Exporting Crosslinks to XMAS


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

✓


    Installed pyXLMS version: 1.5.3


from pyXLMS import parser
from pyXLMS import exporter

All exporting functionality is available via the exporter submodule. We also import the parser submodule to read crosslink-spectrum-matches and crosslinks.


parser_result = parser.read(
    "../../data/ms_annika/XLpeplib_Beveridge_QEx-HFX_DSS_R1_Crosslinks.xlsx",
    engine="MS Annika",
    crosslinker="DSS",
)
xls = parser_result["crosslinks"]

✓


    Reading MS Annika crosslinks...: 100%|██████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 17341.39it/s]

We read crosslinks using the generic parser from a single MS Annika .xlsx file. For easier access we also assign our crosslinks to the variable xls.


from pyXLMS.transform import validate, targets_only
 
xls = targets_only(validate(xls))

✓


    Iterating over scores for FDR calculation...:  25%|██████████████████                                                       | 74/300 [00:00<?, ?it/s]

Important

For down-stream analysis with XMAS you would usually want to only consider high-confidence target crosslinks, either by pre-filtering in the search engine of your choice or via the pyXLMS functions validate() [docs ] and targets_only() [docs ]!


_df = exporter.to_xmas(xls, filename=None)

The function exporter.to_xmas() exports a list of crosslinks to XMAS format for visualization in ChimeraX . The tool XMAS is accessible via the link github.com/ScheltemaLab/ChimeraX_XMAS_bundle or directly via the ChimeraX Toolshed . You can read more about the to_xmas() function and all its parameters here: docs.

Tip

Specifying filename=None will only return the pandas DataFrame and not write it to disk!