Transcriptomic Signature Optimization for Cross-Platform Detection
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Solution Overview
Problem
Existing diagnostic methods face challenges in translating high-throughput transcriptomic data, such as RNAseq, to simpler detection platforms due to compromised performance of diagnostic signatures, leading to low cross-platform validation likelihood.
Innovation Solution
A method to optimize transcriptomic signatures for specific test platforms by considering platform-specific requirements, such as chemistry and instrumentation, to improve detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-throughput transcriptomic data (RNAseq) is used for diagnostic testing, then measurement precision and detection accuracy are improved, but device complexity and laboratory resource requirements increase
Solution Approach 1:
The patent extracts and selects only the most relevant transcriptomic features (genes, exons, or regions) from the comprehensive RNAseq data to create a simplified diagnostic signature. This extraction process identifies a subset of features that maintain high diagnostic accuracy while being suitable for simpler detection platforms like RT-qPCR, thereby reducing laboratory resource requirements while preserving measurement precision.
Solution Approach 2:
The patent segments the complex transcriptomic data into discrete, manageable features such as specific genes, exons, or regulatory regions. By dividing the comprehensive RNAseq output into these segmentable units, the system enables translation to simpler detection platforms that can handle individual features rather than processing entire transcriptomes, thus reducing device complexity.
2Device complexity
If transcriptomic signatures are translated to simpler detection platforms (RT-qPCR), then device complexity is reduced, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent applies parameter changes by optimizing the selected transcriptomic features according to the specific requirements of the target detection platform. This involves adjusting features such as amplicon length, GC content, and primer binding sites to match the capabilities of simpler platforms like RT-qPCR. By tailoring the parameters of the diagnostic signature to the specific platform, the system maintains high detection accuracy while using simpler, less complex technology.
Solution Approach 2:
The patent applies local quality by optimizing specific regions or features of the transcriptomic data for each detection platform. Instead of using a universal signature, the system identifies and optimizes local regions (specific genes, exons, or sequences) that are most suitable for the target platform's capabilities. This localized optimization ensures that each feature is tailored to maximize performance on the specific detection platform it will be used with.
3Measurement precision
If comprehensive transcriptomic data processing is performed, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent extracts and pre-identifies the most relevant diagnostic features from comprehensive RNAseq data during the discovery phase. By performing this extraction and optimization in advance, the system eliminates the need for time-consuming comprehensive processing during actual diagnostic testing. The pre-optimized signature can be directly applied to simpler, faster platforms like RT-qPCR, significantly reducing laboratory processing time while maintaining high diagnostic accuracy.
Solution Approach 2:
The patent performs preliminary actions by conducting the complex transcriptomic data processing, feature selection, and optimization during a discovery or development phase. This preliminary work creates a ready-to-use, pre-optimized diagnostic signature that can be immediately applied to simpler detection platforms without requiring extensive processing time during actual clinical diagnostics, thus reducing time loss and improving productivity.
Data Source
AI summary
The present application relates to a method of obtaining an optimised transcriptomic signature for identification of a biological condition. The optimised transcriptomic signature being optimised for detection via a particular test platform of a plurality of test platforms, each test platform being associated with at least one requirement. The method comprises receiving transcriptomic data obtained from a first set of subjects, wherein a first subset of subjects in the first set of subjects do have the biological condition, and a second subset of subjects in the first set of subjects do not have the biological condition processing the transcriptomic data to identify a plurality of candidate features, each candidate feature being suitable for use in identifying the biological condition. The method further comprises processing, based on at least one first requirement associated with a first test platform of the plurality of test platforms, the plurality of candidate features. The method further comprises outputting, based on the processing of the plurality of candidate features, the optimised transcriptomic signature for identification of a biological condition being optimised for detection via the first test platform, the optimised transcriptomic signature comprising at least one feature of the plurality of candidate features. The application also relates to a system comprising a memory for storing computer-readable instructions; and one or more processors for executing the computer readable instructions to perform the method.