Flow Space Alignment for Low Frequency Variant Detection
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Solution Overview
Problem
Current nucleic acid sequencing technologies face challenges in efficiently detecting low frequency genomic variants, as existing methods often misalign or miscall variants due to errors in sequencing, particularly in complex samples, leading to incomplete understanding of genetic components of complex traits and diseases.
Innovation Solution
The implementation of a computer-implemented system that utilizes Bayesian SNP calling and flow space alignment to improve variant detection, allowing for accurate identification of low frequency variants by analyzing sequence data in flow space and base space, and applying statistical modeling to determine the likelihood of variants without relying on perfect base alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional base alignment methods are used for variant detection, then the process is computationally simpler, but accuracy deteriorates due to misalignment and miscalling of low frequency variants
Solution Approach 1:
The patent introduces flow space as an intermediary representation between raw sequencing data and base alignment. By converting sequence data into flow space (a transformed coordinate system), the system enables accurate variant detection without relying on traditional base alignment methods. This intermediary representation preserves the information needed for accurate variant calling while avoiding the computational complexity and errors of base alignment.
2Measurement precision
If Bayesian SNP calling with flow space alignment is implemented, then variant detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary conversion of sequencing data into flow space before variant detection. By pre-processing the data into this optimized representation, the subsequent Bayesian SNP calling and variant detection steps become more efficient. The preliminary transformation enables the algorithm to work with a more compact and informative data structure, reducing the computational burden during the actual variant calling process.
3Productivity
If traditional sequencing analysis methods are used, then processing speed is faster, but detection capability for low frequency variants deteriorates
Solution Approach 1:
The patent fundamentally changes the parameter space by transforming sequence data from base space to flow space. This parameter transformation allows the system to detect low frequency variants that are invisible or indistinguishable in traditional base alignment approaches. The flow space representation highlights subtle variations in sequencing signals, enabling reliable detection of rare variants while maintaining computational efficiency through optimized algorithms designed for this parameter space.
Data Source
AI summary
Systems and method for determining variants can receive mapped reads, and call variants. In embodiments, flow space information for the reads can be aligned to a flow space representation of a corresponding portion of the reference. Reads spanning a position with a potential variant can be grouped and a score can be calculated for the variant. Based on the scores, a list of probable variants can be provided. In various embodiments, low frequency variants can be identified where multiple potential variants are present at a position.