Variant Haplotype Reconstruction for NGS Annotation Matching
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Solution Overview
Problem
Current methods for identifying genomic variants in NGS workflows are inefficient, particularly in large-scale genomic analysis for clinical applications, as they fail to effectively automate the comparison of variant representations across different sequencing technologies and databases, leading to suboptimal computational resources and manual processing.
Innovation Solution
A method and genomic data analyzer that reconstructs patient and reference variant haplotypes over extended genomic regions to identify matches, allowing for automated variant annotation and matching across diverse sequencing setups, using a variant annotation module to align and compare genomic data with medical reference databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated variant matching methods are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The variant matching process is divided into distinct segments: haplotype reconstruction from patient variants, haplotype reconstruction from database variants, and comparison of reconstructed haplotypes. This segmentation allows each step to be optimized independently and processed efficiently, improving productivity while managing computational complexity through modular design.
Solution Approach 2:
The method performs preliminary haplotype reconstruction for both patient and database variants before conducting the actual comparison. By pre-processing the variant data into haplotype representations, the system reduces the complexity of the subsequent matching step and enables faster identification of matches, thereby improving overall productivity.
2Reliability
If comprehensive variant database searching is performed, then reliability is improved, but loss of time increases
Solution Approach 1:
Instead of directly comparing raw variant data between databases and patient samples, the method creates copied representations in the form of reconstructed haplotypes. This copying approach allows for more flexible and accurate comparisons while reducing the time required for direct variant-by-variant matching, thus improving reliability without proportionally increasing processing time.
Solution Approach 2:
The method transforms variant data from one parameter representation (individual variants with coordinates) to another parameter representation (haplotype sequences). This parameter change enables more effective comparison algorithms that can identify matches more quickly and accurately, balancing reliability improvement with reduced processing time.
Data Source
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AI summary
A genomic data analyzer workflow flow may be configured to identify in a patient sample, with a variant annotation module on at least one data processor, variants from at least one medical variant reference database, a variant v being defined by a start coordinate s(v), an end coordinate e(v) and a string alt(v) of a genomic variation relative to a human genome reference string R. In particular, the workflow may reconstruct, over an extended genomic region coordinate range a..b, a database variant haplotype string ha..b(d) for a reference variant database entry d as the concatenation of three genomic information strings RPleft + alt(d) + RPright, wherein alt(d) is the alternative string characterizing the variant d, RPleft is a string corresponding to extended genomic region on the left of the variant alt(d) in the reference human genome R and RPright is a string corresponding to extended genomic region on the right of the variant alt(d) in the reference human genome R.