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DocumentationResult File ReadingReading MS Annika Result Files

Reading MS Annika Result Files

from pyXLMS import __version__
 
print(f"Installed pyXLMS version: {__version__}")
✓
    Installed pyXLMS version: 1.1.2
from pyXLMS import parser
from pyXLMS import transform

All functionality to parse crosslink-spectrum-matches (CSMs) and crosslinks (XLs) from MS Annika result files is available via the parser submodule. We also import the transform submodule to show some summary statistics of the read files.

Reading MS Annika Result Files via parser.read()

parser_result = parser.read(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt",
    engine="MS Annika",
    crosslinker="DSS",
)
✓
    Reading MS Annika CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 7639.53it/s]

We can read any MS Annika result file using the parser.read() method and setting engine="MS Annika". The method also requires us to specify the used crosslinker, in this case DSS was used (crosslinker="DSS"). You can read the documentation for the parser.read() method here: docs.

for k, v in parser_result.items():
    print(f"{k}: {type(v) if isinstance(v, list) else v}")
✓
    data_type: parser_result
    completeness: partial
    search_engine: MS Annika
    crosslink-spectrum-matches: <class 'list'>
    crosslinks: None

The parser.read() method returns a dictionary with a set of specified keys and their values. We refer to this dictionary as a parser_result object. All parser.read* methods return such a parser_result object, you can read more about that here: docs, and here: data types specification.

As you can see from the parser_result the MS Annika result file contained CSMs. We would be able to access those via parser_result["crosslink-spectrum-matches"].

_ = 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: 1.11
    Maximum CSM score: 452.99

With the transform.summary() method we can also print out some summary statistics about our read CSMs. You can read more about the method here: docs.

sample_csm = parser_result["crosslink-spectrum-matches"][0]
for k, v in sample_csm.items():
    print(f"{k}: {v}")
✓
    data_type: crosslink-spectrum-match
    completeness: full
    alpha_peptide: GQKNSR
    alpha_modifications: {3: ('DSS', 138.06808)}
    alpha_peptide_crosslink_position: 3
    alpha_proteins: ['Cas9']
    alpha_proteins_crosslink_positions: [779]
    alpha_proteins_peptide_positions: [777]
    alpha_score: 119.83
    alpha_decoy: False
    beta_peptide: GQKNSR
    beta_modifications: {3: ('DSS', 138.06808)}
    beta_peptide_crosslink_position: 3
    beta_proteins: ['Cas9']
    beta_proteins_crosslink_positions: [779]
    beta_proteins_peptide_positions: [777]
    beta_score: 119.83
    beta_decoy: False
    crosslink_type: intra
    score: 119.83
    spectrum_file: XLpeplib_Beveridge_QEx-HFX_DSS_R1.raw
    scan_nr: 2257
    charge: 3
    retention_time: 733.188
    ion_mobility: 0.0
    additional_information: None

Here is an example CSM, you can learn more about the specific attributes and their values here: docs, and here: data types specification.

parser_result = parser.read(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_Crosslinks.xlsx",
    engine="MS Annika",
    crosslinker="DSS",
)
✓
    Reading MS Annika crosslinks...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 8801.27it/s]

We can also as easily read a crosslink result file, notice how this time we also read a Microsoft Excel file instead of a txt file and don’t even have to change anything. This is because when a filename is given, the parser will automatically detect the corresponding extension and use the correct sub-method. This behaviour is controlled by the format parameter in the MS Annika parser . More about that later…

_ = transform.summary(parser_result)
✓
    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: 1.11
    Maximum crosslink score: 452.99

Similarly, with the transform.summary() method we can also print out some summary statistics about our read crosslinks.

sample_crosslink = parser_result["crosslinks"][0]
for k, v in sample_crosslink.items():
    print(f"{k}: {v}")
✓
    data_type: crosslink
    completeness: full
    alpha_peptide: GQKNSR
    alpha_peptide_crosslink_position: 3
    alpha_proteins: ['Cas9']
    alpha_proteins_crosslink_positions: [779]
    alpha_decoy: False
    beta_peptide: GQKNSR
    beta_peptide_crosslink_position: 3
    beta_proteins: ['Cas9']
    beta_proteins_crosslink_positions: [779]
    beta_decoy: False
    crosslink_type: intra
    score: 119.83
    additional_information: None

Here is an example crosslink, you can learn more about the specific attributes and their values here: docs, and here: data types specification.

parser_result = parser.read(
    [
        "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt",
        "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_Crosslinks.xlsx",
    ],
    engine="MS Annika",
    crosslinker="DSS",
)
✓
    Reading MS Annika CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 6911.35it/s]
    Reading MS Annika crosslinks...: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 18167.65it/s]

It’s also possible to read multiple files into one parser_result, this for example makes sense if you have crosslinks and crosslink-spectrum-matches from the same run.

