 1,
+        "ModificationPositions1": null,
+        "ModificationPositions2": null,
+        "PEPTIDE_LENGTH_A": 11,
+        "PEPTIDE_LENGTH_B": 8,
+        "MODIFIED_SEQUENCE_A": "IFLEILMEK[UNIMOD:1896]PR",
+        "MODIFIED_SEQUENCE_B": "K[UNIMOD:1896]MYRGALK",
+        "INTENSITIES": [
+            1649.9234619141, 1211.6223144531, 1547.6033935547, 3830.8620605469,
+            3026.4055175781, 3167.5446777344, 1279.8835449219, 41052.54296875,
+            4179.8759765625, 1495.2303466797, 4835.8666992188, 2091.6665039062,
+            4020.9536132812, 1774.7366943359, 9447.5625, 19760.421875,
+            2538.0012207031, 4239.53515625, 2149.666015625, 2484.7629394531,
+            5776.5122070312, 3175.5622558594, 4610.326171875, 1402.7578125,
+            1622.2103271484, 2650.4338378906, 1549.3598632812, 2136.9184570312,
+            1973.8424072266, 1540.8280029297, 1688.7863769531, 2189.8266601562,
+            1808.6822509766, 1674.8231201172, 1595.0266113281, 2098.1088867188,
+            2874.7939453125, 4109.1396484375, 2074.3752441406, 2546.4196777344,
+            1536.3009033203, 1943.6451416016, 2198.9794921875, 1556.3928222656,
+            7829.46875, 1575.7900390625, 3608.9992675781, 344242.65625,
+            1535.6507568359, 1471.6690673828, 1564.0516357422
+        ],
+        "MZ": [
+            175.1189422607, 181.5583190918, 209.1023406982, 209.6069335938,
+            209.6191558838, 271.0348205566, 271.1867980957, 289.044708252,
+            290.0479736328, 291.1580810547, 303.7481384277, 303.7676696777,
+            352.1727905273, 369.1983337402, 571.0651245117, 572.0682373047,
+            572.8663330078, 601.0405883789, 607.3194580078, 629.9936523438,
+            638.3735351562, 674.9624633789, 680.9772338867, 684.3570556641,
+            686.9487915039, 695.9512329102, 696.451965332, 696.9411621094,
+            702.9253540039, 720.9162597656, 730.3587646484, 731.9244995117,
+            732.4262695312, 754.0561523438, 761.2438354492, 770.8967895508,
+            771.3996582031, 771.8993530273, 772.39453125, 778.419921875,
+            781.3943481445, 802.7690429688, 820.4482421875, 820.5348510742,
+            821.4268798828, 831.7366943359, 838.2015380859, 838.4536132812,
+            978.9583129883, 1029.1237792969, 1064.3267822266
+        ],
+        "MZ_RANGE": "172.33332824707-2585.0",
+        "RETENTION_TIME": 12.043786,
+        "MASS_ANALYZER": "FTMS",
+        "FRAGMENTATION": "HCD",
+        "COLLISION_ENERGY": 27.0,
+        "INSTRUMENT_TYPES": "Q Exactive HF-X"
+    },
+    {
+        "run_name": "20210319_QExHFX2_RSLC3_DSSO_mainlib_rep3",
+        "scan_number": 5125,
+        "match_score": 19.025068,
+        "link_pos_p1": 5,
+        "link_pos_p2": 5,
+        "decoy_p1": false,
+        "decoy_p2": false,
+        "base_sequence_p1": "TSGEKHLR",
+        "base_sequence_p2": "TSGEKHLR",
+        "linked_aa_p1": "K",
+        "linked_aa_p2": "K",
+        "aa_len_p1": 8,
+        "aa_len_p2": 8,
+        "precursor_charge": 5,
+        "precursor_mass": 2010.9913105656,
+        "linear": false,
+        "crosslinker_name": "DSSO",
+        "mods_p1": null,
+        "mods_p2": null,
+        "mod_pos_p1": null,
+        "mod_pos_p2": null,
+        "decoy": false,
+        "RAW_FILE": "20210319_QExHFX2_RSLC3_DSSO_mainlib_rep3",
+        "MASS": 2010.9913105656,
+        "PRECURSOR_CHARGE": 5,
+        "CROSSLINKER_TYPE": "DSSO",
+        "SCORE": 19.025068,
+        "REVERSE": false,
+        "SCAN_NUMBER": 5125,
+        "SEQUENCE_A": "TSGEKHLR",
+        "SEQUENCE_B": "TSGEKHLR",
+        "Modifications_A": null,
+        "Modifications_B": null,
+        "CROSSLINKER_POSITION_A": 5,
+        "CROSSLINKER_POSITION_B": 5,
+        "ModificationPositions1": null,
+        "ModificationPositions2": null,
+        "PEPTIDE_LENGTH_A": 8,
+        "PEPTIDE_LENGTH_B": 8,
+        "MODIFIED_SEQUENCE_A": "TSGEK[UNIMOD:1896]HLR",
+        "MODIFIED_SEQUENCE_B": "TSGEK[UNIMOD:1896]HLR",
+        "INTENSITIES": [
+            2596.6228027344, 3521.8896484375, 5616.3862304688, 22091.509765625,
+            11744.9638671875, 1374.6466064453, 1521.4992675781, 4799.8227539062,
+            7404.740234375, 2325.1911621094, 1227.2286376953, 6936.6923828125,
+            1731.0583496094, 8557.349609375, 1455.3513183594, 72637.5234375,
+            4133.166015625, 1727.3315429688, 2005.7320556641, 1406.7026367188,
+            10333.1865234375, 1350.3409423828, 3325.16796875, 2509.6794433594,
+            97752.140625, 5597.5302734375, 6482.0849609375, 2040.5107421875,
+            9587.837890625, 1287.7199707031, 9762.4404296875, 25084.484375,
+            1517.4501953125, 1761.2921142578, 45830.375, 2295.71484375,
+            7075.7216796875, 2625.8605957031, 3100.7861328125, 3865.2888183594,
+            1346.8735351562, 1973.8508300781, 1532.6760253906, 1784.5773925781,
+            9430.5654296875, 1438.1938476562, 3136.8176269531, 3230.3874511719,
+            1376.0367431641, 2123.5854492188, 6462.169921875, 1363.2600097656,
+            12699.0849609375, 2850.0925292969, 1288.9067382812, 2145.7236328125,
+            2702.5104980469, 3547.2487792969, 2083.6926269531, 8589.205078125,
+            2949.8708496094, 6180.6357421875, 2387.1125488281, 3915.8259277344,
+            3735.9670410156, 1725.126953125, 1901.7653808594, 4780.6728515625,
+            11777.1064453125, 2315.0349121094, 1472.8950195312, 3221.3452148438,
+            81785.0, 8530.119140625, 2528.935546875, 1738.8824462891,
+            1362.2612304688, 1815.0100097656, 7746.03125, 4717.58203125,
+            3047.625, 1790.1856689453, 2964.3115234375, 30171.505859375,
+            6053.3481445312, 4752.1044921875, 24424.6015625, 26670.4765625,
+            18099.74609375, 2492.8576660156, 1847.5821533203, 1347.4075927734,
+            1597.0367431641, 3336.4907226562, 6457.7221679688, 5594.2192382812,
+            2898.15625, 3255.0930175781, 2548.208984375, 14730.626953125,
