Linked Life Data

20238089

Source:http://linkedlifedata.com/resource/pubmed/id/20238089

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-3-18
pubmed:abstractText
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.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1940-6029
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
628
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
307-19
pubmed:dateRevised
2010-7-19
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Bioinformatic tools for identifying disease gene and SNP candidates.
pubmed:affiliation
Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural