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SNP annotation
Single nucleotide polymorphism (SNP) annotation is the process to predict the effect or function of an individual SNP using SNP annotational tools. In SNP annotation the biological information is extracted, collected and displayed in a clear form amenable to query. SNP functional annotation is done base on the available information on nucleic acid and protein sequence.〔S. Aubourg, P. Rouzé, “Genome annotation”, Plant Physiol. Biochem, 2001, Vol 29, pp. 181−193〕
==Introduction==
Single nucleotide polymorphism plays an important role in genome wide association studies because they act as primary biomarker. SNPs are currently the marker of choice due to their large numbers in virtually all populations of individuals. The location of these biomarkers can be tremendously important in terms of predicting functional significance, genetic mapping and population genetics.〔Terry H. Shena, Christopher S. Carlsonb, Peter Tarczy-Hornoch, “SNPit: A federated data integration system for the purpose of functional SNP annotation”, Elsevier, 2009, Vol. 95, pp. 181–189〕 Each SNP represents a nucleotide change between two individuals at a defined location. SNPs are the most common genetic variant found in all individual with one SNP every 100–300 bp in some species.〔N. C. Oraguzie, E.H.A. Rikkerink, S.E. Gardiner, H.N. de Silva (eds.), “Association Mapping in Plants”, Springer, 2007〕 Due to the tremendous number of SNPs on the genome to expedite genotyping and analysis, there is a clear need to prioritize SNPs according to their potential effect
Annotating large numbers of SNPs is a difficult and complex process, which need computational method to handle such a large dataset. Many tools available have been developed for SNP annotation in different organism, some of them are optimized for use with organisms densely sampled for SNPs (such as humans), but there are currently few tools available that are species non-specific or support non-model organism data. The majority of SNPs annotation tools provide computationally predicted putative deleterious effects of SNPs. These tools examine whether a SNP resides in functional genomic regions such as exons, splice sites, or transcription regulatory sites, and predict the potential corresponding functional effects that the SNP may have using a variety of machine-learning approaches. But the tools and systems that prioritize functionally significant SNPs, suffer from few limitations: First, they examine the putative deleterious effects of SNPs with respect to a single biological function that provide only partial information about the functional significance of SNPs. Second, current systems classify SNPs into deleterious or neutral group.〔P. H. Lee, H. Shatkay, “Ranking single nucleotide polymorphisms by potential deleterious effects”, Computational Biology and Machine Learning Lab, School of Computing, Queen’s University, Kingston, ON, Canada〕
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