Genomic DNA Concatenation for Mutation Detection Accuracy
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Solution Overview
Problem
Detecting somatic mutations in diseases is challenging due to errors in library preparation and sequencing methods, especially in samples with low diversity and low copy number mutations, making it difficult to distinguish genuine mutations from artifacts.
Innovation Solution
A method involving concatenating genomic DNA fragments, sequencing the concatenated DNA, and grouping sequence reads by 3′ and 5′ end sequences and flanking sequences to determine the genuineness of sequence variations using the number of reads and groups containing the variation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If next generation sequencing is used to detect somatic mutations, then sequencing throughput and coverage are improved, but the ability to distinguish genuine mutations from errors deteriorates due to PCR and sequencing errors
Solution Approach 1:
The method segments the detection problem by analyzing individual molecular clones separately rather than pooling all reads together. Each clone is evaluated independently to determine if it contains a genuine mutation or an error, allowing high-throughput sequencing to maintain its productivity while improving measurement precision through individual clone assessment
Solution Approach 2:
The method performs preliminary cloning and individual clone sequencing before final mutation calling. This preliminary action of separating reads into individual clone groups allows errors to be identified and filtered out before making the final determination of genuine mutations, thereby improving accuracy without sacrificing throughput
2Measurement precision
If samples with low diversity and low copy number mutations are analyzed, then the ability to detect rare mutations is improved, but the confidence in distinguishing genuine mutations from errors deteriorates
Solution Approach 1:
By segmenting the sample into individual molecular clones and analyzing each separately, the method can detect rare mutations with high precision while maintaining reliability. Each clone serves as an independent unit of analysis, allowing rare mutations to be identified with confidence based on their presence in specific clones rather than being lost in the noise of pooled sequencing data
Solution Approach 2:
The method creates multiple copies (clones) of individual DNA molecules and sequences each copy separately. For genuine mutations, the same variant will be observed in multiple independent clones, providing statistical confidence. For errors, the variant will typically appear in only one clone, allowing discrimination between true mutations and artifacts
3Device complexity
If traditional sequencing methods are used without cloning, then the process complexity is reduced, but the ability to correct systematic errors deteriorates
Solution Approach 1:
The method segments the sequencing process into individual clone analyses, which adds a layer of complexity but enables systematic error correction. By treating each clone separately and comparing results across multiple clones, the method can identify and correct systematic errors while maintaining reasonable process complexity through automated analysis pipelines
Data Source
AI summary
Described herein, among other things, is a method of sequencing, comprising: concatenating a plurality of fragments of genomic DNA to produce concatenated DNA; sequencing the concatenated DNA to produce a plurality of sequence reads, wherein at least some of the sequence reads comprise: at least the sequence of the 3′ and/or 5′ ends of a fragment that corresponds to the locus of interest and sequence of one or both of the fragments that flank the fragment in the concatenated DNA; and grouping the sequence reads that corresponds to the locus of interest using, for each of the grouped sequence reads: the 3′ and/or 5′ end sequences; and/or the flanking sequence.