Genomics Inform.  2008 Dec;6(4):166-172.

In Silico Functional Assessment of Sequence Variations: Predicting Phenotypic Functions of Novel Variations

• Won HH
• Kim JW

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.

Keyword

sequence variation; amino acid substitution; nonsynonymous single nucleotide polymorphism; missense mutation; prediction; protein function

MeSH Terms

Amino Acid Substitution
Computer Simulation
Genome
Genome, Human
Humans
Mutation, Missense
Phenotype
Machine Learning