+            3654.7473144531, 1396.0999755859, 32504.35546875, 10726.7783203125,
+            3696.4768066406, 1750.2744140625, 2625.5656738281, 1416.5864257812,
+            1309.5433349609, 5977.8247070312, 96087.9453125, 2570.2377929688,
+            2027.0155029297, 33058.8671875, 6340.48828125, 1704.9842529297,
+            9583.177734375, 2065.9509277344, 4566.3549804688, 1768.1774902344,
+            1439.0317382812, 4542.041015625, 6632.0400390625, 2645.8674316406,
+            1548.8231201172, 2776.9372558594, 5585.900390625, 2327.5705566406,
+            5606.7016601562, 1760.3549804688, 10081.3232421875, 1967.7390136719,
+            1474.2607421875, 19779.9140625, 2686.0549316406, 2393.9072265625,
+            2185.9909667969, 3898.0434570312, 1665.9187011719, 1576.662109375,
+            1690.8114013672, 10627.6162109375, 6770.8305664062, 3658.0007324219,
+            1428.4188232422, 2222.9138183594, 2178.1018066406, 22238.625,
+            22350.20703125, 13115.8671875, 1548.3968505859, 3232.5703125,
+            2411.7587890625, 2636.6713867188, 5523.1123046875, 1577.6115722656,
+            7489.677734375, 12446.080078125, 4790.6459960938, 6864.8833007812,
+            2526.705078125, 13605.1103515625, 14471.1953125, 12309.7568359375,
+            2058.4060058594, 1684.7585449219, 8062.2797851562, 3049.6931152344,
+            7157.4365234375, 2778.5651855469, 3956.3020019531, 3688.9733886719,
+            2906.8107910156, 3483.3720703125, 1527.1604003906, 3733.0600585938,
+            6468.4697265625, 160794.0, 15813.22265625, 55915.4921875,
+            4031.8352050781, 7664.5170898438, 3007.4995117188, 1924.2043457031,
+            3809.5688476562, 14442.1865234375, 3863.388671875, 122356.0859375,
+            9796.62890625, 2251.5004882812, 1432.1335449219, 3977.8798828125,
+            3074.4575195312, 3129.9313964844, 12044.9736328125, 7474.6484375,
+            8642.27734375, 7523.927734375, 2037.9163818359, 70981.28125,
+            26322.8828125, 4092.1496582031, 8593.646484375, 1662.6636962891,
+            4250.517578125, 2105.2434082031, 2978.3601074219, 118773.875,
+            20384.595703125, 3913.330078125, 2316.7087402344, 2092.08984375,
+            15750.1015625, 7629.5239257812, 3067.234375, 1703.9067382812,
+            6064.3134765625, 183192.5, 72429.4921875, 9247.12890625, 9688.25,
+            3582.4033203125, 2397.5622558594, 11844.8134765625, 2296.8322753906,
+            1709.6226806641, 2233.7409667969, 1996.6594238281, 2224.8171386719,
+            1477.9654541016, 2361.6772460938, 2519.5424804688, 81508.859375,
+            25324.861328125, 4703.8432617188, 1614.0625, 1639.0384521484,
+            2364.9189453125, 1834.0895996094, 2065.7299804688, 8549.77734375,
+            4358.8642578125, 2207.8986816406, 1909.6280517578, 2544.701171875,
+            1764.7119140625, 1560.3172607422, 7797.7309570312, 4565.646484375,
+            3690.0961914062, 3619.1403808594, 2804.0134277344, 2423.9016113281,
+            2116.8181152344, 2904.3486328125, 1768.9090576172, 4341.6840820312,
+            24121.41796875, 5445.1030273438, 5315.2158203125, 3967.5903320312,
+            4171.9965820312, 1704.7938232422, 6028.9291992188, 4134.1108398438,
+            1743.37109375, 2834.8740234375, 44503.2890625, 1164.0003662109,
+            16474.05859375, 3774.6560058594, 2872.3820800781, 3097.8569335938,
+            19246.3359375, 9768.0439453125, 3945.1433105469, 3595.7751464844,
+            176225.359375, 82714.78125, 21545.458984375, 3969.7224121094,
+            3831.4484863281, 1978.6649169922, 3357.1499023438, 2120.4553222656,
+            1634.4327392578, 4296.2670898438, 1876.4525146484, 1881.8760986328,
+            1751.8648681641, 1936.2098388672, 3394.7329101562, 1815.638671875,
+            5401.5322265625, 3373.3681640625, 6129.755859375, 3897.646484375,
+            3112.55078125, 1688.5185546875, 3906.1352539062, 5277.0668945312,
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+        "RAW_FILE": "20210319_QExHFX2_RSLC3_DSSO_mainlib_rep3",
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+        "INSTRUMENT_TYPES": "Q Exactive HF-X"
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+        "RAW_FILE": "20210319_QExHFX2_RSLC3_DSSO_mainlib_rep3",
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+        "CROSSLINKER_TYPE": "DSSO",
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diff --git a/tests/unit_tests/data/annotation_xl_cl_output.json b/tests/unit_tests/data/annotation_xl_cl_output.json
new file mode 100644
index 0000000..049d8e1
--- /dev/null
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diff --git a/tests/unit_tests/data/annotation_xl_noncl_input.json b/tests/unit_tests/data/annotation_xl_noncl_input.json
new file mode 100644
index 0000000..58f827c
--- /dev/null
+++ b/tests/unit_tests/data/annotation_xl_noncl_input.json
@@ -0,0 +1,689 @@
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diff --git a/tests/unit_tests/data/annotation_xl_noncl_output.json b/tests/unit_tests/data/annotation_xl_noncl_output.json
new file mode 100644
index 0000000..5af2e9e
--- /dev/null
+++ b/tests/unit_tests/data/annotation_xl_noncl_output.json
@@ -0,0 +1,408 @@
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diff --git a/tests/unit_tests/data/fragments_meta_data.json b/tests/unit_tests/data/fragments_meta_data.json
new file mode 100644
index 0000000..1505b42
--- /dev/null
+++ b/tests/unit_tests/data/fragments_meta_data.json
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new file mode 100644
index 0000000..50b50e3
--- /dev/null
+++ b/tests/unit_tests/data/fragments_meta_data_cl_xl.json
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diff --git a/tests/unit_tests/data/fragments_meta_data_non_cl_xl.json b/tests/unit_tests/data/fragments_meta_data_non_cl_xl.json
new file mode 100644
index 0000000..929cb63
--- /dev/null
+++ b/tests/unit_tests/data/fragments_meta_data_non_cl_xl.json
@@ -0,0 +1,146 @@
+[
+    {
