Computational tools for prioritizing candidate genes: boosting disease gene discovery
- 3 July 2012
- journal article
- review article
- Published by Springer Science and Business Media LLC in Nature Reviews Genetics
- Vol. 13 (8), 523-536
- https://doi.org/10.1038/nrg3253
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.Keywords
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