_ = 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: 1.11
    Maximum CSM score: 452.99
    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: 1.11
    Maximum crosslink score: 452.99

If the parser_result contains both crosslinks and CSMs, summary statistics for both will be calculated by transform.summary().

parser_result = parser.read(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt",
    engine="MS Annika",
    crosslinker="DSS",
    parse_modifications=False,
)
✓
    Reading MS Annika CSMs...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 10655.56it/s]

We can also tell the parser to not parse modifications via parse_modifications=False, this might be useful if you don’t care about post-translational-modifications, or if you have unknown modifications in your results that you would have to manually specify - and you don’t want to do that.

In case you want to parse modifications but have unknown modifications in your results, you have to set them via the modifications parameter that can be passed via **kwargs to parser.read_msannika(). More about that later…

sample_csm = parser_result["crosslink-spectrum-matches"][0]
for k, v in sample_csm.items():
    print(f"{k}: {v}")
✓
    data_type: crosslink-spectrum-match
    completeness: partial
    alpha_peptide: GQKNSR
    alpha_modifications: None
    alpha_peptide_crosslink_position: 3
    alpha_proteins: ['Cas9']
    alpha_proteins_crosslink_positions: [779]
    alpha_proteins_peptide_positions: [777]
    alpha_score: 119.83
    alpha_decoy: False
    beta_peptide: GQKNSR
    beta_modifications: None
    beta_peptide_crosslink_position: 3
    beta_proteins: ['Cas9']
    beta_proteins_crosslink_positions: [779]
    beta_proteins_peptide_positions: [777]
    beta_score: 119.83
    beta_decoy: False
    crosslink_type: intra
    score: 119.83
    spectrum_file: XLpeplib_Beveridge_QEx-HFX_DSS_R1.raw
    scan_nr: 2257
    charge: 3
    retention_time: 733.188
    ion_mobility: 0.0
    additional_information: None

Notice that this time the fields alpha_modifications and beta_modifications are empty (None) for our sample CSM.

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, 6478.03it/s]
    Reading MS Annika crosslinks...: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 10331.48it/s]

Last but not least you can also directly read a pdResult from Proteome Discoverer which will automatically read all CSMs and crosslinks detected with MS Annika.

Reading MS Annika Result Files via parser.read_msannika()

parser_result = parser.read_msannika(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt"
)
✓
    Reading MS Annika CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 5919.43it/s]

We can also read any MS Annika result file using the parser.read_msannika() method which allows a more nuanced control over reading MS Annika result files - even though theoretically everything can be done with the parser.read() function as well. You can read the documentation for the parser.read_msannika() method here: docs.

_ = 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: 1.11
    Maximum CSM score: 452.99
from pyXLMS.constants import MODIFICATIONS
 
MODIFICATIONS
✓
    {'Carbamidomethyl': 57.021464,
     'Oxidation': 15.994915,
     'Phospho': 79.966331,
     'Acetyl': 42.010565,
     'BS3': 138.06808,
     'DSS': 138.06808,
     'DSSO': 158.00376,
     'DSBU': 196.08479231,
     'ADH': 138.09054635,
     'DSBSO': 308.03883,
     'PhoX': 209.97181}

By default the MS Annika parser considers all modifications that are in constants.MODIFICATIONS as shown above for pyXLMS version 1.1.1 - a full list of default modifications is given here: docs.

my_mods = dict(MODIFICATIONS)
my_mods["Methyl"] = 14.01565
my_mods
✓
    {'Carbamidomethyl': 57.021464,
     'Oxidation': 15.994915,
     'Phospho': 79.966331,
     'Acetyl': 42.010565,
     'BS3': 138.06808,
     'DSS': 138.06808,
     'DSSO': 158.00376,
     'DSBU': 196.08479231,
     'ADH': 138.09054635,
     'DSBSO': 308.03883,
     'PhoX': 209.97181,
     'Methyl': 14.01565}

If you have any additional modifications in your result file(s) the parser needs to know about them, which is done via the modifications parameter that allows for passing a custom dictionary of modifications. It is usually a good idea to base this custom dictionary on constants.MODIFICATIONS and add your modifications after, as shown above for methylation.

parser_result = parser.read_msannika(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_CSMs.txt",
    modifications=my_mods,
)
✓
    Reading MS Annika CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 826/826 [00:00<00:00, 8056.97it/s]

You can then pass the full list of expected modifications my_mods via the modifications parameter.

_ = 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: 1.11
    Maximum CSM score: 452.99
with open(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_Crosslinks.txt", "r"
) as f:
    parser_result = parser.read_msannika(f, format="txt")
    f.close()
✓
    Reading MS Annika crosslinks...: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 24124.14it/s]

The format parameter is necessary when files are directly read from a file-stream because the format then cannot be automatically be inferred, such as in the case above. Please do note that reading of pdResult files from a file-stream is not supported. The format parameter accepts the following values: "auto", "csv", "txt", "tsv", "xlsx", "pdresult".

_ = transform.summary(parser_result)
✓
    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: 1.11
    Maximum crosslink score: 452.99

There are several other parameters that can be set, you can read more about them here: docs.

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