Anchor-Based Data Structures for Rapid Microbial Identification
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Solution Overview
Problem
Current methods for microbial identification, such as culturing and serology, face challenges like slow processing times, inability to detect non-culturable pathogens, and issues with specificity and sensitivity, particularly in identifying multiple microorganisms in mixed samples.
Innovation Solution
The use of anchor-based data structures and probabilistic methods to compare unassembled nucleotide fragment reads with reference genomic databases and trait-specific database catalogs, allowing for rapid identification of microorganisms at the species, sub-species, and strain levels without the need for assembly of full microbial sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional culturing methods are used for microbial identification, then the identification process can be performed with simple equipment, but the processing time is extended to several days
Solution Approach 1:
The patent replaces the mechanical culturing process with a sequencing-based detection system that uses nucleic acid amplification and sequencing technologies. This substitution enables rapid microbial identification within hours rather than days, directly resolving the time complexity contradiction while maintaining operational simplicity through automated workflows.
Solution Approach 2:
The patent performs preliminary nucleic acid extraction and amplification before final detection and identification. By preparing the sample in advance through these preliminary steps, the system enables rapid subsequent analysis, thereby reducing the overall identification time without requiring complex real-time processing equipment.
2Ease of manufacture
If serological tests are used for microbial detection, then the method is widely utilized and commercially available, but the specificity and sensitivity are compromised in mixed samples
Solution Approach 1:
The patent segments the detection process into multiple independent steps: nucleic acid extraction, amplification, sequencing, and bioinformatic analysis. This segmentation allows each step to be optimized independently, achieving high specificity and sensitivity in mixed samples while maintaining commercial viability through modular, scalable workflows.
Solution Approach 2:
The patent changes the detection parameter from antibody-antigen interaction (serology) to nucleic acid sequence matching. This parameter change enables precise discrimination between closely related microorganisms in mixed samples, significantly improving measurement precision while the standardized protocol maintains ease of manufacture and commercial availability.
3Loss of information
If assembly of full microbial sequences is performed for identification, then comprehensive genomic information is obtained, but extensive computational resources are required
Solution Approach 1:
The patent extracts only the essential diagnostic information from microbial genomes by targeting specific marker genes and conserved regions for sequencing. This extraction approach provides sufficient genomic information for accurate identification while dramatically reducing the computational burden compared to whole-genome assembly, directly resolving the contradiction between information completeness and resource consumption.
Solution Approach 2:
The patent performs partial sequencing of specific genomic regions rather than complete whole-genome sequencing. This partial action provides adequate information for microbial identification purposes while avoiding the excessive computational resources required for full genome assembly, achieving an optimal balance between information quality and resource efficiency.
Data Source
AI summary
In some embodiments, sample-derived characteristic determination may be facilitated via creation or use of anchor-based data structures. In some embodiments, an anchor and a seed length range may be obtained (e.g., for creating a reference data structure derived from reference data). Based on the anchor and the seed length range, reference seeds may be extracted from the reference data (e.g., such that each of the extracted reference seeds (i) is a data instance adjacent at least one instance of the anchor in the reference data and (ii) has a length within the seed length range). The reference data structure may be created with the extracted reference seeds, and unassembled sample data may be processed using the reference data structure, the anchor, and the seed length range to determine characteristics related to the unassembled sample data.