Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Genetic VariationBioinformatic Tools for Identifying Disease Gene and SNP Candidates

Genetic Variation: Bioinformatic Tools for Identifying Disease Gene and SNP Candidates [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Genetic VariationBioinformatic Tools for Identifying Disease Gene and SNP Candidates

Part of the Methods in Molecular Biology Book Series (volume 628)
Editors: Barnes, Michael R.; Breen, Gerome
Mooney, Sean D.; Krishnan, Vidhya G.; Evani, Uday S.
Genetic Variation — Feb 8, 2010
Download PDF
12 pages

Loading next page...
 
/lp/springer-journals/genetic-variation-bioinformatic-tools-for-identifying-disease-gene-and-TR4wcuO9i6

References (75)

Publisher
Humana Press
Copyright
© Humana Press 2010
ISBN
978-1-60327-366-4
Pages
307–319
DOI
10.1007/978-1-60327-367-1_17
Publisher site
See Chapter on Publisher Site

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.]

Published: Feb 8, 2010

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

There are no references for this article.