[Re-]Annotating Decoy Labels

If your crosslink-spectrum-matches or crosslinks have incorrect or missing decoy labels you might want to [re-]annotate them for down-stream analysis. The examples below show how to do that with the pyXLMS function transform.reannotate_decoy_labels() [docs ].


from pyXLMS import __version__
 
print(f"Installed pyXLMS version: {__version__}")

✓


    Installed pyXLMS version: 1.8.7


from pyXLMS import data
from pyXLMS import parser
from pyXLMS import transform

All data transformation functionality - including reannotate_decoy_labels() to (re-)annotate decoy labels - is available via the transform submodule. We also import the parser submodule here for reading result files and data to manually create some crosslinks.


parser_result = parser.read(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1.pdResult",
    engine="MS Annika",
    crosslinker="DSS",
)

✓


    Reading MS Annika CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 8584.79it/s]
    Reading MS Annika crosslinks...: 100%|██████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 15749.51it/s]

We read crosslink-spectrum-matches and crosslinks using the generic parser from a single .pdResult file.


csms = parser_result["crosslink-spectrum-matches"]
xls = parser_result["crosslinks"]

For easier access we assign our crosslink-spectrum-matches (CSMs) to the variable csms and our crosslinks to the variable xls.

Important

[Re-]Annotation of crosslink-spectrum-matches, crosslinks, and parser_results works exactly the same, as demonstrated by the examples below.

Decoy [Re-]Annotation `by_mapping`

Any of the following are valid function calls:

transform.reannotate_decoy_labels(csms, ...)
transform.reannotate_decoy_labels(xls, ...)
transform.reannotate_decoy_labels(parser_result, ...)

[Re-]Annotating Crosslink-Spectrum-Matches


csms_none = transform.reannotate_decoy_labels(
    csms, by_mapping={True: None, False: None}
)

✓


    Reannotating decoy labels by mapping: {True: None, False: None}!
    Annotating crosslink-spectrum-matches...: 100%|████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<?, ?it/s]

We are actually using our transform.reannotate_decoy_labels() function here to create some dummy data without decoy labels. Passing by_mapping={True: None, False: None} to the function means we label anything that has "alpha_decoy" or "beta_decoy" with value True or False as None. In reality one would of course rather use something like by_mapping={None: False} which would label all data with "alpha_decoy" or "beta_decoy" being None to False (meaning they would be considered target hits). This could be useful if you read results from a search engine that does not label their CSMs or crosslinks but you know that all of them are target hits. You can read more about the transform.reannotate_decoy_labels() function here: docs.


_ = transform.summary(csms)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 786.0
    Number of target-decoy CSMs: 39.0
    Number of decoy-decoy CSMs: 1.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926

One special aspect about this function is that it does not modify the underlying data: the original CSMs are still the same with their original decoy labels!

Important

Please note that the function transform.reannotate_decoy_labels() will always return a copy of the original data, no matter which arguments or by_* parameter is passed!


_ = transform.summary(csms_none)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 0.0
    Number of target-decoy CSMs: 0.0
    Number of decoy-decoy CSMs: 0.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926

The reannotated csms_none is a complete new list of objects that does not share memory with the original CSMs!

[Re-]Annotating Crosslinks


xls_none = transform.reannotate_decoy_labels(xls, by_mapping={True: None, False: None})

✓


    Reannotating decoy labels by mapping: {True: None, False: None}!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<?, ?it/s]


_ = transform.summary(xls)

✓


    Number of crosslinks: 300.0
    Number of unique crosslinks by peptide: 300.0
    Number of unique crosslinks by protein: 298.0
    Number of intra crosslinks: 279.0
    Number of inter crosslinks: 21.0
    Number of target-target crosslinks: 265.0
    Number of target-decoy crosslinks: 0.0
    Number of decoy-decoy crosslinks: 35.0
    Minimum crosslink score: 1.1132827525593785
    Maximum crosslink score: 452.9861536355926


_ = transform.summary(xls_none)

✓


    Number of crosslinks: 300.0
    Number of unique crosslinks by peptide: 300.0
    Number of unique crosslinks by protein: 298.0
    Number of intra crosslinks: 279.0
    Number of inter crosslinks: 21.0
    Number of target-target crosslinks: 0.0
    Number of target-decoy crosslinks: 0.0
    Number of decoy-decoy crosslinks: 0.0
    Minimum crosslink score: 1.1132827525593785
    Maximum crosslink score: 452.9861536355926

Reannotating crosslinks works and behaves exactly the same way!

