Genomic Variant Interpretation via Automated Knowledge Curation
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Solution Overview
Problem
Current clinical sequencing workflows face challenges in efficiently interpreting DNA variants due to the complexity of increasing test volumes, large datasets, and the need for timely curation of literature, which can delay patient treatment and hinder clinical trial enrollment.
Innovation Solution
A knowledge-based system that utilizes expert-curated content and ontology to streamline variant classification, providing automated suggestions and integrating phenotype information for scalable and reproducible decision support, while enabling efficient identification of suitable patients for clinical trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual literature curation is performed by geneticists, then variant interpretation accuracy is improved, but time delay increases and productivity decreases
Solution Approach 1:
The system performs preliminary automated curation of literature and variant interpretation before human review. By pre-processing and organizing relevant information in advance, the system reduces the time geneticists need to spend on initial literature review while maintaining accuracy through subsequent expert validation.
Solution Approach 2:
An automated informatics system acts as an intermediary between raw sequence data and human geneticist review. This intermediary layer performs initial variant classification, literature search, and evidence synthesis, presenting pre-processed information to geneticists for final interpretation, thereby reducing their workload and time requirements.
2Measurement precision
If manual literature curation is performed by geneticists, then variant interpretation accuracy is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary automated curation of literature and variant interpretation before human review. By pre-processing and organizing relevant information in advance, the system reduces the time geneticists need to spend on initial literature review while maintaining accuracy through subsequent expert validation.
Solution Approach 2:
An automated informatics system acts as an intermediary between raw sequence data and human geneticist review. This intermediary layer performs initial variant classification, literature search, and evidence synthesis, presenting pre-processed information to geneticists for final interpretation, thereby reducing their workload and time requirements.
3Adaptability or versatility
If test complexity increases with more genes assayed, then diagnostic capability is improved, but data interpretation difficulty increases
Solution Approach 1:
The system segments the complex interpretation task by processing variants at multiple levels: individual variant analysis, gene-level aggregation, pathway analysis, and phenotypic correlation. This hierarchical segmentation breaks down the overwhelming complexity of whole-exome or whole-genome data into manageable analytical components.
Solution Approach 2:
An automated informatics system acts as an intermediary between raw sequence data and human geneticist review. This intermediary layer performs initial variant classification, literature search, and evidence synthesis, presenting pre-processed information to geneticists for final interpretation, thereby reducing their workload and time requirements.
4Reliability
If manual literature review is performed, then up-to-date knowledge is incorporated, but time and resources are consumed
Solution Approach 1:
The system performs preliminary automated curation of literature and variant interpretation before human review. By pre-processing and organizing relevant information in advance, the system reduces the time geneticists need to spend on initial literature review while maintaining accuracy through subsequent expert validation.
Solution Approach 2:
The automated informatics system performs self-service by autonomously searching, retrieving, and analyzing current literature related to observed variants. The system automatically updates its knowledge base with new publications and clinical data, maintaining currency without requiring continuous manual intervention.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for aiding in the interpretation of variants observed in clinical sequencing data. An embodiment operates by receiving clinical trial enrollment criteria from a user, including but not limited to genetic targeting criteria; searching a knowledge base of patient test information received from a plurality of independent entities for patients that match the clinical trial enrollment criteria; and providing to the user search results for consented patients that match the clinical trial enrollment criteria.