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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 con- sequences of CVs, because predicting the functional ef- fects of CVs and their relevance to disease phenotypes is becoming increasingly important. This article surveys and compares variation databases and in silico pre- diction programs that assess the effects of CVs on pro- tein function. We also introduce a combinatorial ap- proach that uses machine learning algorithms to im- prove prediction performance.

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