Mass Spectrometry Support Workflow Using Screening Lists
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Solution Overview
Problem
Conventional mass spectrometry techniques face challenges in real-time or near-real-time data analysis due to the computational intensity required for processing large batches of data, making it infeasible for immediate processing of thousands or tens of thousands of raw spectrum files.
Innovation Solution
A scientific instrument support apparatus and method that divides raw spectrum files into subsets, processes each subset using a machine learning model to generate spectrum match files, and employs inclusion and exclusion lists to optimize data processing, allowing for real-time or near-real-time data analysis by reducing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques process the entire batch of raw spectrum files multiple times to generate spectrum match files, screening lists, and results, then comprehensive data analysis is achieved, but computational burden increases and real-time processing becomes infeasible
Solution Approach 1:
The patent divides the batch of raw spectrum files into multiple subsets and processes them separately through different analysis pipelines. Some subsets undergo comprehensive multi-pass processing to generate screening lists, while other subsets are processed with pre-loaded screening lists. This segmentation allows the system to achieve both comprehensive analysis and improved computational throughput by parallelizing processing across multiple file subsets.
2Measurement precision
If the entire batch of raw spectrum files is processed multiple times to generate initial spectrum match files, then accurate protein identifications are achieved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing on a first subset of raw spectrum files to generate initial spectrum match files and create screening lists containing entities of interest. These pre-generated screening lists are then loaded and applied during processing of second subsets of files, eliminating the need to re-process entire batches multiple times. This preliminary action approach maintains identification accuracy while significantly reducing total processing time.
3Reliability
If comprehensive processing of all raw spectrum files is performed to generate screening lists and results, then complete entity identification is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts and processes only the most relevant portions of the data batch for generating screening lists. By selecting representative subsets of raw spectrum files for comprehensive processing rather than processing the entire batch, the system extracts sufficient information to create accurate screening lists while consuming significantly fewer computational resources. The extracted entities in the screening lists are then used to guide processing of remaining files.
Data Source
AI summary
Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a scientific instrument support apparatus including memory hardware configured to store instructions and processing hardware configured to execute the instructions. The instructions include loading a batch of raw spectrum files generated by a mass spectrometer, dividing the raw spectrum files into a first subset and a second subset, processing each of the first subset of raw spectrum files with a machine learning model to generate a first subset of spectrum match files, generating a screening list from the first subset of spectrum match files, and processing each of the second subset of raw spectrum files and the screening list with the machine learning model to generate a second subset of spectrum match files.