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DocumentationResult File ReadingReading xiNET/xiVIEW Exported Result Files

Reading xiNET/xiVIEW Exported Result Files

from pyXLMS import __version__
 
print(f"Installed pyXLMS version: {__version__}")
✓
    Installed pyXLMS version: 1.8.1
from pyXLMS import parser
from pyXLMS import transform

All functionality to parse results exported from xiNET  or xiVIEW  is available via the parser submodule. We also import the transform submodule to show some summary statistics of the read files.

Reading xiNET/xiVIEW Files via parser.read()

parser_result = parser.read(
    "../../data/xiview/DDX39B_LCSDA_shared_links_open_clamped.csv",
    engine="xiNET/xiVIEW",
    crosslinker="SDA",
)
✓
    Reading xiNET/xiVIEW CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████| 124/124 [00:00<00:00, 11206.02it/s]

We can read any file exported from xiNET or xiVIEW using the parser.read() method and setting engine="xiNET/xiVIEW". The method also requires us to specify the used crosslinker, in this case SDA was used (crosslinker="SDA"). 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: xiNET/xiVIEW
    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 xiVIEW exported file contained CSMs. We would be able to access those via parser_result["crosslink-spectrum-matches"]. We will do this a bit further down.

_ = transform.summary(parser_result)
✓
    Number of CSMs: 124.0
    Number of unique CSMs: 124.0
    Number of intra CSMs: 106.0
    Number of inter CSMs: 18.0
    Number of target-target CSMs: 53.0
    Number of target-decoy CSMs: 18.0
    Number of decoy-decoy CSMs: 53.0
    Minimum CSM score: 5.416106
    Maximum CSM score: 20.5163

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: partial
    alpha_peptide: MTPVGTASNVKAQAAKEAQHAQLVAVAEDK
    alpha_modifications: None
    alpha_peptide_crosslink_position: 1
    alpha_proteins: ['DECOY_decoy:P11940']
    alpha_proteins_crosslink_positions: [1]
    alpha_proteins_peptide_positions: [1]
    alpha_score: None
    alpha_decoy: True
    beta_peptide: TVAPTAAAAAAARPPGMTQTSTNAR
    beta_modifications: None
    beta_peptide_crosslink_position: 1
    beta_proteins: ['DECOY_decoy:P11940']
    beta_proteins_crosslink_positions: [131]
    beta_proteins_peptide_positions: [131]
    beta_score: None
    beta_decoy: True
    crosslink_type: intra
    score: 20.280395
    spectrum_file: 
    scan_nr: 20
    charge: 6
    retention_time: None
    ion_mobility: None
    additional_information: {'source': {'Id': 20, 'Protein1': 'DECOY_decoy:P11940', 'SeqPos1': 131, 'PepPos1': 131, 'PepSeq1': 'TVAPTAAAAAAARPPGMoxTQTSTNAR', 'LinkPos1': 1, 'Protein2': 'DECOY_decoy:P11940', 'SeqPos2': 1, 'PepPos2': 1, 'PepSeq2': 'MoxTPVGTASNVKAQAAKEAQHAQLVAVAEDK', 'LinkPos2': 1, 'Score': 20.280395, 'PrecursorIntensity': None, 'Charge': 6, 'ExpMz': 946.325674, 'ExpMass': 5671.910385198726, 'CalcMz': 946.324781, 'CalcMass': 5671.905027198725, 'MassError': 0.9446561560267144, 'Missing Peaks': 0, 'Validated': None, 'Search': 45585, 'RawFileName': None, 'PeakListFileName': None, 'ScanNumber': None, 'ScanIndex': None, 'CrossLinkerModMass': 195.125929, 'FragmentTolerance': None, 'IonTypes': '[{"type":"PeptideIon"},{"type":"BIon"},{"type":"YIon"}]', 'Decoy1': 1, 'Decoy2': 1, '3D Distance': None, 'From Chain': None, 'To Chain': None, 'LinkType': 'Self', 'DecoyType': 'DD', 'Retention Time': None}}

Using parser_result["crosslink-spectrum-matches"][0] we can get the first CSM of the file and take a closer look at that.

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

type(parser_result["crosslinks"])
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    NoneType

In this example parser_result["crosslinks"] is None because this file did not contain any crosslinks. This is infered from the available columns in the xiNET/xiVIEW exported file. The parser will check if any spectrum-level information is present in the file and if yes, it will try to parse it as crosslink-spectrum-matches - if no, it will try to parse it as crosslinks. Therefore in this case, no crosslinks can be displayed here.

If you need crosslinks but have crosslink-spectrum-matches, please check on how to aggregate them here: docs.

Reading xiNET/xiVIEW Result Files via parser.read_xinet()

parser_result = parser.read_xinet(
    "../../data/xiview/DDX39B_LCSDA_shared_links_open_clamped.csv"
)
✓
    Reading xiNET/xiVIEW CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████| 124/124 [00:00<00:00, 15044.22it/s]

We can also read any file exported by xiNET or xiVIEW using the parser.read_xinet() method which allows a more nuanced control over reading the files - even though theoretically everything can be done with the parser.read() function as well. You can read the documentation for the parser.read_xinet() method here: docs.

_ = transform.summary(parser_result)
✓
    Number of CSMs: 124.0
    Number of unique CSMs: 124.0
    Number of intra CSMs: 106.0
    Number of inter CSMs: 18.0
    Number of target-target CSMs: 53.0
    Number of target-decoy CSMs: 18.0
    Number of decoy-decoy CSMs: 53.0
    Minimum CSM score: 5.416106
    Maximum CSM score: 20.5163

We get the same result as with the parser.read() method!

Reading xiNET/xiVIEW Result Files via parser.read_xiview()

parser_result = parser.read_xiview(
    "../../data/xiview/DDX39B_LCSDA_shared_links_open_clamped.csv"
)
✓
    Reading xiNET/xiVIEW CSMs...: 100%|█████████████████████████████████████████████████████████████████████████████| 124/124 [00:00<00:00, 11352.29it/s]

We can also read any file exported by xiNET or xiVIEW using the parser.read_xiview() method. This method is just a shorthand function for parser.read_xinet() for convenience, internally both functions execute the same code as the xiNET and xiVIEW exported files share a lot of the same columns.

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