Genomic Variant Identification via Automated Annotation and Filtering
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Solution Overview
Problem
The high costs and labor associated with analyzing genomic data for identifying genetic variations linked to disease phenotypes remain a challenge, despite the decrease in genomic sequencing costs.
Innovation Solution
A processor-readable medium with code that receives genetic variants from comparing experimental and reference DNA sequences, annotates them based on specific criteria, and filters these variants to identify potential disease-causing variants for clinical diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genomic sequencing is performed to identify genetic variations, then disease-associated variants can be detected, but the cost and labor for data analysis remain high
Solution Approach 1:
The patent segments the data analysis process into distinct functional modules including variant filtering, annotation, prioritization, and interpretation. This modular approach allows each component to be optimized independently and processed in parallel, significantly improving analysis throughput while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary filtering and annotation of variants before detailed analysis. By pre-processing the data to eliminate obviously benign variants and annotate key features in advance, the system reduces the computational burden on subsequent analysis steps, thereby improving overall productivity without compromising detection precision.
2Reliability
If comprehensive annotation and filtering of all variants is performed, then diagnostic accuracy is improved, but the time and computational resources required increase
Solution Approach 1:
The patent applies local quality by performing comprehensive annotation and filtering only on variants that pass initial screening criteria. Rather than uniformly processing all variants with the same level of detail, the system adapts the depth of analysis to each variant's characteristics, focusing computational resources on promising candidates while quickly dismissing obvious non-candidates.
Solution Approach 2:
The system performs partial action by implementing a multi-stage filtering process where not all annotation and filtering operations are applied to every variant. Instead, variants progress through successive filters, with only those meeting specific criteria advancing to more computationally intensive analysis stages, thereby reducing overall processing time while maintaining diagnostic reliability for positive cases.
3Measurement precision
If manual analysis of genetic variants is performed by healthcare technicians, then detailed evaluation is possible, but labor costs and workload remain constant
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform variant filtering, annotation, prioritization, and preliminary interpretation without requiring manual intervention at each step. The automated pipeline processes variants through multiple analysis stages, generating prioritized lists that require minimal manual review, thereby significantly reducing technician workload while maintaining evaluation accuracy through algorithmic consistency.
Solution Approach 2:
The system replaces manual mechanical analysis by healthcare technicians with automated computational analysis. The software performs variant evaluation using standardized criteria and algorithms, substituting human manual review with machine-based analysis that can process large volumes of data consistently and efficiently, reducing both labor costs and variability in evaluation quality.
Data Source
AI summary
In some embodiments, a non-transitory processor-readable medium includes code to cause a processor to receive a set of variants identified by a comparison of a test DNA sequence with a reference DNA sequence and associate at least one of the set of variants with at least one of a set of annotations each indicative of at least one criterion. The code includes code to cause the processor to filter, based on the set of annotations, the set of variants to identify a subset of variants from the set of variants. Each variant from the subset of variants is associated with at least one common annotation from the set of annotations. The code further includes code to cause the processor to present the subset of variants such that the subset of variants can be used to render a clinical diagnosis.