Barcoded Cell-Free DNA Analysis for Rare Mutation and CNV Profiling
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Solution Overview
Problem
Existing methods for detecting and monitoring genetic diseases using cell-free DNA are limited in their ability to accurately characterize genetic aberrations such as copy number variation and rare mutations, necessitating improved techniques for sequencing and analysis.
Innovation Solution
A method involving sequencing extracellular polynucleotides with unique barcodes, filtering reads, mapping to a reference sequence, normalizing and comparing read counts in predefined regions, and identifying rare variants or mutations, combined with multiplex sequencing and data analysis using methods like hidden Markov and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sequencing methods are used to detect genetic aberrations in cell-free DNA, then the overall detection capability is maintained, but the accuracy and sensitivity for detecting rare mutations and copy number variation are insufficient
Solution Approach 1:
The genome is divided into predefined regions (bins) for systematic analysis of copy number variation. Each region is independently processed and analyzed, allowing detection of subtle changes in specific genomic locations. This segmentation enables precise measurement of read counts across the genome, improving detection accuracy for rare mutations and copy number aberrations while maintaining overall reliability through comprehensive coverage.
Solution Approach 2:
Unique barcodes are attached to extracellular polynucleotides before sequencing to enable later identification and quantification of specific molecules. This preliminary tagging allows for accurate tracking and counting of reads originating from specific genomic regions, facilitating precise detection of rare mutations and copy number variation by distinguishing true signals from background noise.
2Measurement precision
If sequencing depth is increased to improve detection of rare variants, then sensitivity increases, but the complexity of data analysis and processing increases
Solution Approach 1:
The analysis pipeline is segmented into distinct functional steps: barcode attachment, sequencing, read filtering, mapping to reference sequence, quantification in predefined regions, normalization, and variation detection. This modular segmentation allows each component to be optimized independently, managing overall system complexity while enabling high-sensitivity detection through deep sequencing of individual regions.
Solution Approach 2:
Unique barcodes serve as intermediaries between the sequenced polynucleotides and the analysis system. These barcodes enable efficient grouping and counting of reads from specific genomic regions, simplifying the data processing complexity while maintaining the ability to detect rare variants through aggregated signal analysis across multiple reads.
3Measurement precision
If normalization and comparison to control samples are performed, then detection accuracy improves, but the time required for analysis increases
Solution Approach 1:
Control samples are processed and normalized data are generated in advance, creating reference profiles that can be quickly compared against test samples. This preliminary preparation of control data enables rapid normalization and comparison during actual analysis, improving detection accuracy while minimizing the time required for each individual sample processing step.
Data Source
AI summary
The present disclosure provides a system and method for the detection of rare mutations and copy number variations in cell free polynucleotides. Generally, the systems and methods comprise sample preparation, or the extraction and isolation of cell free polynucleotide sequences from a bodily fluid; subsequent sequencing of cell free polynucleotides by techniques known in the art; and application of bioinformatics tools to detect rare mutations and copy number variations as compared to a reference. The systems and methods also may contain a database or collection of different rare mutations or copy number variation profiles of different diseases, to be used as additional references in aiding detection of rare mutations, copy number variation profiling or general genetic profiling of a disease.