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Updated: Oct 6, 2025

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time.

Miaoshan Lu1,2,3,4, Shaowei An5,6,7,4, Ruimin Wang2,3,7,4

  • 1Zhejiang University, Hangzhou, 310009, Zhejiang Province, China.

BMC Bioinformatics
|January 13, 2022
PubMed
Summary
This summary is machine-generated.

Aird is a new, open-source data format for mass spectrometry (MS) that offers high compression rates and fast decoding speeds. This computational format significantly reduces MS file sizes, improving data processing efficiency.

Keywords:
AirdCompressorDDADIAMass spectrometryMetabolomicsProteomicsZDPD

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Area of Science:

  • Mass Spectrometry Data Analysis
  • Computational Chemistry
  • Bioinformatics

Background:

  • Mass spectrometry (MS) generates large data files that increase with higher precision.
  • Existing formats like mzML have limitations in compression and decoding speed for computational tasks.
  • Data precision in MS is linked to instrument capabilities and downstream algorithms.

Purpose of the Study:

  • Introduce Aird, a novel, open-source, computation-oriented format for mass spectrometry data.
  • Address the need for efficient data compression and rapid decoding in MS data analysis.
  • Provide a scalable and customizable solution for evolving MS data characteristics.

Main Methods:

  • Developed Aird with controllable precision and flexible indexing.
  • Introduced a new compressor, Zlib-Diff-PforDelta (ZDPD), for m/z data.
  • Utilized single instruction multiple data (SIMD) technology for high-speed encoding/decoding.
  • Stored metadata using JavaScript Object Notation (JSON).

Main Results:

  • Aird achieved an average of 55% reduction in m/z data size compared to Zlib compression alone.
  • Aird demonstrated significantly faster decoding times, taking only 33% of the time compared to Zlib.
  • Evaluated Aird using seven diverse datasets from ProteomeXchange and Metabolights across different instrument vendors (SCIEX, Thermo, Agilent).
  • Developed Aird-SDK (Java) and AirdPro GUI client (C#) for accessibility.

Conclusions:

  • Aird offers high scalability, compression rates, and fast decoding, making it suitable for computational tasks.
  • The ZDPD compressor leverages MS digital features and can be adapted for other formats like mzML.
  • The evolving nature of MS data necessitates specialized and customized storage solutions like Aird.