NGS Pathogen Detection with Staged Alignment and False-Positive Filtering
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Solution Overview
Problem
Traditional diagnostic methods for infectious diseases, such as DNA sequencing, face challenges with long processing times and high false-positive rates due to the vast amount of data and misannotations in reference genome databases, making it difficult to accurately identify pathogens in clinical settings.
Innovation Solution
The use of alignment algorithms like SNAP and RAPSearch, combined with taxonomic classification and filtering techniques, to quickly and accurately align sequence reads to classified reference genomes, removing false positives and misannotations, and providing clinically relevant results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional DNA sequencing and alignment methods are used to identify pathogens, then comprehensive pathogen detection is achieved, but processing time becomes excessively long
Solution Approach 1:
The alignment process is segmented into multiple stages: initial rapid alignment using SNAP algorithm, followed by selective re-alignment using RAPSearch for candidate pathogens. This segmentation allows the system to process the vast amount of sequencing data efficiently while maintaining detection accuracy through multi-stage verification.
Solution Approach 2:
The system performs preliminary alignment actions using the SNAP algorithm to quickly identify potential pathogen matches before applying more computationally intensive verification methods. This preliminary action filters out obvious non-matches and focuses computational resources on promising candidates, significantly reducing overall processing time while maintaining reliability.
2Adaptability or versatility
If comprehensive sequence alignment is performed to identify all potential pathogens, then detection coverage is improved, but the number of false positives increases
Solution Approach 1:
The system applies different alignment strategies to different parts of the sequence data based on local characteristics. High-stringency alignment is applied to regions with high similarity to known pathogens, while more permissive alignment is used for divergent sequences. This local quality approach maintains comprehensive detection coverage while reducing false positives in specific genomic regions.
Solution Approach 2:
The system incorporates feedback mechanisms where alignment results are continuously evaluated and used to adjust subsequent search parameters. When sequences align to multiple reference genomes with conflicting taxonomic classifications, the system uses feedback to resolve ambiguities and eliminate false positives through iterative refinement of alignment criteria.
3Measurement precision
If manual interpretation of alignment results is performed by experts, then result accuracy is improved, but productivity decreases
Solution Approach 1:
The alignment system performs self-service through automated taxonomic classification and false positive filtering using integrated algorithms. The system automatically interprets alignment results, classifies pathogens by taxonomy, and filters spurious matches without requiring manual intervention, thereby maintaining high accuracy while dramatically increasing productivity and analysis throughput.
Solution Approach 2:
The system replaces the mechanical process of manual expert interpretation with automated computational algorithms for taxonomic classification and result validation. This substitution maintains measurement precision through sophisticated algorithms while eliminating the bottleneck of manual review, enabling high-volume processing of clinical samples.
Data Source
AI summary
Embodiments are directed to systems and methods for pathogen detection using next-generation sequencing (NGS) analysis of a sample. Embodiments may apply alignment algorithms (e.g., SNAP and/or RAPSearch alignment algorithms) to align individual sequence reads from a sample in a next-generation sequencing (NGS) dataset against reference genome entries in a classified reference genome database. Embodiments of the present invention may include classifying, filtering, and displaying results to a clinician that can then quickly and easily obtain the results of the sequencing to identify a pathogen or other genetic material in a sample that is being tested. A negative sample and a corresponding database can be used to remove contaminants from a list of candidate pathogens. Thus, embodiments are directed to a system that is configured to filter the results of a sequencing alignment and classify a sample quickly.