Biopolymer Sequencing via Structural Electronegativity Encoding
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Solution Overview
Problem
Existing methods for sequencing biopolymers, such as DNA, through nanopore technology face challenges with statistical error and numerical effort, requiring improvements in accuracy and efficiency.
Innovation Solution
The method involves placing a biopolymer in an osmosis cell with a nanopore membrane, applying a voltage, and measuring the current as the biopolymer passes through the nanopores. This current is recorded over time and encoded using structural electronegativity encoding (SEN encoding), which identifies the biopolymer sequence using a neural network, specifically a transformer architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical methods are used for nanopore sequencing, then the sequencing can be performed, but statistical errors increase and numerical effort increases
Solution Approach 1:
The patent replaces traditional statistical methods with a neural network-based machine learning system. The neural network is trained on simulated nanopore sequencing data and then used to predict base sequences from actual sequencing signals, substituting the mechanical statistical analysis process with an intelligent system that reduces both statistical errors and numerical computational effort while improving sequencing accuracy
2Productivity
If traditional methods are used to handle dynamic artifacts like slippage and sticking, then sequencing continues, but accuracy decreases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on simulated nanopore sequencing data that includes various dynamic artifacts such as slippage and sticking events. This pre-training prepares the network to recognize and correctly interpret these artifacts during actual sequencing, allowing the system to maintain high throughput while achieving improved accuracy in distinguishing true biological signals from artifacts
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 enhances the accuracy and efficiency of biopolymer sequencing by reducing statistical errors and improving the handling of dynamic artifacts such as slippage and sticking, while also providing a mechanism for recognizing biopolymer structural units and epigenetic markers.
Implementation Method 1
The passing of the biopolymer through the nanopores of the membrane may be driven by concentration gradients, by the applied voltage
Implementation Method 2
measuring a current through the osmosis cell as the biopolymer passes the nanopores
Implementation Method 3
The passing of the biopolymer through the nanopores of the membrane may be driven by concentration gradients, by the applied voltage
Data Source
Figure 1~3
Figure 4a~4c
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AI summary
The present invention provides sequencing of biopolymers by: putting a biopolymer in a first chamber of an osmosis cell, the osmosis cell comprising two chambers and a membrane with nanopores; applying a voltage across the osmosis cell; measuring a current through the osmosis cell as the biopolymer passes the nanopores; recording a time sequence of the current; encoding the monomers of the biopolymer via structural electronegativity encoding; and identifying the sequence of the biopolymer from the time sequence of the current using said SEN encoding. Herein, the biopolymer may be any one-dimensional biopolymer, for example, DNA, RNA, proteins, sugars, complex lipids, or artificially created biopolymers.