Basecall Quality Parameter Determination Using Probability Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing basecall algorithms based on machine learning fail to accurately reflect error rates due to reliance on fluorescence intensity information, leading to suboptimal quality parameter determination in nucleic acid sequencing.
Innovation Solution
A method and apparatus for determining a quality parameter in basecall by acquiring calling result data, calculating a first parameter based on base probability distribution, and filtering it using a preset Q0 value to derive a target quality parameter, utilizing Q0=−10×log10 e, where e is the basecall error rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning basecall algorithms are used, then basecall accuracy is improved, but quality parameter determination becomes inaccurate
Solution Approach 1:
The patent changes the parameter used for quality assessment from fluorescence intensity-based metrics to base probability distribution-based metrics. By calculating quality parameters from the base probability distribution output by machine learning models, the system maintains the accuracy benefits of ML while establishing reliable quality parameters that actually reflect basecall error rates.
2Ease of manufacture
If fluorescence intensity information is relied upon, then existing basecall algorithms work, but error rate reflection becomes inaccurate
Solution Approach 1:
The patent replaces the traditional fluorescence intensity-based quality assessment mechanism with a probability distribution-based mechanism. This substitution eliminates the fundamental flaw of using intensity information that does not accurately reflect error rates, while maintaining ease of implementation through straightforward probability calculations.
3Reliability
If quality parameter is determined using base probability distribution, then quality parameter accuracy is improved, but calculation complexity increases
Solution Approach 1:
The patent transforms the quality parameter determination into a straightforward parameter calculation based on base probability distributions. By using simple mathematical operations on probability values (calculating expected errors and converting to Q-scores), the system achieves high accuracy without introducing complex computational procedures.
Data Source
AI summary
The present application discloses a method and an apparatus for determining a quality parameter in basecall. The method comprises: acquiring calling result data of a basecall model for a nucleic acid sample of interest, the calling result data comprising base probability distribution for a current base extension reaction in the nucleic acid sample of interest; calculating a first parameter on the basis of the base probability distribution; and filtering the first parameters on the basis of a preset Q0 value to give a target quality parameter. In the present application, the target quality parameter is determined using the information output by the basecall model, such that the target quality parameter can more accurately determine the basecall results learned by a machine.