Aird 2.0: High-performance mass spectrometry data format optimized for computational analysis

Background: The escalating volume of data generated by mass spectrometry (MS) presents a significant bottleneck for large-scale computational analysis. While data compression is crucial, the primary challenge lies in accelerating data access for processing-intensive tasks. To address this, we introduce Aird 2.0, a high-performance data format engineered for rapid data retrieval and efficient storage. Results: Aird 2.0 features a novel, dynamic compression framework that intelligently selects from six specialized algorithms to optimize performance for any given MS file. Our comprehensive evaluation on 58 public datasets demonstrates that Aird 2.0 achieves a 2.8-fold increase in random-access speed and a 6-fold increase in full-file loading speed compared to the standard mzML format, while also reducing file sizes by an average of 12.3% over its predecessor, Aird 1.0. Additionally, it enhances metadata deserialization speed by 2.5-fold and halves metadata file size, further accelerating analysis workflows. Significance: Supported by a comprehensive conversion client (AirdPro) and SDKs for Java and C#. Aird 2.0 is a comprehensive, open-source solution for accelerating the analysis of large-scale Mass Spectrometry data. It provides a high-performance data format that can be seamlessly integrated into existing software ecosystems, offering a robust foundation for future research and development. • Aird 2.0: a high-performance MS data format for fast computational analysis. • Introduces adaptive combinatorial compression for speed and efficiency. • Achieves 6×faster loading and 2.7× faster access vs mzML. • Reduces metadata load time by 2.5× and improves compression ratio. • Open SDKs and tools enable broad integration in omics workflows.

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Publication Details

Journal
Results in Chemistry
Published
2026-01-20
DOI
https://doi.org/10.1016/j.rechem.2026.103083
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
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article

Aird 2.0: High-performance mass spectrometry data format optimized for computational analysis

Changbin Yu, Shuochao Li, Miaoshan Lu, Junjie Tong et al.
Results in Chemistry
Scientific Computing and Data Management
article

Aird 2.0: High-performance mass spectrometry data format optimized for computational analysis

Changbin Yu, Shuochao Li, Miaoshan Lu, Junjie Tong, Xixi Wang
article en

Abstract

Background: The escalating volume of data generated by mass spectrometry (MS) presents a significant bottleneck for large-scale computational analysis. While data compression is crucial, the primary challenge lies in accelerating data access for processing-intensive tasks. To address this, we introduce Aird 2.0, a high-performance data format engineered for rapid data retrieval and efficient storage. Results: Aird 2.0 features a novel, dynamic compression framework that intelligently selects from six specialized algorithms to optimize performance for any given MS file. Our comprehensive evaluation on 58 public datasets demonstrates that Aird 2.0 achieves a 2.8-fold increase in random-access speed and a 6-fold increase in full-file loading speed compared to the standard mzML format, while also reducing file sizes by an average of 12.3% over its predecessor, Aird 1.0. Additionally, it enhances metadata deserialization speed by 2.5-fold and halves metadata file size, further accelerating analysis workflows. Significance: Supported by a comprehensive conversion client (AirdPro) and SDKs for Java and C#. Aird 2.0 is a comprehensive, open-source solution for accelerating the analysis of large-scale Mass Spectrometry data. It provides a high-performance data format that can be seamlessly integrated into existing software ecosystems, offering a robust foundation for future research and development. • Aird 2.0: a high-performance MS data format for fast computational analysis. • Introduces adaptive combinatorial compression for speed and efficiency. • Achieves 6×faster loading and 2.7× faster access vs mzML. • Reduces metadata load time by 2.5× and improves compression ratio. • Open SDKs and tools enable broad integration in omics workflows.

Results in ChemistryVol. 21
Hainan Normal University (CN), Jinan Central Hospital (CN), Shandong Provincial Hospital (CN), Shandong First Medical University (CN), Xi’an Jiaotong-Liverpool University (CN)
Openalex Percentile: Top 83%
Scientific Computing and Data Management
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