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Nat Rev Genet. 2012 Jul 3;13(8):523-36. doi: 10.1038/nrg3253.

Computational tools for prioritizing candidate genes: boosting disease gene discovery.

Author information

1
Department of Electrical Engineering ESAT-SCD and IBBT-KU Leuven Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. yves.moreau@esat. kuleuven.be

Abstract

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.

PMID:
22751426
DOI:
10.1038/nrg3253
[Indexed for MEDLINE]

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