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+        "no": 1,
+        "charge": 3,
+        "mass": 24.686314467000003,
+        "min_mass": 24.685820740710664,
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diff --git a/tests/unit_tests/data/spectrum_input.csv b/tests/unit_tests/data/spectrum_input.csv
index 38eed77..88c7b33 100644
--- a/tests/unit_tests/data/spectrum_input.csv
+++ b/tests/unit_tests/data/spectrum_input.csv
@@ -1,5 +1,5 @@
-,MODIFIED_SEQUENCE_MSA,MASS_ANALYZER,PRECURSOR_CHARGE,INTENSITIES,MZ
-0,MADC[UNIMOD:4]AL,ITMS,3,"[8.367374208919049, 2.518602603776224, 2.659845673210951, 4.299728173158516, 8.429437752326157, 0.7658791467979553, 8.687813740941493, 4.242274660314113, 0.8870952432832802, 4.313779684221713, 9.85999775062545, 7.063310047653008, 3.769344924086586, 0.34573944283315394, 9.230812630932526, 2.9032249506511487, 0.16896663233629594, 4.298758775170723, 7.217987502296315, 3.353297934806431, 4.631502162705368, 9.755216218486419, 2.1256480468357153, 5.008320012303572, 3.9299345929497087, 5.518371902352563, 9.678792427922913, 7.188332486612458, 3.575907979109255, 1.6198502512898438, 2.073756458495215, 8.542819672412879, 4.617939899870581, 4.551642363765419, 5.261255462309467, 3.0909922308333106]","[44.60418045, 44.83719672, 65.46789156, 65.74364211, 68.43836233, 68.5568945, 100.04084565, 101.06332082, 107.3093803, 120.28354647, 130.53598685, 132.44059902, 159.17067912, 160.41780244, 160.61696132, 182.38682974, 184.29107775, 185.52539476, 203.86638805, 204.69984021, 221.93538571, 229.01732765, 239.34301275, 240.40067089, 273.94746116, 274.54878787, 318.50472119, 330.1423962, 340.31803747, 362.56644913, 477.78429961, 478.88793184, 549.28001133, 549.89034434, 663.11748586, 678.75743273]"
-1,MMMMMMMMMMMMMMMMMMMMMMMMMMMMMM,ITMS,2,"[1.4164248121239809,8.877522470499141,2.769929884985547,4.2262473778221485,0.11980708135825413,4.018845069231666,9.129723320200778,7.697861046532033,8.208623238448748,0.2639301805160499,9.27485048215434,9.46769208204482,7.557618808649943,3.6574758537198173,8.772023052259234,4.893487701564584,1.75783549921386,2.465661093837327,6.904189639036693,5.927126848082592,1.0850499436616545,5.054284934369222,9.153715686259892,9.131390705792223,4.105196269529302,4.05439827391873,6.5417126797862934,0.31361390940653044,5.146609177243445,4.288002600271125]","[45.161295710005085,51.54885296589418,66.59100232828412,76.46998818311636,88.9497931302318,94.96375607336665,132.96862467283384,132.54034379092417,132.55305878340636,138.0706414233357,141.0999927313521,150.82224511858934,175.92807415613515,181.80640760214433,197.77185605094982,207.40705810019213,220.12250893419375,225.6223314834844,263.84611569200774,263.30510318393493,263.41659790235036,270.02004034886886,272.7518249295351,282.0540723305553,306.8305400229241,313.4314960810718,328.9902378739003,337.98734285656167,351.0333742950934,357.17866493720095,394.91071186443645,394.92476647336974,394.2605513308279,400.76848201659664,404.0125835881873,412.387350512958,437.95367564389164,444.55922566802104,459.68647556806906,469.1467820469859,481.89981407197683,488.3253064884637,525.2095228333937,525.3922134061314,525.5337504706993,531.203711733911,534.4200648177631,543.4991269622365,569.6550041417343,575.8133534060477,591.0796459175361,600.5273208333838,612.5713968777383,618.9441927324988,656.2203894626525,657.1362539082139,657.082171156148,662.5487893264586,665.5395176208337,674.818637557056,699.9166783622037,706.620777331881,721.7521401423007,731.0699195248876,744.5279003979845,749.8527336716827,787.7409658294658,787.4801720786868,788.2320656600327,793.406320902931,796.29361892111,806.1776975914215,831.3221372145645,837.6570409232702,852.8402523367416,861.7960838094697,874.9184972270374,880.763422146188,918.9985345574634,918.5688863250335,918.6807659728639,924.6849083392635,928.1189570717835,936.9283785457288,962.2787187987577,968.4347641322878,983.9939215170773,993.0108377337374,1006.1669113852267,1012.6052471800037,1049.5219822351721,1050.0379828904424,1049.8348192064007,1055.7296672846323,1059.2780736381553,1067.7055229952107,1093.7747425184532,1099.3377993470767,1115.398218600843,1123.9713008676222,1137.632816939427,1143.4017201086865,1181.3407409495042,1181.2692327999941,1181.249650702033,1186.681867775009,1190.0384615136777,1198.8515830270944,1224.3700093191767,1230.3975243951559,1246.4090745955398,1255.6774540311935,1268.4419517888236,1274.3579746109313,1312.1193207244226,1312.1811014354405,1311.6469879473418,1317.7245065105624,1320.7616303725897,1329.8969043283748,1377.9054872054999,1386.8918935200213,1442.6515673868616,1443.1064266726273,1452.3955566891084,1460.5108215533644,1508.7594517706225,1517.1291788857693,1573.5867056668221,1573.7716562562962,1583.2119609851222,1592.111644078143,1639.392538650422,1648.673618965434,1705.0452374402644,1705.3227848374838,1714.323880681171,1723.111408219502,1770.9380486508123,1779.8536228159417,1836.2752798275599,1836.0630738971968,1844.9172338627804,1853.6735977638225,1901.8440972041199,1910.585010434536,1967.0956948390422,1966.8194437069674,1975.9609314719844,1985.2766000449305,2098.0234227672318,2116.317431295543,2228.7832057810197,2247.592389834701,2360.199275899176,2378.384390433377,2491.647799101034,2509.779214713572,2622.80841980401,2640.5814749246942,2753.44052053984,2771.1039877534827,2884.6781347038673,2902.1421208508036,3015.571665816316,3033.938677074293,3146.9298774773843,3164.43184409032,3277.8969636376964,3295.167880881916,3408.813608767307,3426.960576739998,3539.6522482081523,3557.5234870820505,3670.6156544143073,3689.11727078171,3801.5354429013455,3820.0536427641496,3932.6923292585666,3950.6436603255406]"
-2,MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM,ITMS,3,"[]","[]"
-3,MM,ITMS,1,"[1.4164248121239809,8.877522470499141,2.769929884985547,4.2262473778221485]","[49.161295710005085,49.54885296589418,63.59100232828412,79.46998818311636]"
+,MODIFIED_SEQUENCE_MSA,PEPTIDE_LENGTH,MASS_ANALYZER,PRECURSOR_CHARGE,INTENSITIES,MZ
