Somatic Mutation Identification via Barcode Sequencing
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Solution Overview
Problem
Current nucleic acid sequencing technologies face challenges in efficiently identifying somatic mutations, particularly in cancerous tumors, due to the high complexity of sample preparation and the need for high-throughput, cost-effective methods that can process large volumes of data quickly to determine biological and diagnostic relevance.
Innovation Solution
A computer-implemented system and method for identifying somatic mutations using nucleic acid sequencing data, which involves obtaining sequence information from tumor and normal tissue samples, applying barcode sequences for multiplex analysis, and employing algorithms to distinguish between somatic variants and sequencing errors by analyzing coverage and error rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ultra-high throughput nucleic acid sequencing is used to process large numbers of samples in parallel, then productivity increases, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent applies segmentation by dividing the complex sequencing process into distinct modular components: sample preparation modules, barcode assignment modules, sequencing modules, and data analysis modules. Each module handles a specific aspect of the workflow, allowing parallel processing of multiple samples while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The patent uses barcode sequences as intermediaries to link biological samples to their corresponding sequencing data. These barcodes act as mediators that enable automated tracking and identification of samples throughout the high-throughput process, reducing the complexity of sample management and data association without limiting throughput.
2Productivity
If computational resources are increased to assemble and analyze large numbers of reads quickly, then productivity improves, but use of energy and device complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-processing sequencing reads through adaptive error correction and quality filtering before assembly and analysis. This preprocessing step reduces the computational burden on subsequent analysis stages by eliminating low-quality reads and correcting obvious errors early in the workflow, thereby reducing overall energy consumption while maintaining analysis speed.
Solution Approach 2:
The patent applies partial action by implementing selective assembly and analysis strategies that focus computational resources on high-priority or high-quality reads. Rather than processing all reads with equal computational intensity, the system applies varying levels of analysis depth based on read quality metrics and biological relevance, optimizing the balance between productivity and energy use.
3Measurement precision
If stringent filtering criteria are applied to distinguish somatic variants from sequencing errors, then measurement precision improves, but loss of information increases
Solution Approach 1:
The patent implements feedback mechanisms through iterative error modeling and quality assessment. The system continuously refines its understanding of sequencing errors by analyzing control samples and known variant databases, then uses this feedback to adjust filtering thresholds dynamically. This allows the system to maintain high precision while minimizing false negatives, as the filtering criteria are optimized based on actual performance data rather than static thresholds.
Solution Approach 2:
The patent applies parameter changes by adjusting filtering stringency based on multiple contextual parameters including read depth, base quality scores, mapping quality, and local sequence context. Rather than using a single fixed threshold, the system dynamically modifies filtering parameters based on the specific characteristics of each variant candidate and its surrounding genomic context, thereby maintaining precision while reducing information loss.
Data Source
AI summary
Systems and method for identifying somatic mutations can receive first and second sequence information, determine if a variant present in the first sequencing information is also present in the second sequence information, and identify variants present in the first sequence information are somatic mutations when the variant is either not present in the second sequence information or the presence of the variant in the second sequence information is likely due to a sequencing error.