Bioinformatic tools for identifying disease gene and SNP candidates

  • Mooney S
  • Krishnan V
  • Evani U
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As databases of genome data continue to grow, our understanding of the functional elements of the genome grows as well. Many genetic changes in the genome have now been discovered and characterized, including both disease-causing mutations and neutral polymorphisms. In addition to experimental approaches to characterize specific variants, over the past decade, there has been intense bioinformatic research to understand the molecular effects of these genetic changes. In addition to genomic experimental assays, the bioinformatic efforts have focused on two general areas. First, researchers have annotated genetic variation data with molecular features that are likely to affect function. Second, statistical methods have been developed to predict mutations that are likely to have a molecular effect. In this protocol manuscript, methods for understanding the molecular functions of single nucleotide polymorphisms (SNPs) and mutations are reviewed and described. The intent of this chapter is to provide an introduction to the online tools that are both easy to use and useful.

Author-supplied keywords

  • Bioinformatics
  • Candidate gene
  • Genetic disease
  • Genome
  • Machine learning
  • SNP
  • Single nucleotide polymorphism

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  • Sean D. Mooney

  • Vidhya G. Krishnan

  • Uday S. Evani

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