Storing Mass-Spectrometry Data in Simple Databases Enables Flexible and Intuitive Exploration without Time or Space...

Area of Science:

• Analytical Chemistry
• Bioinformatics
• Data Science

Background:

• Mass spectrometry (MS) generates vast datasets requiring specialized formats and expertise for data extraction.
• Existing MS data formats can be complex, hindering rapid and accessible information retrieval.
• The need for efficient data management solutions in MS research is growing.

Purpose of the Study:

• To evaluate if a structured query language (SQL) database can store raw MS data efficiently.
• To assess the performance of SQL databases for querying MS data compared to traditional formats.
• To develop an intuitive MS data schema for enhanced exploratory analysis.

Main Methods:

• A basic MS schema was designed with intuitive database tables and fields.
• Raw MS data subsets (single scans, ion chromatograms, retention time ranges, fragmentation searches) were queried.
• Implementations using SQLite, DuckDB, and Parquet were compared for read time and disk space.

Main Results:

• The proposed SQL database schema allows for easily readable queries of raw MS data.
• Query performance for common data subsets was found to be under one second, even for gigabyte-sized files.
• The SQL database approach demonstrated competitive or superior performance compared to other popular MS formats.

Conclusions:

• A tidy SQL data schema can effectively store and query diverse MS data types.
• This approach offers transparent data access without significant penalties in computational performance or disk space.
• The schema provides a viable alternative for efficient and interactive analysis of large MS datasets.