Genomic Data Analyzer for NGS Variant Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Next-generation sequencing (NGS) technologies face challenges in achieving high specificity and sensitivity in genomic analysis due to biases from sequencing technology, DNA enrichment methods, and genomic data structures, particularly in regions with homopolymer and heteropolymer repeats, which require manual customization and are costly and inefficient for routine clinical applications.
Innovation Solution
A genomic data analyzer system that automatically detects experimental and genomic context-specific biases by configuring sequence alignment and variant calling modules to refine data processing, using probability distributions to accurately estimate repeat pattern lengths and minimize experimental errors, enabling robust variant calling across diverse sequencing setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual customization is used for analyzing homopolymer and heteropolymer repeat regions, then measurement precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The system performs preliminary action by pre-calculating expected probability distributions for various repeat pattern lengths using control samples before analyzing patient data. This pre-computation of reference distributions enables automated comparison and eliminates the need for manual customization during actual variant detection, while maintaining high precision through statistically robust expected value comparisons.
Solution Approach 2:
The system implements self-service by automatically detecting experimental biases and genomic context-specific biases without human intervention. The bias detection module autonomously analyzes control samples to characterize sequencing and enrichment biases, then uses these characteristics to automatically adjust probability distribution calculations for accurate variant calling in repeat regions, eliminating manual workflow customization.
2Productivity
If automated bias detection is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the complex automated workflow into distinct functional modules: a bias detection module that analyzes control samples to characterize sequencing and enrichment biases, a probability distribution calculation module that computes expected distributions for various repeat pattern lengths, and a variant detection module that compares patient data against these distributions. This modular segmentation manages system complexity by making each module independently testable and maintainable while achieving high overall productivity through automation.
Solution Approach 2:
The system uses parameter changes by dynamically adjusting probability distribution parameters based on detected biases. The bias detection module quantifies sequencing and enrichment biases as numerical parameters, which are then used to modify the expected probability distribution calculations. This automatic parameter adjustment enables the system to adapt to different experimental conditions and genomic contexts without manual reconfiguration, significantly improving productivity across diverse applications.
3Measurement precision
If probability distributions are used to estimate repeat pattern lengths, then measurement precision is improved, but loss of time increases due to computational complexity
Solution Approach 1:
The system applies preliminary action by pre-computing expected probability distributions for various repeat pattern lengths using control samples before patient data analysis. These pre-calculated reference distributions are stored and reused during variant detection, eliminating the need to perform complex computational simulations during actual patient sample analysis. This approach maintains high measurement precision through statistically robust distributions while significantly reducing computational processing time for clinical applications.
Data Source
AI summary
A genomic data analyzer may be configured to detect and characterize, with a variant calling module, genomic variant scenarios on sequencing reads from an enriched patient genomic sample comprising a combination of a first repeat pattern and a second repeat pattern, such as repeats of homopolymer (single nucleotide) and/or heteropolymer (multiple nucleotide) basic motifs. The variant calling module may estimate the probability distribution of the length of the first repeat pattern and the probability distribution of the repeat pattern length measurements in patient data to the distribution of the repeat pattern length measurements in control data, in order to remove biases possibly induced by the next generation sequencing laboratory setup both in control and patient data. The variant calling module may further measure, read by read, the joint probability distribution for the first and the second repeat patterns lengths, and compare it with the expected joint probability distribution for various genomic variant scenarios for the patient, each variant scenario being characterized by a first length of the first repeat pattern and a second length of the second repeat pattern, to select the most likely patient genomic variant scenario as the scenario for which the measured joint probability distribution best matches the expected joint probability distribution.