AN ANALOGY OF ALGORITHMS FOR TAGGING OF SINGLE NUCLEOTIDE POLYMORPHISM AND EVALUATION THROUGH LINKAGE DISEQUILIBRIUM
Keywords:
Explosive Growth, Revolutionary Technologies, Small Efficient DatasetAbstract
Recent years have seen an explosive growth in biological data. It should be managed and stored for various purposes. Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate and easy to comprehend genome information. Here comes the relevance of tagging data. From huge large DNA sequence information scientists needed some small efficient dataset that they can do their research on and that is exactly why some optimization needed to be carried upon these big data. A subset of SNPs that are selected to represent the original information embedded in the full set of SNPs is referred to as the set of Tag SNPs. Large sequencing projects are producing increasing quantities of nucleotide sequences. The contents of nucleotide databases are doubling in size approximately every fourteen months. So to track and analyze this amount of data scientists need some small set of data that can represent the whole database characteristically. So computer scientists came up with some innovative algorithms to find tag SNPs. We have done a comparative study by implementing the popular algorithms and evaluating them by scoring Linkage Disequilibrium
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