[Re-]Annotating a `parser_result`


parser_result_none = transform.reannotate_decoy_labels(
    parser_result, by_mapping={True: None, False: None}
)

✓


    Reannotating decoy labels by mapping: {True: None, False: None}!
    Annotating crosslink-spectrum-matches...: 100%|████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 823703.07it/s]
    Reannotating decoy labels by mapping: {True: None, False: None}!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 199697.06it/s]


_ = transform.summary(parser_result)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 786.0
    Number of target-decoy CSMs: 39.0
    Number of decoy-decoy CSMs: 1.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926
    Number of crosslinks: 300.0
    Number of unique crosslinks by peptide: 300.0
    Number of unique crosslinks by protein: 298.0
    Number of intra crosslinks: 279.0
    Number of inter crosslinks: 21.0
    Number of target-target crosslinks: 265.0
    Number of target-decoy crosslinks: 0.0
    Number of decoy-decoy crosslinks: 35.0
    Minimum crosslink score: 1.1132827525593785
    Maximum crosslink score: 452.9861536355926


_ = transform.summary(parser_result_none)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 0.0
    Number of target-decoy CSMs: 0.0
    Number of decoy-decoy CSMs: 0.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926
    Number of crosslinks: 300.0
    Number of unique crosslinks by peptide: 300.0
    Number of unique crosslinks by protein: 298.0
    Number of intra crosslinks: 279.0
    Number of inter crosslinks: 21.0
    Number of target-target crosslinks: 0.0
    Number of target-decoy crosslinks: 0.0
    Number of decoy-decoy crosslinks: 0.0
    Minimum crosslink score: 1.1132827525593785
    Maximum crosslink score: 452.9861536355926

Reannotating a parser_result also works and behaves the same way and will reannotate both CSMs and crosslinks (if available)!

Decoy [Re-]Annotation `by_decoy_protein_prefix`


crosslinks = [
    data.create_crosslink_min(
        "PEKP",
        3,
        "TIDKE",
        4,
        proteins_a=["PROTEIN"],
        proteins_b=["REV__PROTEIN"],
        decoy_a=None,
        decoy_b=None,
    ),
]
transform.display(crosslinks[0])

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     ['PROTEIN']
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        None
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      ['REV__PROTEIN']
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         None
    Crosslink Type:                     inter
    Crosslink Score:                    None

Let’s consider a list of crosslinks containing the above example crosslink where the beta protein is a decoy match, characterized by the REV__ prefix string. However, the decoy labels for alpha and beta are both missing (None).


crosslinks_reannotated = transform.reannotate_decoy_labels(
    crosslinks, by_decoy_protein_prefix="REV__"
)

✓


    Reannotating decoy labels by decoy protein prefix: REV__!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]

By calling transform.reannotate_decoy_labels(crosslinks, by_decoy_protein_prefix="REV__") we can signal the function that any protein starting with a "REV__" string should be considered a decoy match. You can read more about the transform.reannotate_decoy_labels() function here: docs.


transform.display(crosslinks_reannotated[0])

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     ['PROTEIN']
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        False
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      ['REV__PROTEIN']
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         True
    Crosslink Type:                     inter
    Crosslink Score:                    None

After reannotation alpha and beta are correctly labelled as target and decoy respectively.


crosslinks = [
    data.create_crosslink_min(
        "PEKP",
        3,
        "TIDKE",
        4,
        proteins_a=["PROTEIN"],
        proteins_b=["REV__PROTEIN", "PROTEIN"],
        decoy_a=None,
        decoy_b=None,
    ),
]
transform.display(crosslinks[0])

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     ['PROTEIN']
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        None
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      ['REV__PROTEIN', 'PROTEIN']
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         None
    Crosslink Type:                     intra
    Crosslink Score:                    None

Let’s consider a second case, this time the beta peptide maps to both a decoy and a target protein!


crosslinks_reannotated = transform.reannotate_decoy_labels(
    crosslinks, by_decoy_protein_prefix="REV__"
)

✓


    Reannotating decoy labels by decoy protein prefix: REV__!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]


transform.display(crosslinks_reannotated[0])

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     ['PROTEIN']
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        False
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      ['REV__PROTEIN', 'PROTEIN']
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         False
    Crosslink Type:                     intra
    Crosslink Score:                    None

Important

After reannotation beta is labelled as target hit this time because one of the proteins was a target match. It would only be labelled as a decoy if all of its associated proteins are decoy matches!