+0,MADC[UNIMOD:4]AL,6,ITMS,3,"[8.367374208919049, 2.518602603776224, 2.659845673210951, 4.299728173158516, 8.429437752326157, 0.7658791467979553, 8.687813740941493, 4.242274660314113, 0.8870952432832802, 4.313779684221713, 9.85999775062545, 7.063310047653008, 3.769344924086586, 0.34573944283315394, 9.230812630932526, 2.9032249506511487, 0.16896663233629594, 4.298758775170723, 7.217987502296315, 3.353297934806431, 4.631502162705368, 9.755216218486419, 2.1256480468357153, 5.008320012303572, 3.9299345929497087, 5.518371902352563, 9.678792427922913, 7.188332486612458, 3.575907979109255, 1.6198502512898438, 2.073756458495215, 8.542819672412879, 4.617939899870581, 4.551642363765419, 5.261255462309467, 3.0909922308333106]","[44.60418045, 44.83719672, 65.46789156, 65.74364211, 68.43836233, 68.5568945, 100.04084565, 101.06332082, 107.3093803, 120.28354647, 130.53598685, 132.44059902, 159.17067912, 160.41780244, 160.61696132, 182.38682974, 184.29107775, 185.52539476, 203.86638805, 204.69984021, 221.93538571, 229.01732765, 239.34301275, 240.40067089, 273.94746116, 274.54878787, 318.50472119, 330.1423962, 340.31803747, 362.56644913, 477.78429961, 478.88793184, 549.28001133, 549.89034434, 663.11748586, 678.75743273]"
+1,MMMMMMMMMMMMMMMMMMMMMMMMMMMMMM,30,ITMS,2,"[1.4164248121239809,8.877522470499141,2.769929884985547,4.2262473778221485,0.11980708135825413,4.018845069231666,9.129723320200778,7.697861046532033,8.208623238448748,0.2639301805160499,9.27485048215434,9.46769208204482,7.557618808649943,3.6574758537198173,8.772023052259234,4.893487701564584,1.75783549921386,2.465661093837327,6.904189639036693,5.927126848082592,1.0850499436616545,5.054284934369222,9.153715686259892,9.131390705792223,4.105196269529302,4.05439827391873,6.5417126797862934,0.31361390940653044,5.146609177243445,4.288002600271125]","[45.161295710005085,51.54885296589418,66.59100232828412,76.46998818311636,88.9497931302318,94.96375607336665,132.96862467283384,132.54034379092417,132.55305878340636,138.0706414233357,141.0999927313521,150.82224511858934,175.92807415613515,181.80640760214433,197.77185605094982,207.40705810019213,220.12250893419375,225.6223314834844,263.84611569200774,263.30510318393493,263.41659790235036,270.02004034886886,272.7518249295351,282.0540723305553,306.8305400229241,313.4314960810718,328.9902378739003,337.98734285656167,351.0333742950934,357.17866493720095,394.91071186443645,394.92476647336974,394.2605513308279,400.76848201659664,404.0125835881873,412.387350512958,437.95367564389164,444.55922566802104,459.68647556806906,469.1467820469859,481.89981407197683,488.3253064884637,525.2095228333937,525.3922134061314,525.5337504706993,531.203711733911,534.4200648177631,543.4991269622365,569.6550041417343,575.8133534060477,591.0796459175361,600.5273208333838,612.5713968777383,618.9441927324988,656.2203894626525,657.1362539082139,657.082171156148,662.5487893264586,665.5395176208337,674.818637557056,699.9166783622037,706.620777331881,721.7521401423007,731.0699195248876,744.5279003979845,749.8527336716827,787.7409658294658,787.4801720786868,788.2320656600327,793.406320902931,796.29361892111,806.1776975914215,831.3221372145645,837.6570409232702,852.8402523367416,861.7960838094697,874.9184972270374,880.763422146188,918.9985345574634,918.5688863250335,918.6807659728639,924.6849083392635,928.1189570717835,936.9283785457288,962.2787187987577,968.4347641322878,983.9939215170773,993.0108377337374,1006.1669113852267,1012.6052471800037,1049.5219822351721,1050.0379828904424,1049.8348192064007,1055.7296672846323,1059.2780736381553,1067.7055229952107,1093.7747425184532,1099.3377993470767,1115.398218600843,1123.9713008676222,1137.632816939427,1143.4017201086865,1181.3407409495042,1181.2692327999941,1181.249650702033,1186.681867775009,1190.0384615136777,1198.8515830270944,1224.3700093191767,1230.3975243951559,1246.4090745955398,1255.6774540311935,1268.4419517888236,1274.3579746109313,1312.1193207244226,1312.1811014354405,1311.6469879473418,1317.7245065105624,1320.7616303725897,1329.8969043283748,1377.9054872054999,1386.8918935200213,1442.6515673868616,1443.1064266726273,1452.3955566891084,1460.5108215533644,1508.7594517706225,1517.1291788857693,1573.5867056668221,1573.7716562562962,1583.2119609851222,1592.111644078143,1639.392538650422,1648.673618965434,1705.0452374402644,1705.3227848374838,1714.323880681171,1723.111408219502,1770.9380486508123,1779.8536228159417,1836.2752798275599,1836.0630738971968,1844.9172338627804,1853.6735977638225,1901.8440972041199,1910.585010434536,1967.0956948390422,1966.8194437069674,1975.9609314719844,1985.2766000449305,2098.0234227672318,2116.317431295543,2228.7832057810197,2247.592389834701,2360.199275899176,2378.384390433377,2491.647799101034,2509.779214713572,2622.80841980401,2640.5814749246942,2753.44052053984,2771.1039877534827,2884.6781347038673,2902.1421208508036,3015.571665816316,3033.938677074293,3146.9298774773843,3164.43184409032,3277.8969636376964,3295.167880881916,3408.813608767307,3426.960576739998,3539.6522482081523,3557.5234870820505,3670.6156544143073,3689.11727078171,3801.5354429013455,3820.0536427641496,3932.6923292585666,3950.6436603255406]"
+2,MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM,31,ITMS,3,"[]","[]"
+3,MM,2,ITMS,1,"[1.4164248121239809,8.877522470499141,2.769929884985547,4.2262473778221485]","[49.161295710005085,49.54885296589418,63.59100232828412,79.46998818311636]"
diff --git a/tests/unit_tests/test_annotation.py b/tests/unit_tests/test_annotation.py
index 25d800a..5722f37 100644
--- a/tests/unit_tests/test_annotation.py
+++ b/tests/unit_tests/test_annotation.py
@@ -18,6 +18,7 @@ def test_annotate_spectra(self):
             index_col=0,
             converters={"INTENSITIES": literal_eval, "MZ": literal_eval},
         )
+
         expected_result = pd.read_csv(
             Path(__file__).parent / "data/spectrum_output.csv",
             index_col=0,
@@ -29,6 +30,29 @@ def test_annotate_spectra(self):
         result = annotation.annotate_spectra(spectrum_input)
         pd.testing.assert_frame_equal(expected_result, result)
 
