Genomic Variant Identification via Modular Sequencing Analysis
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Solution Overview
Problem
Current nucleic acid sequencing technologies face challenges in efficiently detecting genomic variants, particularly in homopolymer regions, which are crucial for understanding genetic diseases, due to the high complexity and volume of data generated, requiring improved systems and methods for variant identification.
Innovation Solution
The development of a computer-implemented system and methods for identifying sequence variations using next-generation sequencing technologies, including barcode adaptors for multiplex sequencing, and advanced algorithms for variant calling and genetic disease analysis, enabling efficient detection of variants in complex genetic data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If next-generation sequencing technologies are used to increase throughput and reduce cost, then sequencing capacity and accessibility are improved, but data complexity and volume increase significantly
Solution Approach 1:
The patent segments the complex genomic data processing task into distinct computational modules: initial mapping of reads to reference genome, identification of candidate variant positions, and detailed variant calling. This modular approach allows each module to be optimized independently, managing the complexity introduced by high-throughput sequencing while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary reference genome sequence that mediates between the raw sequencing reads and the final variant identification. By mapping all reads against this reference, the system creates an intermediate representation that simplifies subsequent analysis, transforming the complex raw data into a structured format that is easier to process while preserving the high throughput capability.
2Measurement precision
If advanced algorithms are implemented to detect variants in homopolymer regions, then measurement precision is improved, but computational resource requirements increase
Solution Approach 1:
The patent performs preliminary actions by first mapping all sequencing reads to the reference genome and identifying candidate variant positions before conducting detailed variant calling. This preliminary filtering step reduces the computational burden on subsequent analysis by focusing resources only on relevant genomic regions and potential variants, rather than analyzing the entire genome data set in detail.
Solution Approach 2:
The patent applies different levels of analysis quality to different genomic regions. Homopolymer regions and areas with high variant density receive more sophisticated analysis with higher computational resources, while other regions use streamlined processing. This local quality approach maintains high measurement precision where needed while reducing overall computational resource requirements.
3Reliability
If comprehensive variant analysis is performed to identify all types of genetic variants, then reliability of disease diagnosis is improved, but analysis time increases
Solution Approach 1:
The patent implements continuous useful action by processing sequencing reads through a streamlined pipeline that performs mapping, candidate identification, and variant calling in an integrated manner. The system maintains continuous processing flow without unnecessary interruptions or redundant steps, allowing comprehensive variant analysis to be completed more efficiently while maintaining high diagnostic reliability through thorough analysis of all variant types.
Data Source
AI summary
Systems and method for identifying variants associated with a genetic disease can include obtaining sequencing reads for a plurality of individuals for a list of variant positions. The reads can be compared to identify variants that are found in affected individuals and absent in non-affected individuals. Such variants can include loss of heterozygosity, trans-phased compound heterozygotes, increased frequency mitochondrial variants, homozygous recessive variants, de novo variants, sex-linked variants, and combinations thereof.