Syndrome-based discrimination of single nucleotide polymorphism.
The ability to discriminate nucleic acid sequences is necessary for a wide variety of applications: high throughput screening, distinguishing genetically modified organisms (GMOs), molecular computing, differentiating biological markers, fingerprinting a specific sensor response for complex systems, etc. Hybridization-based target recognition 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 introducing 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.
May
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EE
Comput. Biol. Dept., Sandia Nat. Labs., Albuquerque, NM 87185, USA. eemay@sandia.gov
Dolan
P
P
Crozier
P
P
Brozik
S
S
D000465:Algorithms; D001499:Bayes Theorem; D015374:Biosensing Techniques; D002874:Chromosome Mapping; D004247:DNA / Q000235:genetics; D005822:Genetic Vectors; D006801:Humans; D015233:Models, Statistical; D009154:Mutation; D009693:Nucleic Acid Hybridization; D011110:Polymorphism, Genetic; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D013577:Syndrome; D013997:Time Factors
D004247:DNA