+    def test_annotate_spectra_noncl_xl(self):
+        """Test annotate spectra non cleavable crosslinked peptides."""
+        spectrum_input = pd.read_json(
+            Path(__file__).parent / "data" / "annotation_xl_noncl_input.json", orient="records"
+        )
+
+        expected_result = pd.read_json(
+            Path(__file__).parent / "data" / "annotation_xl_noncl_output.json", orient="records"
+        )
+
+        result = annotation.annotate_spectra(spectrum_input)
+        pd.testing.assert_frame_equal(expected_result, result)
+
+    def test_annotate_spectra_cl_xl(self):
+        """Test annotate spectra cleavable crosslinked peptides."""
+        spectrum_input = pd.read_json(Path(__file__).parent / "data" / "annotation_xl_cl_input.json", orient="records")
+        expected_result = pd.read_json(
+            Path(__file__).parent / "data" / "annotation_xl_cl_output.json", orient="records"
+        )
+
+        result = annotation.annotate_spectra(spectrum_input)
+        pd.testing.assert_frame_equal(expected_result, result)
+
     def test_handle_multiple_matches(self):
         """Test handle_multiple_matches function."""
         # Example input data with multiple matches. They don't make biological sense but it tests the mathematical correctness.
diff --git a/tests/unit_tests/test_fragments.py b/tests/unit_tests/test_fragments.py
index b0cd7eb..b45e5b2 100644
--- a/tests/unit_tests/test_fragments.py
+++ b/tests/unit_tests/test_fragments.py
@@ -1,4 +1,6 @@
+import json
 import unittest
+from pathlib import Path
 
