DIAT Tensor Mass Spectrometry Data Structure
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Solution Overview
Problem
Current mass spectrometry data structures, such as mzXML and mzML formats, suffer from low storage efficiency and high computational requirements for analyzing large-scale data independent acquisition (DIA) mass spectrometry data, leading to data distortion and inefficiencies in data processing and analysis.
Innovation Solution
The proposed solution is a biomolecular omics mass spectrometry data structure based on data independent acquisition mass spectra, specifically the DIAT (Data-Independent Acquisition Tensor) format, which transforms original data into a three-dimensional tensor structure, allowing for efficient storage and analysis by converting original mass spectrometry data into a DIAT format that includes cycle index, fragment ion mass-to-charge ratio, and precursor ion window index, and further reduces data size through pooling and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If XML-based file formats (mzXML, mzML) are used to store mass spectrometry data, then the data can be converted into readable languages, but the file size increases significantly and I/O rates decrease
Solution Approach 1:
The patent changes the data storage format from XML (text-based) to HDF5 (binary-based), fundamentally altering the parameter of data representation. This transformation maintains data readability through structured organization while dramatically reducing file size by efficiently encoding numerical data in binary format, directly resolving the contradiction between readability and file size.
Solution Approach 2:
The patent replaces the text-based XML mechanical structure with a binary HDF5 data structure optimized for numerical mass spectrometry data. This substitution eliminates the overhead of text encoding and parsing, reducing file size while maintaining efficient data access through the HDF5 hierarchical structure, thus resolving the file size issue without sacrificing readability.
2Ease of operation
If XML-based file formats are used for mass spectrometry data, then data can be stored in readable language, but non-sequential reading is required for DIA data analysis resulting in low I/O rates
Solution Approach 1:
The patent introduces a new dimensional organization in HDF5 that allows multi-dimensional data access patterns. The hierarchical structure enables direct access to specific data dimensions (e.g., specific m/z ranges or time points) without sequential reading, transforming the data access model from linear (XML) to multi-dimensional (HDF5), thereby dramatically improving I/O rates for DIA analysis.
Solution Approach 2:
The patent segments mass spectrometry data into distinct HDF5 datasets organized by experimental dimensions (e.g., separate datasets for MS1, MS2, precursor ions, fragment ions). This segmentation allows selective reading of only the required data portions during analysis, eliminating the need to read entire files sequentially and significantly improving I/O efficiency for DIA data processing.
3Quantity of substance
If Mz5 format is used for efficient data storage, then storage efficiency improves, but the ontology of mzML file content is maintained which is not suitable for DIA data analysis
Solution Approach 1:
The patent creates a dynamic and flexible HDF5 data structure that adapts to DIA analysis requirements. Unlike the static mzML ontology, the HDF5 structure allows dynamic organization of data by experimental parameters (e.g., configurable window sizes, variable cycle structures), enabling the same storage format to efficiently handle different DIA experimental designs and analysis workflows.
Solution Approach 2:
The patent performs preliminary organization of DIA data during the storage phase by pre-grouping fragment ions by precursor ion windows and pre-calculating data groupings. This preliminary structuring eliminates the need for complex post-storage data reorganization and XIC extraction, making the data immediately ready for efficient DIA analysis while maintaining compact storage.
4Measurement precision
If prior information of targeted molecules is obtained in DDA mode to perform XIC extraction for DIA analysis, then ion chromatograph information can be inferred, but computing resources and time are consumed and data distortion occurs
Solution Approach 1:
The patent performs preliminary organization of DIA data by precursor ion windows during the data acquisition and storage phase. Fragment ions are pre-grouped with their corresponding precursor ions in the HDF5 structure, eliminating the need for time-consuming post-acquisition XIC extraction and peak grouping. This preliminary structuring provides accurate ion chromatograph information directly from the stored data without additional computational overhead.
Data Source
AI summary
The present invention relates to the technical field of biomolecular omics mass spectrometry data, in particular to an implementation method of a molecular omics data structure based on data independent acquisition mass spectra. The mass spectrometry data structure is DIAT (Data-Independent Acquisition Tensor) data generated from original mass spectrometry data and has attributes of three dimensions, the first dimension is a cycle index, the second dimension is a fragment ion mass-to-charge ratio, and the third dimension is a precursor ion window index corresponding to a fragment ion. The DIAT data of this solution is high in integrity, convenient to read and high in reading speed, and the size of a DIAT file is only a few tenths of that of an mzXML file. DIA mass spectrometry data can be directly observed through a visualized pooled DIAT file image, and a DIAT can be analyzed by directly using a visual processing algorithm, which avoids the operation of extracting ion chromatographic with a large amount of calculation and can directly establish a computer deep learning model for clinical phenotype classification and prediction according to the file.