Dynamic Reference Panel for CNV Detection
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Solution Overview
Problem
Conventional systems for detecting copy number variants (CNVs) in genomic sequences using reference panels are not suitable for various laboratory workflows, leading to complex data management, uncertainty in selecting an optimal reference panel, and unreliable detection of variants, which can result in incorrect medical treatments.
Innovation Solution
A system and method that utilize a database and computing arrangement to store genomic DNA sequences and metadata, allowing for the identification of a dynamic reference panel based on characteristic attributes, enabling accurate and automated selection of a suitable reference panel for CNV detection, suitable for high-throughput, automated genomic analysis across different laboratory workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems use a fixed reference panel for CNV detection, then the system structure is simple, but the reliability of CNV detection deteriorates due to mismatches between reference panel and target sample characteristics
Solution Approach 1:
The reference panel is transformed from a static, fixed set of sequences to a dynamic, adaptable structure that can be automatically customized for each target sample. The system dynamically selects and weights reference sequences based on metadata matching criteria such as sequencing technology, sample type, and laboratory workflow characteristics, ensuring optimal reliability without manual intervention.
Solution Approach 2:
The system changes the parameters used to construct the reference panel by incorporating multiple metadata attributes (sequencing technology type, sample type, library preparation method, etc.) to characterize and select reference sequences. This parameter-based selection approach adapts the reference panel to match the target sample's characteristics, improving detection reliability.
2Adaptability or versatility
If manual assembly of reference panels is performed, then the adaptability to specific workflows is improved, but the loss of time and productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The system implements self-service by automatically assembling and configuring reference panels without requiring manual intervention. The computing arrangement autonomously compares target sample metadata with reference sequence metadata, selects appropriate references, and constructs the optimized panel, eliminating time-consuming manual assembly while maintaining workflow adaptability.
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing reference sequences with their metadata characteristics before actual CNV detection. This advance preparation enables rapid automatic selection and assembly when target samples are analyzed, eliminating the need for time-consuming manual assembly at the time of detection.
3Adaptability or versatility
If diverse sequencing technologies are used to generate genomic sequences, then the versatility of the system is improved, but the manufacturing precision deteriorates due to introduction of data errors and biases from different sequencing types
Solution Approach 1:
The system applies local quality by selecting reference sequences with specific metadata characteristics that match the target sample's sequencing technology and experimental conditions. Instead of using a generic reference panel, the system locally optimizes the reference composition based on the target's specific sequencing type, library preparation method, and other technical parameters, thereby maintaining high detection accuracy across diverse technologies.
Solution Approach 2:
The metadata comparison mechanism acts as an intermediary that bridges diverse sequencing technologies. By using metadata attributes as intermediate selection criteria, the system translates technical differences between sequencing platforms into compatible reference selections, enabling accurate CNV detection across multiple technologies without direct interference from their inherent biases.
4Reliability
If comprehensive metadata is collected and compared for reference panel selection, then the reliability of CNV detection is improved, but the device complexity increases due to additional data management requirements
Solution Approach 1:
The system segments the comprehensive metadata into distinct, manageable attributes (sequencing technology type, sample type, library preparation method, etc.). Each metadata attribute is independently collected, stored, and compared, allowing the complex data management task to be divided into simpler, modular operations that can be processed systematically without overwhelming complexity.
Data Source
AI summary
A system for managing a CNV reference panel is disclosed, wherein the system includes a database arrangement configured to store a plurality of sample genomic DNA sequences and metadata associated with each of plurality of sample genomic DNA sequences. The system further includes a computing arrangement communicatively coupled to the database arrangement. The computing arrangement is configured to render a user interface to receive a target genomic DNA sequence along with interpretation request for calling CNVs in target genomic DNA sequence. The computing arrangement compares the plurality of characteristic attributes in the interpretation request with the metadata associated with each of plurality of sample genomic DNA sequences. Furthermore, the computing arrangement identifies a set of sample genomic DNA sequences as a reference panel, based on the comparison. Moreover, the computing arrangement utilise the reference panel for calling CNVs in the target genomic DNA sequence.