Automated Metadata Extraction from mzML Files with RunAssessor

Area of Science:

• Proteomics
• Bioinformatics
• Data Science

Background:

• Proteomics data reusability is hindered by insufficient metadata in public repositories.
• Lack of reliable annotation limits large-scale reanalysis of proteomics datasets.
• Manual metadata curation is time-consuming and prone to errors.

Purpose of the Study:

• To develop a tool, RunAssessor, for systematic extraction of metadata from mass spectrometry data files.
• To improve the reliability and accessibility of proteomics metadata for data reprocessing.
• To facilitate the reuse of public proteomics data through automated annotation.

Main Methods:

• RunAssessor systematically extracts sample preparation and instrument acquisition parameters directly from mass spectrometry data files.
• The tool analyzes data files prior to peptide identification.
• Metadata extraction was demonstrated using one complete dataset and test files from 18 additional datasets.

Main Results:

• RunAssessor successfully extracted key metadata including instrument models, isobaric labels, phosphoenrichment, ion tolerances, and dynamic exclusion times.
• Extracted metadata were stored in a comprehensive output file and summarized in a standard Sample and Data Relationship Format (SDRF) file.
• The tool demonstrated its ability to reduce manual curation burden and enhance metadata reliability.

Conclusions:

• RunAssessor effectively automates the extraction and summarization of essential proteomics metadata.
• The tool significantly improves the quality and reliability of metadata, facilitating data reprocessing and reuse.
• Automated metadata extraction by RunAssessor addresses a critical bottleneck in large-scale proteomics research.