Genomic Structural Variation Detection via Multi-Reference K-Mer Analysis
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Solution Overview
Problem
Current genomic structural variation analysis programs are limited in detecting various types of structural variations, particularly in cancer-related cases, and fail to accurately account for racial differences in genome sequences, leading to false positives or false negatives.
Innovation Solution
A method and apparatus that utilize a multi-reference genome approach to detect genomic structural variations by comparing sample sequence data to multi-reference genome data, determining k-mer reads not included in the reference genome, and predicting structural variation types based on sequence mapping patterns and breakpoints, effectively addressing racial sequence differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-reference genome analysis programs are used, then the analysis process is simple, but detection accuracy decreases due to racial sequence differences being misinterpreted as structural variations
Solution Approach 1:
The invention segments the reference genome into multiple racial-specific reference genomes (e.g., Asian, European, African). Instead of using a single universal reference genome, the system divides the reference into race-specific segments, allowing sample sequences to be compared against the most appropriate racial reference. This segmentation resolves the contradiction by improving detection accuracy through racial matching while managing complexity through modular program design that can select appropriate references based on sample characteristics.
Solution Approach 2:
The invention changes the parameter of reference genome selection from a fixed single reference to a variable multi-reference system. The system dynamically selects or weights multiple reference genomes based on racial ancestry parameters of the sample. This parameter change allows the analysis to adapt to different racial backgrounds, improving detection accuracy while the computational framework manages the complexity of handling multiple references through systematic parameter-based selection.
2Adaptability or versatility
If conventional structural variation prediction methods are used, then the computational complexity is lower, but the ability to detect all types of structural variations is limited
Solution Approach 1:
The invention creates a universal analysis framework that can detect multiple types of structural variations (deletions, duplications, insertions, inversions, translocations) using a unified multi-reference genome approach. The system is designed to perform multiple detection functions simultaneously by comparing sample sequences against multiple racial references, enabling versatile structural variation detection while managing algorithmic complexity through a standardized computational pipeline that handles all variation types consistently.
3Reliability
If multi-reference genome data is used, then racial sequence differences are properly accounted for, but the computational resources and analysis complexity increase
Solution Approach 1:
The invention performs preliminary actions by pre-processing and organizing multiple reference genomes into an efficient data structure before actual sample analysis. The system pre-calculates and stores racial-specific reference sequences, creating a ready-to-use multi-reference library that can be quickly accessed during sample analysis. This preliminary preparation reduces the computational burden during actual detection while maintaining high reliability through comprehensive racial reference coverage.
Solution Approach 2:
The invention introduces an intermediary component that manages the comparison between sample sequences and multiple reference genomes. This intermediary layer handles the complexity of multi-reference comparison by implementing efficient algorithms that systematically evaluate sample sequences against multiple racial references, mediating between the raw computational complexity and the final reliable detection results. The intermediary manages memory usage, comparison strategies, and result integration to maintain reliability while controlling computational demands.
Data Source
AI summary
Disclosed is a method of detecting a genomic structural variation based on k-mer set in a reference genome by means of a computer apparatus, the method including receiving sample sequence data, comparing the sample sequence data to k-mer set in reference genome data to determine at least one k-mer read that is not included in the reference genome data among reads of the sample sequence data, determining a breakpoint and a candidate region of a structural variation by mapping the at least one k-mer read to standard reference genome data, and predicting a structural variation type for the sample sequence data on the basis of a sequence mapping pattern and the breakpoint corresponding to the mapping result.