Mass Spectrometry Data Processing for Adaptive Compound Identification
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Solution Overview
Problem
Biological mass spectrometry data evaluation faces challenges due to large data sizes and fragmentation of data evaluation tools, with existing systems being inflexible and inefficient in processing and analyzing data from mass spectrometry experiments, particularly in identifying study variables and their influence on biological systems.
Innovation Solution
A data processing device and method that utilizes a processor unit to process and group initial data vectors from mass spectrometers, integrating additional data properties to facilitate flexible data analysis, visualization, and adaptive processing workflows, enabling efficient identification of compounds and proteins while allowing for dynamic data type definitions and connections within a relational database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional mass spectrometry data processing methods are used, then data can be processed, but processing time is excessive and system flexibility is limited
Solution Approach 1:
The patent segments the monolithic data processing workflow into distinct, independent modules including data import, peak detection, spectral matching, protein identification, and result visualization. Each module processes specific aspects of mass spectrometry data independently, allowing parallel execution and eliminating sequential bottlenecks. This modular architecture enables the system to process different data streams simultaneously, dramatically improving processing throughput while reducing total analysis time.
Solution Approach 2:
The system implements dynamic processing workflows that adapt to the specific characteristics of input data. The processor automatically adjusts processing parameters, selects appropriate analysis algorithms, and modifies workflow steps based on data quality, mass spectrometer type, and experimental conditions. This dynamic adaptation eliminates unnecessary processing steps and optimizes computational resources, significantly reducing processing time while maintaining accuracy.
2Adaptability or versatility
If rigid software packages are used, then processing workflows are standardized, but adaptability to new experimental designs is poor
Solution Approach 1:
The patent creates a universal data processing platform that handles multiple mass spectrometry techniques (LC-MS, GC-MS, MALDI-TOF, ESI-MS) and various experimental designs through a single integrated system. The modular architecture with standardized interfaces allows the same core processing engine to accommodate different data types, instruments, and analysis requirements without requiring separate specialized software packages, thereby achieving high versatility.
Solution Approach 2:
The system employs dynamic workflow configuration where processing steps and parameters can be adjusted in real-time based on experimental requirements. Users can dynamically add, remove, or modify processing modules without reprogramming the entire system. The software automatically adapts processing parameters to match specific experimental conditions, enabling flexible adaptation to new experimental designs while maintaining system manageability.
3Measurement precision
If comprehensive data analysis is performed, then identification accuracy improves, but computational resource requirements increase
Solution Approach 1:
The patent divides comprehensive data analysis into hierarchical processing stages: initial rapid filtering of low-quality spectra, intermediate detailed analysis of promising candidates, and final verification only for high-priority identifications. This segmented approach ensures that computationally intensive algorithms are applied only where necessary, maintaining high identification accuracy for critical compounds while minimizing overall computational resource consumption across the entire dataset.
Solution Approach 2:
The system implements selective comprehensive analysis where full analytical depth is applied only to data regions and compounds that meet specific quality criteria, while other regions receive streamlined processing. This partial application of comprehensive analysis maintains high accuracy for the most important identifications while reducing total computational load by avoiding unnecessary exhaustive analysis of all data points.
Data Source
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AI summary
A data processing device according to the invention comprises a processor unit adapted to process a plurality of initial data vectors provided by a chromatograph and/or a mass spectrometer, the processing being carried out in one, two or more processing steps producing items of processed data, and a storage unit adapted to save and retrieve initial data vectors and/or items of processed data, in particular processed data vectors or identified compounds, and/or items of additional data, in particular properties of the sample introduced in the mass spectrometer. Each item of processed data and/or additional data is connected to at least one initial data vector, and wherein the processor unit is adapted to group, select and/or modify initial data vectors and/or items of processed data according to one or more items of additional data. The invention further pertains to a mass spectrometry setup and a method for identifying and/or quantifying peptides and/or proteins and/or metabolites.