Microbial Identification Database Filtering Low-Quality Genome Data
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Solution Overview
Problem
Existing microbial identification methods using mass spectrometry face challenges due to low-quality genome data affecting the accuracy of predicted mass-to-charge ratio databases, leading to false peaks and reduced identification accuracy.
Innovation Solution
Construct a mass-to-charge ratio database using high-quality genome data that satisfies specific criteria, predicting proteins likely to be expressed and assigning weights to reduce false peaks, thereby enhancing database quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a genome database is used to construct a mass-to-charge ratio database for microbial identification, then the method can overcome the shortcoming of fingerprinting methods being affected by medium conditions and measurement reproduction, but the quality of the database may be affected by low-quality genome data included in the genome database
Solution Approach 1:
The patent applies preliminary action by evaluating genome data quality before constructing the mass-to-charge ratio database. The system assesses genome data against predetermined criteria (completeness, contamination level, assembly quality) and selectively includes only high-quality genomes in the database construction process. This preliminary filtering ensures that low-quality genome data does not compromise the overall database quality, while still benefiting from the broader coverage of using a genome database approach.
2Adaptability or versatility
If all genome data from a genome database is used to construct a mass-to-charge ratio database, then the database coverage is maximized, but low-quality genome data introduces false peaks and reduces identification accuracy
Solution Approach 1:
The patent applies local quality by implementing quality-specific processing for different genome data entries. Instead of treating all genome data uniformly, the system evaluates each genome individually against quality criteria and applies selective inclusion or exclusion. High-quality genomes are fully incorporated into the mass-to-charge ratio database, while low-quality genomes are excluded or downweighted. This local quality assessment maintains high database coverage from diverse genomes while ensuring that only reliable data contributes to peak accuracy.
Data Source
AI summary
A method of constructing a microbial identification database, the method comprising: (ST02) acquiring genome data for microorganisms from a genome database; (ST06) determining whether a criterion is satisfied by the genome data thus acquired; (ST16) for respective sets of the genome data that were determined that they satisfied the criterion, predicting proteins to be expressed; and (ST20A, 20C) constructing a mass-to-charge ratio database including mass-to-charge ratio lists, the mass-to-charge ratio lists being predicted for the respective sets of the genome data based on the proteins thus predicted.