Candidate gene identification is typically labour intensive, involving 
laboratory experiments required to corroborate or disprove any hypothesis for a 
nominated candidate gene being considered the causative gene. The traditional 
approach to reduce the number of candidate genes entails fine-mapping studies 
using markers and pedigrees. Gene prioritization establishes the ranking of 
candidate genes based on their relevance to the biological process of interest, 
from which the most promising genes can be selected for further analysis. To 
date, many computational methods have focused on the prediction of candidate 
genes by analysis of their inherent sequence characteristics and similarity with 
respect to known disease genes, as well as their functional annotation. In the 
last decade, several computational tools for prioritizing candidate genes have 
been proposed. A large number of them are web-based tools, while others are 
standalone applications that install and run locally. This review attempts to 
take a close look at gene prioritization criteria, as well as candidate gene 
prioritization algorithms, and thus provide a comprehensive synopsis of the 
subject matter.