Supported file formats¶

MS-MINT is designed to support a variety of mass spectrometry (MS) data formats by converting them into a standardized tabular format for downstream analysis. Here's a breakdown of the file types you can use with MS-MINT, based on your code:

Supported File Formats in MS-MINT¶

Format	Extension	Description	Read By Function
mzXML	`.mzxml`	An older open XML-based format for MS data.	`mzxml_to_df()`
mzML	`.mzml`	Widely used open standard for MS data (XML-based).	`mzml_to_df()`
mzMLb	`.mzmlb`	A binary-efficient variant of mzML (faster, smaller).	`mzmlb_to_df__pyteomics()`
HDF5	`.hdf`, `.h5`	Hierarchical format often used for storing large numerical datasets.	`pd.read_hdf()`
Feather	`.feather`	Fast, lightweight binary format for DataFrames (used with Arrow).	`pd.read_feather()`
Parquet	`.parquet`	Columnar data format for fast read and compression.	`pd.read_parquet()`

How It Works¶

The function ms_file_to_df() acts as a universal file loader. It:

Detects the file extension (e.g., .mzML, .hdf, etc.)
Dispatches to the appropriate reader (e.g., mzml_to_df, read_parquet)
Normalizes the schema to include the following standard columns:

["scan_id", "ms_level", "polarity", "scan_time", "mz", "intensity"]

These columns are crucial for MS-MINT processing and analysis.

Special Cases and Notes¶

Time Unit Handling¶

mzXML and mzML files may report scan times in minutes, but MS-MINT normalizes this to seconds.

Thermo RAW Parquet Files¶

If you load a .parquet file not already in MS-MINT format, MS-MINT attempts to reformat it using format_thermo_raw_file_reader_parquet().

mzMLb Support¶

Only works if the optional dependency pyteomics.mzmlb is available.