Targeted Isotope Clustering for Faster Mass Spectrum Matching
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Solution Overview
Problem
Conventional isotope matching techniques in laboratory analytical instruments are time-consuming and resource-intensive, often leading to errors due to the vast amount of data that needs to be processed and the compounding of mistakes early in the analysis process, which results in lower accuracy and inefficiency.
Innovation Solution
The proposed method involves a computer-implemented process that uses prior knowledge of the sample's composition to identify expected patterns in the data, matching predicted isotope clusters to the raw data, and employing a two-stage processing approach to build charge clusters and flag ambiguous peaks, thereby reducing computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional isotope matching techniques process all raw data without prior knowledge filtering, then comprehensive data analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by using prior knowledge of the sample's composition to predict expected isotope clusters and fragmentation patterns before processing the raw mass spectrum data. This pre-computation of expected patterns allows the system to filter and focus only on relevant data regions, significantly reducing processing time while maintaining identification accuracy.
Solution Approach 2:
The invention extracts only the relevant portions of the raw data that match the predicted isotope clusters based on prior knowledge. By taking out and focusing on specific m/z regions and fragmentation patterns that are expected to be present, the system avoids processing the entire dataset, thereby reducing computational resources and processing time without sacrificing comprehensive analysis.
2Reliability
If conventional techniques process vast amounts of data without targeted filtering, then all potential isotopes are considered, but computational resources and processing time increase
Solution Approach 1:
The system uses prior knowledge to pre-calculate expected isotope clusters and their characteristic patterns before analyzing the raw data. This preliminary computation creates a targeted search framework that guides the processing algorithm to focus only on relevant data regions, reducing computational resource consumption while maintaining reliable identification of all potential isotopes.
Solution Approach 2:
The invention applies local quality by treating different regions of the mass spectrum with different processing intensities. Regions that match predicted isotope clusters based on prior knowledge receive focused, detailed analysis, while other regions are either skipped or processed with reduced intensity. This selective processing approach maintains identification reliability for relevant isotopes while reducing overall computational resource usage.
3Measurement precision
If conventional methods analyze data without prior knowledge guidance, then all fragmentation patterns are examined, but processing efficiency decreases
Solution Approach 1:
The system performs preliminary action by using prior knowledge of the sample composition to predict which fragmentation patterns are expected to be present in the mass spectrum. This pre-computation creates a targeted list of expected fragments and their corresponding m/z values, allowing the processing algorithm to efficiently match only these predicted patterns against the raw data, thereby maintaining high matching accuracy while improving processing efficiency.
4Reliability
If conventional techniques process data in a single pass without staged analysis, then complete data evaluation is achieved, but error propagation increases
Solution Approach 1:
The invention segments the data processing into multiple stages: first, predicting expected isotope clusters and fragmentation patterns based on prior knowledge; second, matching these predicted patterns against the raw data; and third, refining the matches by comparing observed peaks with expected patterns. This segmented, multi-stage approach reduces error propagation by systematically validating results at each stage while reducing processing time through focused analysis at each step.
Data Source
AI summary
Exemplary embodiments provide computer-implemented methods, mediums, and apparatuses configured to perform targeted isotope clustering. A mass spectrum for a sample may be obtained from an analytical laboratory instrument, and a set of peaks within the mass spectrum may be identified. A list of fragments expected to be potentially present in the sample may be obtained, and a set of predicted peaks may be generated from the list. The spectrum may be searched for the predicted peaks to determine if any combination of the peaks present in the spectrum match the expected fragment patterns. Accordingly, isotope (charge) clusters may be built in a targeted way using a priori knowledge to target the matches. As a result, spectrum analysis can be done more quickly and efficiently than in conventional systems that use neutral or untargeted matching, and the matches can be made more accurately.