Next-Generation Sequencing Probe Design with Adaptive Concentration Tuning
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Solution Overview
Problem
Existing methods for designing probes for next-generation sequencing assays are inadequate in optimizing probe concentrations to achieve uniform coverage across the genome, leading to inefficiencies in sequencing performance.
Innovation Solution
The method involves modifying probe concentrations by suppressing over-performing probes and enhancing under-performing probes using techniques such as altering the ratio of labeled and unlabeled probes, adding locked nucleic acid modifications, and employing interference methods to achieve optimized probe sets with even capture rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If probe concentration is reduced by adding reverse complement of over-performing probes, then sequencing coverage uniformity is improved, but probe pool complexity increases
Solution Approach 1:
The probe pool is divided into multiple sub-pools, each containing probes with similar characteristics or targeting specific genomic regions. This segmentation allows independent optimization of each sub-pool and simplifies the management of probe concentrations by treating them as modular units rather than a monolithic mixture.
Solution Approach 2:
Different sub-pools are assigned different concentrations based on their specific performance characteristics and genomic target requirements. Instead of uniform treatment, each sub-pool receives tailored concentration optimization, allowing over-performing regions to be suppressed locally while under-performing regions are enhanced without affecting the entire probe pool.
2Manufacturing precision
If array-based platform is used to set probe concentration, then coverage uniformity is improved, but manufacturing cost and complexity increase
Solution Approach 1:
The invention changes the concentration parameter of probes in different sub-pools to achieve uniform coverage. By adjusting the molar ratios of probes across sub-pools rather than using a single uniform concentration, the system achieves precision comparable to array-based platforms while using simpler liquid-phase hybridization chemistry.
3Adaptability or versatility
If probe sub-pools are formulated at known equimolar concentrations, then modular use is enabled, but coverage uniformity across all targets is insufficient
Solution Approach 1:
The probe set is divided into multiple sub-pools that can be independently formulated, stored, and combined. Each sub-pool maintains equimolar concentration for modular flexibility, while the differential concentration adjustment across sub-pools achieves overall coverage uniformity when combined.
Solution Approach 2:
Each sub-pool is optimized with specific concentration characteristics suited to its genomic targets, while maintaining the ability to be used independently or in combination. This local optimization preserves modular versatility while achieving global coverage uniformity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach results in improved uniformity and sequencing depth across target regions, reducing biases and enhancing the overall performance of next-generation sequencing assays.
Implementation Method 1
altering the ratio of labeled and unlabeled probes, adding locked nucleic acid modifications, and employing interference methods
Data Source
AI summary
Systems and methods are provided for determining an optimized probe set. The method proceeds by obtaining a set of probes, where each probe has a respective concentration. The set of probes is assayed against a sample library, and at least i) a respective recovery rate for each probe in the set of probes, and ii) a median recovery rate for the set of probes are obtained. Modify the respective concentration of each probe that does not satisfy predetermined recovery rate threshold. Reevaluate the set of probes against the sample library. Repeat the modifying and reevaluation until the respective updated recovery rate for each probe in the updated set of probes satisfies the predetermined recovery rate threshold, thereby providing the optimized set of probes for the sample library.