Syndrome-Based Discrimination of Single Nucleotide Polymorphism

The ability to discriminate nucleic acid se- quences is necessary for a wide variety of applications: high throughput screening, distinguishing genetically modified organisms (GMOs), molecular computing, differentiating bi- ological markers, fingerprinting a specific sensor response for complex systems, etc. Hybridization-based target recogni- tion and discrimination is central to the operation of nucleic acid sensor systems. Therefore developing a quantitative correlation between mishybridization events and sensor out- put is critical to the accurate interpretation of results. In this work, using experimental data produced by intro- ducing single mutations (single nucleotide polymorphisms, SNPs) in the probe sequence of computational catalytic molecular beacons (deoxyribozyme gates) (1), we investigate coding theory algorithms for uniquely categorizing SNPs based on the calculation of syndromes.