Probabilistic Genomic Data Structures for Rapid Pathogen Identification
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Solution Overview
Problem
Current genomic analysis methods are computationally intensive and require access to powerful processors and extensive storage, limiting rapid and accurate identification of pathogens and antibiotic resistance variants, especially in resource-constrained settings where high-performance computing resources are unavailable.
Innovation Solution
The use of probabilistic data structures, such as Bloom filters, to encode and compress genomic reference data, allowing for efficient storage and comparison of genomic data on consumer-grade computing devices without the need for high-speed network connections or supercomputing resources, enabling rapid identification of unknown genomic material.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact and approximate string matching algorithms are used to compare genomic data, then identification accuracy is improved, but computational resource requirements and time consumption increase significantly
Solution Approach 1:
The patent extracts only the essential identifying features from complete genomic sequences by using k-mer decomposition. Instead of comparing entire genomic sequences, the system extracts and compares specific k-mer substrings that are sufficient for identification, thereby reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent changes the parameter of comparison from complete sequences to k-mer substrings of specific lengths. By transforming the comparison task from full-sequence matching to k-mer frequency comparison, the system achieves faster processing while preserving the ability to accurately identify genomic similarities and differences.
2Reliability
If complete genomic sequences are stored and compared, then comprehensive analysis capability is improved, but storage requirements and data transmission needs increase
Solution Approach 1:
The patent extracts only the necessary k-mer substrings from complete genomic sequences for storage and comparison purposes. By storing only these extracted k-mer features rather than complete sequences, the system reduces storage requirements and data transmission needs while maintaining the ability to perform comprehensive genomic analysis.
Solution Approach 2:
The patent segments complete genomic sequences into smaller k-mer units for storage and comparison. This segmentation allows the system to manage and process genomic data in manageable chunks, reducing the overall storage burden while preserving the analytical capability to reconstruct and compare complete sequence information when needed.
3Measurement precision
If sophisticated computational resources are used for genomic analysis, then analysis accuracy and capability are improved, but cost and accessibility worsen
Solution Approach 1:
The patent replaces expensive, sophisticated computational resources with simpler, more accessible computing systems. By using k-mer based comparison methods, the system can achieve accurate genomic analysis on standard consumer-grade computers or mobile devices, making the technology accessible to a broader range of users including remote clinics and field workers.
Solution Approach 2:
The patent changes the computational parameters from complex full-sequence alignment algorithms to simpler k-mer frequency counting and comparison. This parameter change reduces the computational power required, enabling accurate genomic analysis on inexpensive, widely available devices rather than requiring access to expensive supercomputing resources.
Data Source
AI summary
Techniques for identifying and/or classifying genomic information are provided. In some embodiments, genomic information may be identified by computing systems without access to a database of reference genomic information, instead relying on locally stored probabilistic data structures representing reference genomic information. Query genomic data, such as data taken from a read-set, may be divided into sub-strings, and each of the locally-stored probabilistic data structures may be queried by each of the extracted sub-strings, generating probabilistic outputs indicating either that (a) the sub-string is probably included in the set of data represented by the probabilistic data structure or (b) the sub-string is definitely not included in the set of data. Based on the number and/or proportion of sub-strings from a read-set that are indicated as being likely represented by a probabilistic data structure, a likely identity or classification for the genomic information in the read-set may be determined.