Decoy [Re-]Annotation `by_decoy_protein_substring`


crosslinks = [
    data.create_crosslink_min(
        "PEKP",
        3,
        "TIDKE",
        4,
        proteins_a=["PROTEIN"],
        proteins_b=["GENE | REV__PROTEIN"],
        decoy_a=None,
        decoy_b=None,
    ),
]
transform.display(crosslinks[0])

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     ['PROTEIN']
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        None
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      ['GENE | REV__PROTEIN']
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         None
    Crosslink Type:                     inter
    Crosslink Score:                    None

Let’s consider a list of crosslinks containing the above example crosslink where the beta protein is a decoy match, characterized by the REV__ substring which is also prefixed by a "GENE | " symbol. However, the decoy labels for alpha and beta are both missing (None). Because of the gene prefix reannotation by_decoy_protein_prefix would not work.


crosslinks_reannotated = transform.reannotate_decoy_labels(
    crosslinks, by_decoy_protein_substring="REV__"
)

✓


    Reannotating decoy labels by decoy protein substring: REV__!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]

By calling transform.reannotate_decoy_labels(crosslinks, by_decoy_protein_substring="REV__") we can signal the function that any protein containing a "REV__" string anywhere should be considered a decoy match. You can read more about the transform.reannotate_decoy_labels() function here: docs.


transform.display(crosslinks_reannotated[0])

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     ['PROTEIN']
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        False
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      ['GENE | REV__PROTEIN']
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         True
    Crosslink Type:                     inter
    Crosslink Score:                    None

After reannotation alpha and beta are correctly labelled as target and decoy respectively.

Decoy [Re-]Annotation `by_target_fasta`


_ = transform.summary(csms_none)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 0.0
    Number of target-decoy CSMs: 0.0
    Number of decoy-decoy CSMs: 0.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926

Remember the dummy data we generated at the start? This data has no decoy labels and also no protein prefixes or substrings that would allow decoy label inference.


csms_reannotated = transform.reannotate_decoy_labels(
    csms_none, by_target_fasta="../../data/_fasta/Cas9_plus10.fasta"
)

✓


    Reannotating decoy labels by provided target fasta file!
    Annotating crosslink-spectrum-matches...: 100%|████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 433495.38it/s]

By calling transform.reannotate_decoy_labels(csms_none, by_target_fasta="../../data/_fasta/Cas9_plus10.fasta") we can signal the function that any peptide/protein NOT IN the FASTA file "../../data/_fasta/Cas9_plus10.fasta" should be considered a decoy match. In the background this checks if the FASTA file contains the peptide sequence and does not use protein names. You can read more about the transform.reannotate_decoy_labels() function here: docs.


_ = transform.summary(csms_reannotated)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 786.0
    Number of target-decoy CSMs: 39.0
    Number of decoy-decoy CSMs: 1.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926


_ = transform.summary(csms)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 786.0
    Number of target-decoy CSMs: 39.0
    Number of decoy-decoy CSMs: 1.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926

As you can see this reproduces exactly our original CSMs!

Decoy [Re-]Annotation `by_decoy_fasta`


_ = transform.summary(csms_none)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 0.0
    Number of target-decoy CSMs: 0.0
    Number of decoy-decoy CSMs: 0.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926


csms_reannotated = transform.reannotate_decoy_labels(
    csms_none, by_decoy_fasta="../../data/_fasta/Cas9_plus10.fasta"
)

✓


    Reannotating decoy labels by provided decoy fasta file!
    Annotating crosslink-spectrum-matches...: 100%|████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 823898.95it/s]

We can do the inverse operation by calling transform.reannotate_decoy_labels(csms_none, by_decoy_fasta="../../data/_fasta/Cas9_plus10.fasta") where we signal the function that any peptide/protein IN the FASTA file "../../data/_fasta/Cas9_plus10.fasta" should be considered a decoy match. In the background this checks if the FASTA file contains the peptide sequence and does not use protein names. You can read more about the transform.reannotate_decoy_labels() function here: docs.


_ = transform.summary(csms_reannotated)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 1.0
    Number of target-decoy CSMs: 39.0
    Number of decoy-decoy CSMs: 786.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926


_ = transform.summary(csms)

✓


    Number of CSMs: 826.0
    Number of unique CSMs: 826.0
    Number of intra CSMs: 803.0
    Number of inter CSMs: 23.0
    Number of target-target CSMs: 786.0
    Number of target-decoy CSMs: 39.0
    Number of decoy-decoy CSMs: 1.0
    Minimum CSM score: 1.1132827525593785
    Maximum CSM score: 452.9861536355926

This of course also produces the inverse results of our original data!

