In Silico Functional Assessment of Sequence Variations: Predicting Phenotypic Functions of Novel Variations.
- Author:
Hong Hee WON
1
;
Jong Won KIM
Author Information
1. Samsung Biomedical Research Institute, Samsung Medical Center, Seoul 135-710, Korea.
- Publication Type:Review
- Keywords:
sequence variation;
amino acid substitution;
nonsynonymous single nucleotide polymorphism;
missense mutation;
prediction;
protein function
- MeSH:
Amino Acid Substitution;
Computer Simulation;
Genome;
Genome, Human;
Humans;
Mutation, Missense;
Phenotype;
Machine Learning
- From:Genomics & Informatics
2008;6(4):166-172
- CountryRepublic of Korea
- Language:English
-
Abstract:
A multitude of protein-coding sequence variations (CVs) in the human genome have been revealed as a result of major initiatives, including the Human Variome Project, the 1000 Genomes Project, and the International Cancer Genome Consortium. This naturally has led to debate over how to accurately assess the functional consequences of CVs, because predicting the functional effects of CVs and their relevance to disease phenotypes is becoming increasingly important. This article surveys and compares variation databases and in silico prediction programs that assess the effects of CVs on protein function. We also introduce a combinatorial approach that uses machine learning algorithms to improve prediction performance.
