Synchronous Sequencing Base-Calling Correction for Optical Crosstalk
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Solution Overview
Problem
Existing base-calling software is inadequate for synchronous sequencing, failing to accurately recognize base combinations due to optical crosstalk and phasing interference, leading to inaccurate sequencing results.
Innovation Solution
A method and system for correcting base-calling results in synchronous sequencing by applying crosstalk and phasing corrections using regression models and machine learning, utilizing high-confidence sample spots to determine correction parameters, and integrating the corrected results into a machine learning model for accurate base combination determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing base-calling software is used for synchronous sequencing, then the sequencing process can be completed, but the base recognition accuracy deteriorates due to optical crosstalk and phasing interference
Solution Approach 1:
The patent introduces an intermediary correction algorithm that processes the raw base-calling results. This intermediary layer applies crosstalk correction and phasing correction to the signal data, acting as a mediator between the raw sequencing data and the final accurate base recognition, thereby resolving the contradiction between maintaining high throughput and improving accuracy
Solution Approach 2:
The patent replaces the traditional mechanical/base-calling software approach with a computational correction system. Instead of relying on hardware modifications or simple software rules, the invention uses mathematical models and algorithms to substitute and correct the signal processing, enabling accurate base recognition from synchronous sequencing data
2Speed
If synchronous sequencing is performed to improve throughput, then sequencing speed increases, but signal accuracy deteriorates due to crosstalk and phasing interference
Solution Approach 1:
The patent implements feedback mechanisms where the correction algorithm continuously refines the base-calling results by comparing expected patterns with actual signals. The crosstalk correction and phasing correction use feedback from the raw data to adjust and improve signal accuracy, maintaining reliable results at high sequencing speeds
Solution Approach 2:
The patent changes the parameters of signal processing by applying correction factors and transformation algorithms to the raw signal data. By modifying how the signal parameters are interpreted and corrected, the system maintains high sequencing speed while improving signal accuracy through mathematical transformations
Data Source
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AI summary
Provided is a synchronous sequencing method, including: constructing a sequencing library for a nucleic acid sample to be tested; loading the sequencing library onto a sequencing chip; performing a plurality of synchronous sequencing reaction cycles on the sequencing library, wherein an image set generated in each of the plurality of synchronous sequencing reaction cycles constitutes a raw image set of the synchronous sequencing; acquiring a base-calling result of the synchronous sequencing based on the raw image set of the synchronous sequencing; correcting the signal intensity value of each base channel based on a predetermined correction parameter to obtain a corrected base-calling result; and determining a base output result of the synchronous sequencing based on the corrected base-calling result.