Genomic Pathogen Analysis via Confidence Scoring
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Solution Overview
Problem
Current bioinformatic methods for high-throughput sequencing data analysis are bottlenecks in accurately and efficiently identifying taxonomic composition and predicting disease outbreaks, particularly due to the complexity of genomic data and the need for improved surveillance and diagnostic tools.
Innovation Solution
A method involving the alignment of metagenomic datasets with clinically relevant proteomes, filtering for high identity and length alignments, scoring for confidence, and predicting pathological agents using a confidence threshold, along with K-means clustering for taxonomic composition prediction, which includes steps for data preprocessing, alignment, filtering, scoring, and iterative processing to achieve accurate and reliable disease outbreak prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high throughput sequencing is used to analyze genomic data, then the amount of data recovered increases, but the complexity of analysis methods required increases
Solution Approach 1:
The patent segments the complex bioinformatic analysis into distinct functional modules: quality filtering, alignment to reference databases, taxonomic classification, and outbreak prediction. Each module handles a specific aspect of the analysis, making the overall system more manageable and efficient despite processing large datasets
Solution Approach 2:
The patent introduces intermediary databases containing reference genomic sequences and taxonomic information that mediate between the raw sequencing data and the final analysis results. These intermediary structures enable efficient comparison and classification without requiring direct complex analysis of all raw data
2Reliability
If current bioinformatic methods are used for disease outbreak prediction, then analysis can be performed, but accuracy and efficiency are insufficient
Solution Approach 1:
The patent performs preliminary actions by pre-processing sequencing data through quality filtering and pre-aligning to reference databases before actual outbreak prediction. This preliminary processing improves both accuracy of predictions and efficiency by reducing the computational burden during the prediction phase
Solution Approach 2:
The patent implements feedback mechanisms where analysis results are continuously refined by comparing predicted outbreaks with actual outbreak data, adjusting parameters and improving the accuracy of future predictions while maintaining efficient processing
Data Source
AI summary
The present invention is directed towards a bioinformatic method for screening complex patient-derived samples for predicting and identifying at least one pathological agent, especially human pathological agents, comprising establishing agent confidence value from a set of collected patient-derived samples.