Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis.
Since the initial sequencing of the human genome, many projects are underway to understand the effects of genetic variation between individuals. Predicting and understanding the downstream effects of genetic variation using computational methods are becoming increasingly important for single nucleotide polymorphism (SNP) selection in genetics studies and understanding the molecular basis of disease. According to the NIH, there are now more than four million validated SNPs in the human genome. The volume of known genetic variations lends itself well to an informatics approach. Bioinformaticians have become very good at functional inference methods derived from functional and structural genomics. This review will present a broad overview of the tools and resources available to collect and understand functional variation from the perspective of structure, expression, evolution and phenotype. Additionally, public resources available for SNP identification and characterisation are summarised.
Mooney
Sean
S
Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, 714 N Senate Ave; EF 250, Indianapolis, IN 46202, USA. sdmooney@iupui.edu
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods* / Q000639:trends; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D004252:DNA Mutational Analysis / Q000379:methods* / Q000639:trends; D014644:Genetic Variation / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods* / Q000639:trends; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000639:trends; D012984:Software; D014584:User-Computer Interface