Primer Sequence Removal in Amplicon NGS via Multi-Reference Matching
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Solution Overview
Problem
Conventional methods for removing primer sequences in amplicon-based next-generation sequencing (NGS) are inefficient and inaccurate due to reliance on a single reference value, leading to low primer removal accuracy and prolonged processing times, which can result in false positives during variant detection.
Innovation Solution
A method that involves acquiring read data through amplicon-based NGS, analyzing the primer sequence within the read, and removing it using multiple reference values and error tolerance, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional programs use only one reference value for primer removal, then the process is simple, but the primer removal accuracy is low
Solution Approach 1:
The patent applies parameter changes by comparing read sequences against multiple reference values (multiple primer sequences) rather than a single reference value. This allows the system to account for variations in primer sequences across different amplicons and sequencing runs, significantly improving primer removal accuracy while managing complexity through systematic comparison protocols
2Productivity
If conventional programs use only one reference value for primer removal, then the method is simple, but it takes a long time to detect and remove primer sequences
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing multiple reference primer sequences before the actual primer removal process. This preparation phase creates an efficient lookup structure that enables rapid comparison during primer removal, thus improving both speed and accuracy simultaneously
3Reliability
If the primer sequence is not removed, then the analysis can be performed on the complete read data, but it acts as a false positive in variation detection
Solution Approach 1:
The patent applies the extraction principle by identifying and removing the primer sequence portion from the read data before variation detection analysis. This separation extracts the harmful primer sequence component that causes false positives, allowing the remaining authentic sequence data to be analyzed without contamination from primer-derived artifacts
Data Source
AI summary
The present invention relates to a method for increasing the efficiency of read data analysis by removing primer sequence information present in a read obtained through next-generation sequencing (NGS) and, more specifically, to a method for matching information of a read and a designed primer to various reference values in several steps so as to determine primer sequence information within a read, and then precisely removing only a primer sequence so as to increase the efficiency of read data analysis. The method for increasing the efficiency of read data analysis in a primer removal-based NGS, according to the present invention, has a rapid data analysis speed and can precisely remove only a primer sequence, thereby being useful for increasing the efficiency and accuracy of read data analysis.