Decoy [Re-]Annotation `by_function`


crosslinks = [
    data.create_crosslink_min(
        "PEKP",
        3,
        "TIDKE",
        4,
        decoy_a=None,
        decoy_b=None,
        additional_information={"Decoy Class": "TD"},
    ),
]
transform.display(crosslinks[0], show_additional_information=True)

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     None
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        None
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      None
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         None
    Crosslink Type:                     inter
    Crosslink Score:                    None
    Additional Information:             {'Decoy Class': 'TD'}

Let’s consider a list of crosslinks containing the above example crosslink where the beta protein is a decoy match, characterized by data that is only available in the additional_information, e.g. it is a TD crosslink meaning the first peptide is a target match and the second peptide is a decoy match. However, the decoy labels for alpha and beta are both missing (None).


from typing import Dict, Any, Tuple
 
 
def parse_alpha_beta_target_decoy(crosslink: Dict[str, Any]) -> Tuple[bool, bool]:
    is_decoy_alpha: bool = crosslink["additional_information"]["Decoy Class"][0] == "D"
    is_decoy_beta: bool = crosslink["additional_information"]["Decoy Class"][1] == "D"
    return is_decoy_alpha, is_decoy_beta
 
 
crosslinks_reannotated = transform.reannotate_decoy_labels(
    crosslinks, by_function=parse_alpha_beta_target_decoy
)

✓


    Reannotating decoy labels by provided function!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]

We can write our own function to parse decoy labels such as parse_alpha_beta_target_decoy(). The function needs to take a CSM or crosslink as input and should return two boolean values: the first one should be the decoy label for the alpha peptide, and the second one should be the decoy label for the beta peptide. We can then do reannotation by calling transform.reannotate_decoy_labels(crosslinks, by_function=parse_alpha_beta_target_decoy) where we pass our custom function as an argument. You can read more about the transform.reannotate_decoy_labels() function here: docs.


transform.display(crosslinks_reannotated[0], show_additional_information=True)

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     None
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        False
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      None
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         True
    Crosslink Type:                     inter
    Crosslink Score:                    None
    Additional Information:             {'Decoy Class': 'TD'}

This correctly labels our crosslink with alpha as target and beta as decoy!

Warning

Please note that labelling based on values in additional_information must be done with extreme caution as alpha and beta might be switched during parsing because pyXLMS uses its own ordering rules!

This is demonstrated by the example below:


crosslinks = [
    data.create_crosslink_min(
        "TIDKE",
        4,
        "PEKP",
        3,
        decoy_a=None,
        decoy_b=None,
        additional_information={"Decoy Class": "TD"},
    ),
]

Let’s consider a list of crosslinks containing the above example crosslink where the PEKP peptide is a decoy match, characterized by data that is only available in the additional_information, e.g. it is a TD crosslink meaning the first peptide is a target match and the second peptide is a decoy match. However, the decoy labels for alpha and beta are both missing (None).


transform.display(crosslinks[0], show_additional_information=True)

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     None
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        None
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      None
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         None
    Crosslink Type:                     inter
    Crosslink Score:                    None
    Additional Information:             {'Decoy Class': 'TD'}

As you can see the peptides got swapped upon creation of the crosslink because pyXLMS internally orders them alphabetically for consistency and to avoid duplicates (e.g. the TIDKE4-PEKP3 crosslink is the same as the PEKP3-TIDKE4 crosslink).


crosslinks_reannotated = transform.reannotate_decoy_labels(
    crosslinks, by_function=parse_alpha_beta_target_decoy
)

✓


    Reannotating decoy labels by provided function!
    Annotating crosslinks...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]


transform.display(crosslinks_reannotated[0], show_additional_information=True)

✓


    Data Type:                          crosslink
    Completeness:                       partial
    Alpha Peptide:                      PEKP
    Alpha Peptide Crosslink Position:   3
    Alpha Proteins:                     None
    Alpha Proteins Crosslink Positions: None
    Alpha Decoy:                        False
    Beta Peptide:                       TIDKE
    Beta Peptide Crosslink Position:    4
    Beta Proteins:                      None
    Beta Proteins Crosslink Positions:  None
    Beta Decoy:                         True
    Crosslink Type:                     inter
    Crosslink Score:                    None
    Additional Information:             {'Decoy Class': 'TD'}

Reannotation by the same custom parse_alpha_beta_target_decoy() function would now assign false decoy labels because alpha and beta were swapped!

[Re-]Annotating Decoy Labels

Decoy [Re-]Annotation by_mapping

[Re-]Annotating Crosslink-Spectrum-Matches

[Re-]Annotating Crosslinks

[Re-]Annotating a parser_result

Decoy [Re-]Annotation by_decoy_protein_prefix

Decoy [Re-]Annotation by_decoy_protein_substring

Decoy [Re-]Annotation by_target_fasta

Decoy [Re-]Annotation by_decoy_fasta

Decoy [Re-]Annotation by_function

Decoy [Re-]Annotation `by_mapping`

[Re-]Annotating a `parser_result`

Decoy [Re-]Annotation `by_decoy_protein_prefix`

Decoy [Re-]Annotation `by_decoy_protein_substring`

Decoy [Re-]Annotation `by_target_fasta`

Decoy [Re-]Annotation `by_decoy_fasta`

Decoy [Re-]Annotation `by_function`