Interactive Dendrogram for Pathogen Sequence Clustering
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Solution Overview
Problem
Current software tools used by health departments in the U.S. for tracking pathogens are cumbersome and error-prone, making it difficult to identify infectious disease outbreaks from millions of nucleotide sequences and extract actionable medical data.
Innovation Solution
A user interface system that includes an interactive dendrogram for clustering biological samples based on nucleotide sequence similarity and a similarity matrix to analyze genetic variations, supported by a backend system for processing and visualizing medical and genomic data, enabling efficient detection and tracking of infectious disease outbreaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rudimentary software tools such as spreadsheets are used to track pathogen sequences, then device complexity is reduced, but productivity and reliability deteriorate due to cumbersome operations and errors
Solution Approach 1:
The patent introduces an intermediary software system that acts as a mediator between the complex pathogen sequence data and the user. This system includes automated clustering algorithms, visualization interfaces, and data processing pipelines that handle the complexity internally while presenting simplified interactions to users, thereby improving productivity without requiring users to manage the underlying complexity directly
Solution Approach 2:
The patent segments the pathogen tracking system into modular components including sequence processing modules, clustering modules, visualization modules, and data management modules. Each module handles specific tasks independently, allowing the system to manage complexity through division of labor while maintaining high productivity through specialized processing in each segment
2Device complexity
If rudimentary software tools are used for analyzing millions of nucleotide sequences, then device complexity is low, but measurement precision and reliability worsen due to error-prone manual tracking
Solution Approach 1:
The patent implements self-service mechanisms where the software system automatically performs quality control checks, error detection, and validation of sequence data without requiring manual intervention. The system includes automated algorithms that verify data integrity, detect anomalies, and correct errors, thereby maintaining high measurement precision while keeping the interface simple for users
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor the accuracy and precision of sequence analysis. The system provides real-time feedback on data quality metrics, clustering confidence levels, and analysis results, allowing automated adjustments to maintain high precision while managing complexity through intelligent control loops
3Ease of operation
If manual review of medical data is performed to identify outbreaks, then ease of operation is maintained with simple tools, but loss of time increases due to the need to review multiple data types in various ways
Solution Approach 1:
The patent performs preliminary actions by automatically pre-processing and organizing medical data before user review. The system pre-clusters sequences, pre-identifies potential outbreaks, and pre-organizes data by relevant criteria, so that when users do review the data, they are working with pre-processed information that requires minimal additional time while maintaining ease of operation
Solution Approach 2:
The patent transforms the data review process by adding dimensional organization to the data presentation. Instead of reviewing data in a single flat dimension, the system organizes data across multiple dimensions including temporal, geographic, and genetic similarity dimensions simultaneously, allowing users to identify outbreaks faster by viewing data through multiple organizational lenses without increasing operational complexity
4Productivity
If comprehensive pathogen sequence data is collected to improve outbreak detection, then productivity improves, but device complexity and difficulty of detecting patterns worsen due to millions of sequences to manage
Solution Approach 1:
The patent extracts actionable information from the vast pathogen sequence data by automatically identifying and isolating key patterns, outlier sequences, and potential outbreak signals. The system separates relevant signal from noise by extracting only the critical information needed for outbreak detection, thereby maintaining high productivity while reducing the complexity of data management by focusing on extracted insights rather than raw data volumes
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting clustering thresholds, similarity criteria, and analysis parameters based on the volume and characteristics of the data being processed. The system automatically modifies these parameters to optimize the balance between comprehensive outbreak detection and manageable system complexity, allowing the system to handle millions of sequences efficiently by adapting its processing parameters to the data scale
Data Source
AI summary
Techniques for a user interface for pathogen analysis are provided. The user interface may include an interactive dendrogram that identifies a plurality of biological samples and their corresponding sequences. The biological samples of the interactive dendrogram may be arranged based on a degree of similarity between nucleotide sequences of the biological samples. In response to a selection of biological sample, the interactive dendrogram may identify a cluster of biological samples. Each biological sample of the cluster can be identified based on a determination that a number of variations between the sequences of the biological sample and the selected biological samples are under a predefined threshold. The user interface may also include a similarity matrix that identifies a number of variations between sequences of two biological samples selected from the interactive dendrogram.