Computational tools for prioritizing candidate genes: Boosting disease gene discovery

At different stages of any research project, molecular biologists need to choose-often somewhat arbitrarily, even after careful statistical data analysis-which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets-such as expression data, sequence information, functional annotation and the biomedical literature-allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers. © 2012 Macmillan Publishers Limited. All rights reserved.