MiFi Bioinformatic Tool for Pathogen Detection via E-Probe Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current pathogen detection techniques, particularly Next Generation Sequencing (NGS), face challenges with time-consuming and labor-intensive data analysis, requiring specialized bioinformatics knowledge and high-performance computing, limiting their throughput and accessibility for rapid pathogen identification in plants and animals.
Innovation Solution
The MiFi platform provides a user-friendly online bioinformatic tool using short curated electronic probes (e-probes) to detect and identify pathogens from raw NGS datasets, ignoring irrelevant sequences, enabling simultaneous screening for multiple pathogens and reducing the need for extensive laboratory tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NGS data analysis methods are used, then pathogen detection accuracy is maintained, but analysis time and computational resource requirements increase significantly
Solution Approach 1:
The patent extracts and isolates only the pathogen-specific relevant sequences from the entire NGS dataset using targeted bioinformatic filtering. By removing host genome sequences, microbial community sequences, and other irrelevant data, the system retains only pathogen-containing reads for analysis. This extraction approach maintains detection accuracy while dramatically reducing the data volume requiring computational analysis, thereby resolving the contradiction between accuracy and analysis time.
Solution Approach 2:
The patent segments the pathogen detection process into distinct computational stages: initial quality filtering, host sequence removal, pathogen-specific sequence identification, and final pathogen calling. By dividing the analysis workflow into modular segments that can be processed independently and in parallel, the system maintains comprehensive pathogen detection capability while reducing overall analysis time through efficient resource utilization.
2Adaptability or versatility
If comprehensive pathogen screening is performed, then detection coverage is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing pathogen-specific k-mer signatures and sequence characteristics in databases before actual sample analysis. These pre-prepared reference data structures enable rapid comparison and identification during runtime without requiring complex real-time computations. This preliminary preparation allows comprehensive multi-pathogen screening capability while keeping the actual analysis computationally simple and efficient.
3Reliability
If extensive laboratory tests are conducted, then pathogen identification reliability is improved, but cost and resource consumption increase
Solution Approach 1:
The patent creates in-silico copies of pathogen genomes and sequences that can be repeatedly analyzed computationally without consuming additional physical laboratory resources. Instead of performing multiple duplicate wet-lab tests, the system uses digital sequence replication and virtual PCR amplification through bioinformatic algorithms. These computational copies provide reliable pathogen identification through repeated analysis while eliminating the need for proportional increases in physical reagents, samples, and laboratory consumables.
Data Source
AI summary
Systems and methods for interactive pathogen detection are described including receiving at least one target genome file and at least one near-neighbor genome file and analyzing the target genome file and the near-neighbor genome file to generate a plurality of raw e-probes unique to a target pathogen. Each raw e-probe includes a unique nucleic acid signature sequence selected from along a length of the pathogen genome of the target pathogen. The plurality of raw e-probes are curated to provide a curated e-probe set. The curated e-probe set can be in silico validated and/or in vitro validated. The resulting e-probe set can be used to determine presence of the target pathogen in a sample metagenome in an e-probe diagnostic system.