 from numpy.testing import assert_almost_equal
 
@@ -10,19 +12,19 @@ class TestGetModifications:
 
     def test_get_modifications(self):
         """Test get_modifications."""
-        assert fragments._get_modifications("ABC") == ({}, 1, "ABC")
+        assert fragments._get_modifications("ABC") == {}
 
     def test_get_modifications_carbamidomethylation(self):
         """Test get_modifications."""
-        assert fragments._get_modifications("ABC[UNIMOD:4]") == ({2: 57.02146}, 1, "ABC")
+        assert fragments._get_modifications("ABC[UNIMOD:4]") == {2: 57.021464}
 
     def test_get_modifications_tmt_tag(self):
         """Test get_modifications."""
-        assert fragments._get_modifications("[UNIMOD:737]-ABC[UNIMOD:4]") == ({0: 229.162932, 2: 57.02146}, 2, "ABC")
+        assert fragments._get_modifications("[UNIMOD:737]-ABC[UNIMOD:4]") == {-2: 229.162932, 2: 57.021464}
 
     def test_get_modifications_tmtpro_tag(self):
         """Test get_modifications."""
-        assert fragments._get_modifications("[UNIMOD:2016]-ABC[UNIMOD:4]") == ({0: 304.207146, 2: 57.02146}, 2, "ABC")
+        assert fragments._get_modifications("[UNIMOD:2016]-ABC[UNIMOD:4]") == {-2: 304.207146, 2: 57.021464}
 
 
 class TestComputeMasses(unittest.TestCase):
@@ -61,22 +63,22 @@ def test_compute_ion_masses_tmtpro(self):
     def test_compute_peptide_masses(self):
         """Test computation of peptide masses with valid input."""
         seq = "SEQUENC[UNIMOD:4]E"
-        self.assertEqual(fragments.compute_peptide_mass(seq), 1045.2561516699998)
+        self.assertEqual(fragments.compute_peptide_mass(seq), 1045.2561556699998)
 
     def test_compute_peptide_masses_tmtpro(self):
         """Test computation of peptide masses with valid input and tmt tag."""
         seq = "[UNIMOD:737]-SEQUENC[UNIMOD:4]E"
-        self.assertEqual(fragments.compute_peptide_mass(seq), 1274.41908367)
+        self.assertEqual(fragments.compute_peptide_mass(seq), 1274.41908767)
 
     def test_compute_peptide_masses_with_invalid_syntax(self):
         """Negative testing of comuptation of peptide mass with unsupported syntax of mod string."""
         seq = "SEQUEM(Ox.)CE"
-        self.assertRaises(AssertionError, fragments.compute_peptide_mass, seq)
+        self.assertRaises(KeyError, fragments.compute_peptide_mass, seq)
 
     def test_compute_peptide_masses_with_invalid_mod(self):
         """Negative testing of computation of peptide mass with unknown modification in mod string."""
         seq = "SEQUENC[UNIMOD:0]E"
-        self.assertRaises(AssertionError, fragments.compute_peptide_mass, seq)
+        self.assertRaises(KeyError, fragments.compute_peptide_mass, seq)
 
