Genomic Data Analyzer Automated NGS Workflow Configuration
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Solution Overview
Problem
Next-generation sequencing (NGS) workflows in clinical practice face challenges in specificity and sensitivity due to biases introduced by sequencing technology, DNA enrichment methods, and genomic data structures, requiring manual configuration and optimization by specialized personnel, which is costly and not scalable.
Innovation Solution
A method for analyzing NGS genomic data that automatically identifies characteristics of the sequencing request, configures data alignment and variant calling modules, and refines alignments to minimize biases, enabling the genomic data analyzer to operate on diverse data from different laboratory setups with improved sensitivity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration and optimization of NGS workflows is performed by specialized personnel, then specificity and sensitivity of genomic analysis are improved, but cost and scalability deteriorate
Solution Approach 1:
The system automatically detects characteristics of NGS data (such as sequencing technology type, target enrichment method, genomic context) and self-configures the analysis workflow without requiring manual intervention from specialized personnel. The automated pipeline selects appropriate alignment and variant calling parameters based on detected data characteristics, enabling the system to serve itself rather than requiring continuous human optimization for each dataset.
Solution Approach 2:
The system changes processing parameters dynamically based on detected data characteristics. When different sequencing technologies, enrichment methods, or genomic contexts are identified, the system automatically adjusts alignment algorithms, quality thresholds, and variant calling parameters to optimize specificity and sensitivity for each specific data type, eliminating the need for manual reconfiguration.
2Reliability
If NGS workflows are manually setup for each case, then analysis specificity is optimized, but device complexity and operational burden increase
Solution Approach 1:
The system automatically detects characteristics of incoming NGS data and self-configures the entire analysis workflow, including selection of alignment algorithms, parameter settings, and variant calling methods. This eliminates the need for manual workflow setup for each case, reducing operational burden while maintaining high analysis specificity through automated optimization.
Solution Approach 2:
The system performs preliminary detection of data characteristics (sequencing technology, enrichment method, genomic context) before initiating the analysis workflow. Based on this preliminary information, the system pre-configures all necessary processing parameters and selects appropriate algorithms in advance, so that when actual analysis begins, everything is already optimized and ready, eliminating manual configuration steps.
3Productivity
If automated workflow processing is implemented, then scalability is improved, but measurement precision may deteriorate due to lack of manual optimization
Solution Approach 1:
The system incorporates feedback mechanisms where the detected data characteristics directly influence the configuration of analysis parameters. By continuously monitoring and adapting to the specific characteristics of each NGS dataset (such as read depth, quality scores, genomic context), the automated system adjusts its processing parameters in real-time to maintain high specificity and sensitivity while scaling operations.
Solution Approach 2:
The automated system dynamically changes processing parameters based on detected data characteristics. When different sequencing technologies or enrichment methods are identified, the system automatically selects and adjusts alignment algorithms, quality thresholds, and variant calling parameters to match the optimal settings for each data type, ensuring that scalability does not compromise measurement precision.
Data Source
AI summary
A genomic data analyzer system method to analyze next generation sequencing genomic data from a sourcing laboratory. The method includes receiving, with a processor, a next generation sequencing analysis request from a sourcing laboratory, the next generation sequencing request comprising at least a raw next generation sequencing data file and the sourcing laboratory identification; identifying, with a processor, a first set of characteristics associated with the next generation sequencing analysis request, the first set of characteristics comprising at least a target enrichment technology identifier, a sequencing technology identifier, and a genomic context identifier; configuring, with a processor, a data alignment module to align the input raw sequencing data file in accordance with at least one characteristic of said first set of characteristics; and aligning, with the data alignment module processor, the input sequencing data to a genomic sequence.