Protocol

Genetic Variation

Volume 628 of the series Methods in Molecular Biology pp 307-319

Date:

Bioinformatic Tools for Identifying Disease Gene and SNP Candidates

  • Sean D. MooneyAffiliated withDepartment of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Email author 
  • , Vidhya G. KrishnanAffiliated withDepartment of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine
  • , Uday S. EvaniAffiliated withDepartment of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine

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Abstract

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.

Key words

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