 
 class TestMassTolerances(unittest.TestCase):
@@ -105,3 +107,80 @@ def test_mass_tol_with_defaults(self):
         self.assertEqual(window_ftms, (9.9998, 10.0002))
         self.assertEqual(window_tof, (9.9996, 10.0004))
         self.assertEqual(window_itms, (9.65, 10.35))
+
+
+class TestInitializePeaks(unittest.TestCase):
+    """Class to test initialize_peaks function."""
+
+    def test_initialize_peaks(self):
+        """Test initialize_peaks with basic input."""
+        fragments_input = {"sequence": "AAAA", "mass_analyzer": "FTMS", "charge": 3, "noncl_xl": False}
+
+        with open(Path(__file__).parent / "data/fragments_meta_data.json") as file:
+            expected_list_out = json.load(file)
+        expected_tmt_nt_term = 1
+        expected_peptide_sequence = "AAAA"
+        expected_mass_s = 302.15902
+
+        actual_list_out, actual_tmt_n_term, actual_peptide_sequence, actual_calc_mass_s = fragments.initialize_peaks(
+            **fragments_input
+        )
+
+        self.assertEqual(actual_list_out, expected_list_out)
+        self.assertEqual(actual_tmt_n_term, expected_tmt_nt_term)
+        self.assertEqual(actual_peptide_sequence, expected_peptide_sequence)
+        assert_almost_equal(actual_calc_mass_s, expected_mass_s, decimal=5)
+
+    def test_initialize_peaks_non_cl_xl(self):
+        """Test initialize_peaks_xl with basic input for non-cleavable crosslinked peptides."""
+        initialize_peaks_xl_input = {
+            "sequence": "AKC",
+            "mass_analyzer": "FTMS",
+            "crosslinker_position": 2,
+            "crosslinker_type": "BS3",
+            "sequence_beta": "AKA",
+        }
+
+        with open(Path(__file__).parent / "data/fragments_meta_data_non_cl_xl.json") as file:
+            expected_fragments_meta_data = json.load(file)
+        expected_tmt_nt_term = 1
+        expected_peptide_sequence = "AKC"
+        expected_mass = 320.15182
+
+        (
+            actual_fragments_meta_data,
+            actual_tmt_n_term,
+            actual_peptide_sequence,
+            actual_mass,
+        ) = fragments.initialize_peaks_xl(**initialize_peaks_xl_input)
+        self.assertEqual(actual_fragments_meta_data, expected_fragments_meta_data)
+        self.assertEqual(actual_tmt_n_term, expected_tmt_nt_term)
+        self.assertEqual(actual_peptide_sequence, expected_peptide_sequence)
+        assert_almost_equal(actual_mass, expected_mass, decimal=5)
+
+    def test_initialize_peaks_cl_xl(self):
+        """Test initialize_peaks_xl with basic input for cleavable crosslinked peptides."""
+        initialize_peaks_xl_input = {
+            "sequence": "AKC",
+            "mass_analyzer": "FTMS",
+            "crosslinker_position": 2,
+            "crosslinker_type": "DSSO",
+            "sequence_beta": "AKA",
+        }
+
+        with open(Path(__file__).parent / "data/fragments_meta_data_cl_xl.json") as file:
+            expected_fragments_meta_data = json.load(file)
+        expected_tmt_nt_term = 1
+        expected_peptide_sequence = "AKC"
+        expected_mass = 320.15182
+
+        (
+            actual_fragments_meta_data,
+            actual_tmt_n_term,
+            actual_peptide_sequence,
+            actual_mass,
+        ) = fragments.initialize_peaks_xl(**initialize_peaks_xl_input)
+        self.assertEqual(actual_fragments_meta_data, expected_fragments_meta_data)
+        self.assertEqual(actual_tmt_n_term, expected_tmt_nt_term)
+        self.assertEqual(actual_peptide_sequence, expected_peptide_sequence)
+        assert_almost_equal(actual_mass, expected_mass, decimal=5)
diff --git a/tests/unit_tests/test_fragments_ratio.py b/tests/unit_tests/test_fragments_ratio.py
index 0b901aa..85989ee 100644
--- a/tests/unit_tests/test_fragments_ratio.py
+++ b/tests/unit_tests/test_fragments_ratio.py
@@ -309,6 +309,7 @@ def test_count_observed_but_not_predicted_y(self):
             ],
             fr.ObservationState.INVALID_ION,
         )
+
         np.testing.assert_equal(
             fr.FragmentsRatio.count_observation_states(
                 observation_state, fr.ObservationState.OBS_BUT_NOT_PRED, constants.Y_ION_MASK
@@ -320,6 +321,171 @@ def test_count_observed_but_not_predicted_y(self):
 class TestCalc:
     """Class to test calc."""
 
