Linked Life Data

18951440

Source:http://linkedlifedata.com/resource/pubmed/id/18951440

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2008-11-3
pubmed:abstractText
Genetic testing for mutations in high-risk cancer susceptibility genes often reveals missense substitutions that are not easily classified as pathogenic or neutral. Among the methods that can help in their classification are computational analyses. Predictions of pathogenic vs. neutral, or the probability that a variant is pathogenic, can be made based on: 1) inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest for almost any missense sequence variant; and 2) for many variants, structural features of wild-type and variant proteins. These in silico methods have improved considerably in recent years. In this work, we review and/or make suggestions with respect to: 1) the rationale for using in silico methods to help predict the consequences of missense variants; 2) important aspects of creating PMSAs that are informative for classification; 3) specific features of algorithms that have been used for classification of clinically-observed variants; 4) validation studies demonstrating that computational analyses can have predictive values (PVs) of approximately 75 to 95%; 5) current limitations of data sets and algorithms that need to be addressed to improve the computational classifiers; and 6) how in silico algorithms can be a part of the "integrated analysis" of multiple lines of evidence to help classify variants. We conclude that carefully validated computational algorithms, in the context of other evidence, can be an important tool for classification of missense variants.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1098-1004
pubmed:author
pubmed:copyrightInfo
(c) 2008 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1327-36
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
In silico analysis of missense substitutions using sequence-alignment based methods.
pubmed:affiliation
International Agency for Research on Cancer (IARC), Lyon, France. tavtigian@iarc.fr
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural