DNA Sequencing Correlation Analysis for Indel Error Correction
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Solution Overview
Problem
Existing de novo DNA sequencing technologies face inefficiencies in correcting insertion and deletion errors, particularly in the absence of a reference sequence, which hampers the generation of accurate genomic sequences.
Innovation Solution
The application of correlation matrices and Viterbi algorithms to analyze multiple copies of DNA strands, determining a most likely path to correct insertion and deletion errors, followed by consensus alignment and error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error correction methods are used in de novo sequencing, then the process can be completed, but the efficiency is low and accuracy is insufficient for correcting insertion and deletion errors
Solution Approach 1:
The patent introduces a correlation matrix as an intermediary data structure that captures relationships between sequence copies. This matrix serves as a mediator that enables efficient error correction by transforming the complex problem of comparing multiple sequences into a matrix-based analysis, allowing identification of insertion and deletion errors through correlation patterns rather than direct sequence alignment
Solution Approach 2:
The patent replaces traditional mechanical sequence alignment algorithms with a Viterbi algorithm that processes correlation matrices. This substitution transforms the error correction mechanism from direct sequence comparison to probabilistic path finding through matrix correlations, significantly improving both accuracy and efficiency in correcting insertion and deletion errors
2Reliability
If multiple copies of DNA strands are sequenced to improve accuracy, then error correction becomes possible, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple sequence copies into a unified correlation matrix structure. Instead of processing each sequence copy independently, the method combines information from all copies into a single matrix that captures their relationships, reducing processing complexity while maintaining the reliability benefits of multiple copies
Solution Approach 2:
The patent transforms the error correction problem from a one-dimensional sequence comparison approach to a two-dimensional matrix analysis. By representing sequence relationships as correlations in a matrix, the system gains an additional dimension for analysis that simplifies the detection and correction of errors across multiple copies
Data Source
AI summary
Example systems and methods for de novo sequencing of DNA or DNA-like sequences using Viterbi-like correlation analysis are described. A sequencing system receives the read data for multiple copies of a DNA strand from a sequence reader, such as a nanopore reader. The sequencing system generates a convolutional matrix based on one copy and a reference matrix based on another copy and uses them to generate a correlation matrix. A most likely path through the correlation matrix is determined to identify and correct errors between the two copies.