+    def test_calc_xl(self):
+        """Test calc."""
+        z = constants.EPSILON
+        #                                         y1.1  y1.2  y1.3  b1.1  b1.2  b1.3  y2.1  y2.2  y2.3
+        predicted_intensities = get_padded_array([7.2, 2.3, 0.01, 0.02, 6.1, 3.1, z, z, 0], xl=True)
+        observed_intensities = get_padded_array([10.2, z, 1.3, z, 8.2, z, 3.2, z, 0], xl=True)
+
+        fragments_ratio = fr.FragmentsRatio(predicted_intensities, observed_intensities)
+        fragments_ratio.calc(xl=True)
+
+        # counting metrics - peptide_a
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_predicted_a"][0], 4)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_predicted_b_a"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_predicted_y_a"][0], 2)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_a"][0], 4)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_b_a"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_y_a"][0], 3)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_and_predicted_a"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_and_predicted_b_a"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_and_predicted_y_a"][0], 1)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_and_not_predicted_a"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_and_not_predicted_b_a"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_and_not_predicted_y_a"][0], 1)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_but_not_predicted_a"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_but_not_predicted_b_a"][0], 0)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_but_not_predicted_y_a"][0], 2)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_but_predicted_a"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_but_predicted_b_a"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_but_predicted_y_a"][0], 1)
+
+        # fractional count metrics - peptide a
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_predicted_a"][0], 4 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_predicted_b_a"][0], 2 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_predicted_y_a"][0], 2 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_a"][0], 4 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_b_a"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_y_a"][0], 3 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_a"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_b_a"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_y_a"][0], 1 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_a"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_b_a"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_y_a"][0], 1 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_but_not_predicted_a"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_but_not_predicted_b_a"][0], 0 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_but_not_predicted_y_a"][0], 2 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_but_predicted_a"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_but_predicted_b_a"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_but_predicted_y_a"][0], 1 / 5)
+
+        # fractional count metrics relative to predictions - peptide a
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_vs_predicted_a"][0], 2 / 4)
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_and_predicted_b_vs_predicted_b_a"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_and_predicted_y_vs_predicted_y_a"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_vs_predicted_a"][0], 2 / 4
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_b_vs_predicted_b_a"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_y_vs_predicted_y_a"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_but_not_predicted_vs_predicted_a"][0], 2 / 4
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_but_not_predicted_b_vs_predicted_b_a"][0], 0 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_but_not_predicted_y_vs_predicted_y_a"][0], 2 / 2
+        )
+
+        # peptide  b
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_predicted_b"][0], 4)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_predicted_b_b"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_predicted_y_b"][0], 2)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_b"][0], 4)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_b_b"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_y_b"][0], 3)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_and_predicted_b"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_and_predicted_b_b"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_and_predicted_y_b"][0], 1)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_and_not_predicted_b"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_and_not_predicted_b_b"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_and_not_predicted_y_b"][0], 1)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_but_not_predicted_b"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_but_not_predicted_b_b"][0], 0)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_observed_but_not_predicted_y_b"][0], 2)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_but_predicted_b"][0], 2)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_but_predicted_b_b"][0], 1)
+        np.testing.assert_equal(fragments_ratio.metrics_val["count_not_observed_but_predicted_y_b"][0], 1)
+
+        # fractional count metrics - peptide b
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_predicted_b"][0], 4 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_predicted_b_b"][0], 2 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_predicted_y_b"][0], 2 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_b"][0], 4 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_b_b"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_y_b"][0], 3 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_b"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_b_b"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_y_b"][0], 1 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_b"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_b_b"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_y_b"][0], 1 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_but_not_predicted_b"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_but_not_predicted_b_b"][0], 0 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_but_not_predicted_y_b"][0], 2 / 5)
+
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_but_predicted_b"][0], 2 / 8)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_but_predicted_b_b"][0], 1 / 3)
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_not_observed_but_predicted_y_b"][0], 1 / 5)
+
+        # fractional count metrics relative to predictions - peptide b
+        np.testing.assert_equal(fragments_ratio.metrics_val["fraction_observed_and_predicted_vs_predicted_b"][0], 2 / 4)
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_and_predicted_b_vs_predicted_b_b"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_and_predicted_y_vs_predicted_y_b"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_vs_predicted_b"][0], 2 / 4
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_b_vs_predicted_b_b"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_not_observed_and_not_predicted_y_vs_predicted_y_b"][0], 1 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_but_not_predicted_vs_predicted_b"][0], 2 / 4
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_but_not_predicted_b_vs_predicted_b_b"][0], 0 / 2
+        )
+        np.testing.assert_equal(
+            fragments_ratio.metrics_val["fraction_observed_but_not_predicted_y_vs_predicted_y_b"][0], 2 / 2
+        )
+
     def test_calc(self):
         """Test calc."""
         z = constants.EPSILON
@@ -418,8 +584,13 @@ def assert_equal_sparse(a, b):
     assert (a != b).nnz == 0  # checks that there are 0 elements for which a != b
 
 
-def get_padded_array(arr, padding_value=0):
+def get_padded_array(arr, padding_value=0, xl: bool = False):
     """Get padded array."""
-    return scipy.sparse.csr_matrix(
-        np.array([np.pad(arr, (0, constants.VEC_LENGTH - len(arr)), "constant", constant_values=padding_value)])
-    )
+    if xl:
+        padded_arr = np.pad(arr, (0, constants.VEC_LENGTH_CMS2 - len(arr)), "constant", constant_values=padding_value)
+        return scipy.sparse.csr_matrix(np.concatenate([padded_arr, padded_arr]))
+
+    else:
+        return scipy.sparse.csr_matrix(
+            np.array([np.pad(arr, (0, constants.VEC_LENGTH - len(arr)), "constant", constant_values=padding_value)])
+        )
diff --git a/tests/unit_tests/test_mod_string.py b/tests/unit_tests/test_mod_string.py
index 1372932..6edc124 100644
--- a/tests/unit_tests/test_mod_string.py
+++ b/tests/unit_tests/test_mod_string.py
@@ -170,7 +170,7 @@ def test_internal_to_mod_masses(self):
         """Test internal with mods to internal without_mods."""
         self.assertEqual(
             mod.internal_to_mod_mass(["[UNIMOD:737]-ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"]),
-            ["[+229.162932]-ABC[+57.02146]DEFGHK[+229.162932]"],
+            ["[+229.162932]-ABC[+57.021464]DEFGHK[+229.162932]"],
         )
 
     def test_proteomicsdb_to_internal(self):