Alignment-free variant calling using k-mer density analysis
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Solution Overview
Problem
Alignment-based approaches for detecting human genome variants are computationally complex and time-consuming, especially with the increasing size of genomic data from Next Generation Sequencing, limiting their application on large-scale sequence data.
Innovation Solution
A server-based method that performs k-mer sequence analysis, including counting, filtering, and dimensionality reduction, to identify genetic variants such as SNPs, MNPs, and INDELs, using techniques like T-tests and neighbor detection, and merges k-mer pairs to reduce computational cost and enhance variant calling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alignment-based approaches are used for variant detection, then accuracy is improved for closely related sequences, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the genomic sequence analysis into k-mer units (subsequences of length k). Instead of aligning entire reads to the reference genome, the method divides sequences into smaller k-mer segments that can be counted and compared independently. This segmentation reduces the computational burden while maintaining variant detection accuracy through localized sequence frequency analysis.
Solution Approach 2:
The patent replaces the mechanical alignment process with a counting-based statistical approach. Instead of performing computationally intensive sequence alignment operations, the method counts k-mer frequencies in sample sequences and compares them against reference k-mer counts. This substitution of alignment mechanics with counting statistics dramatically reduces computational complexity while preserving variant detection capability.
2Reliability
If alignment-based approaches are used for variant detection, then reliable results are obtained for closely related sequences, but the method becomes time-consuming and limited for large-scale data
Solution Approach 1:
The patent performs preliminary k-mer counting on the reference genome to build a frequency distribution before analyzing sample sequences. By pre-computing reference k-mer frequencies and storing them in a lookup structure, the method eliminates the need for time-consuming alignment operations during sample analysis. Variant detection is achieved through rapid comparison of sample k-mer counts against the pre-computed reference, significantly reducing processing time while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameter from sequence alignment scores to k-mer frequency counts. Instead of measuring similarity through alignment algorithms that require significant computation time, the method uses k-mer frequency as the primary parameter for variant detection. This parameter change enables rapid processing of large-scale genomic data while preserving the ability to reliably identify variants through statistical comparison.
3Productivity
If k-mer density filtering is applied to identify variants, then computational cost is reduced, but the method must handle high-dimensional data efficiently
Solution Approach 1:
The patent transforms the high-dimensional k-mer frequency data into a lower-dimensional representation by projecting k-mer counts onto a reduced dimensional space. This dimensionality reduction allows efficient visualization and analysis of variant patterns while maintaining the essential information needed for accurate variant calling. The transformation from high-dimensional k-mer space to reduced dimensional space improves computational efficiency without sacrificing variant detection capability.
Data Source
AI summary
Disclosed is a methodology to find genetic sequence variations. In certain embodiments, a server receives a dataset comprised of a genetic sequence of control group RNA samples and experimental RNA samples and performs a count of unique k-mer sequences based on density values. Then, the server sorts the plurality of k-mer sequences based on their density values and applies a neighbor detection function to the plurality of k-mer sequences to identify one or more neighbor k-mer sequences to form one or more k-mer pair sequences. Then, the server filters the one or more k-mer pair sequences and merges the one or more filtered k-mer pair sequences into genetic variant candidates. The server then localizes the variant candidates in the reference genome to validate their existence and type and compares the plurality of genetic variant candidates against a variant database. The server then outputs the one or more identified sequence genetic variants.