Computational tools for prioritizing candidate genes: boosting disease gene discovery
Computational tools for prioritizing candidate genes: boosting disease gene discovery
- Nature Reviews Genetics, 13, p.523-536, 2012 .
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.
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.