Single nucleotide polymorphisms (SNPs) play an important role in genome wide association studies because they act as primary biomarkers. 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. 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. Since there is a massive number of SNPs on the genome, there is a clear need to prioritize SNPs according to their potential effect in order to expedite genotyping and analysis.
Annotating large numbers of SNPs is a difficult and complex process, which need computational methods to handle such a large dataset. Many tools available have been developed for SNP annotation in different organisms: 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 SNP 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.
Many annotation algorithms focus on single nucleotide variants (SNVs), considered more rare than SNPs as defined by their minor allele frequency (MAF).  As a consequence, training data for the corresponding prediction methods may be different and hence one should be careful to select the appropriate tool for a specific purpose.  For the purposes of this article, "SNP" will be used to mean both SNP and SNV, but readers should bear in mind the differences.