Nanopore Measurement Model Adjustment for Sequence Estimation
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Solution Overview
Problem
Nanopore-based sequencing technologies face challenges in accurately estimating nucleotide sequences due to variations in measurement systems, such as local and systematic changes in nanopore properties, leading to reduced accuracy in resolving k-mers and identifying single nucleotides from measurements dependent on groups of nucleotides.
Innovation Solution
A computer-implemented method adjusts a global model of nanopore measurement systems by performing parameterized transformations on emission and transition weightings using reference measurements, allowing the model to better fit the specific conditions of each measurement series, thereby improving the accuracy of nucleotide sequence estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a global model is used for all measurements, then the model is simple and consistent, but it cannot account for read-to-read variations and local nanopore property changes, reducing accuracy
Solution Approach 1:
The global model is segmented into multiple adjusted models, each tailored to specific reading conditions or nanopore instances. This segmentation allows the system to capture local variations and read-to-read differences while maintaining the overall framework of a Hidden Markov Model, thereby improving accuracy without completely redesigning the model structure.
Solution Approach 2:
The model transitions from a static global model to dynamic adjusted models that adapt to specific reading conditions. By introducing adjustable parameters that can be optimized for each reading or nanopore instance, the system dynamically adapts to local variations in nanopore properties, measurement conditions, and polynucleotide characteristics, improving estimation accuracy.
2Measurement precision
If k is increased to resolve more k-mers, then more nucleotide combinations can be distinguished, but signal distributions overlap more and measurements become harder to resolve
Solution Approach 1:
The system optimizes the value of k and adjusts the model parameters (emission and transition weightings) to achieve the best balance between k-mer resolution and signal distinguishability. By changing parameters such as the k-mer size and the weighting schemes in the Hidden Markov Model, the system can resolve sufficient k-mer diversity without overwhelming signal overlap, thereby improving measurement precision while managing detection difficulty.
3Measurement precision
If the model is adjusted to fit specific measurement conditions, then accuracy improves, but the adjustment process requires additional reference measurements and computation
Solution Approach 1:
Reference measurements are taken beforehand to pre-adjust the global model into adjusted models that account for specific nanopore properties and measurement conditions. This preliminary adjustment prepares the models in advance, so when actual sequencing measurements are taken, the system can immediately apply the pre-adjusted models without requiring time-consuming real-time adjustments, thereby reducing the time loss while maintaining high accuracy.
Solution Approach 2:
The system uses reference measurements with known sequences to provide feedback for adjusting the global model. This feedback mechanism allows the system to learn and adapt to specific nanopore characteristics and measurement conditions, improving accuracy. The feedback loop is established during the calibration phase using reference data, enabling efficient model adjustment without continuous time loss during actual sequencing operations.
Data Source
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AI summary
An estimate of a target sequence of polymer units is generated from a series of measurements taken by a measurement system comprising nanopores during translocation of the polymer through a nanopore. A global model of the measurement system is stored, comprising transition weightings for possible transitions between k-mers on which successive measurements are dependent and emission weightings for possible values of measurements being observed when the measurement is dependent on possible identities of k-mer. The global model is adjusted, making reference to measurements taken using the measurement system such that the fit of the measurements to the adjusted model is improved. The estimate of a target sequence of polymer units is generated using the adjusted model. The adjustment of the model improves the quality of the estimation.