Nucleotide Analysis System for Microbial Detection
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Solution Overview
Problem
Existing methods for sequencing microbes are inadequate for non-human samples due to assumptions based on human nucleic acid similarities, leading to the neglect of unaligned sequences and limited insights into microbial populations and disease states.
Innovation Solution
A nucleotide analysis system that utilizes a deep learning system to analyze collective genomes from samples, incorporating both aligned and unaligned sequences to predict microbe populations, disease states, and transmission pathways, without requiring individual microbe isolation or sequencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sequencing methods use alignment algorithms to count nucleic acid sequences matching known microbe genomes, then the presence of known microbes can be detected, but sequences with mutations or from unknown microbes are discarded as unaligned and lost
Solution Approach 1:
The patent converts the previously harmful or useless unaligned sequences into beneficial information by applying machine learning models. These sequences, which contained mutations or represented unknown microbes, are now processed to identify novel pathogens and track disease outbreaks, transforming data that was discarded as noise into valuable epidemiological intelligence
Solution Approach 2:
The patent changes the analytical parameters from traditional alignment-based exact matching to machine learning-based pattern recognition. This allows the system to detect microbial sequences with mutations and identify novel pathogens by learning from collective genome data, rather than requiring exact matches to known genomes
2Ease of manufacture
If traditional methods assume human nucleic acid sequence similarities, then human genetic studies can be standardized, but these assumptions are invalid for microbial sequencing
Solution Approach 1:
The patent creates a universal machine learning-based analysis system that functions across diverse microbial species without requiring species-specific assumptions. The collective genome approach and ML models can analyze sequences from any microbe, making the system adaptable to various pathogens while maintaining standardized analytical procedures
Solution Approach 2:
Instead of assuming similarities and looking for differences (the traditional human-centered approach), the patent inverts the methodology by using machine learning to learn the actual patterns and differences present in microbial data. This allows the system to adapt to microbial diversity rather than forcing microbial data into human genetic frameworks
3Measurement precision
If individual microbe isolation and sequencing is performed, then precise identification can be achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent merges multiple individual sequencing results into a collective genome analysis. By combining sequences from many samples and using machine learning to analyze the aggregate data, the system achieves precise pathogen identification without requiring laborious individual isolation and sequencing of each microbe, significantly simplifying the workflow
Data Source
AI summary
A system and method for the detection of pathogens and other microbes using nucleotide analysis is described. Aligned and unaligned nucleotide sequences are utilized to predict the presence or absence of pathogens and other microbes.