Computational tools for prioritizing candidate genes: boosting disease gene discovery

Tipo de material: Texto

TextoSeries ; Nature Reviews Genetics, 13, p.523-536, 2012Trabajos contenidos:

Yves, Moreau
Tranchevent, L-C

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Resumen: 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.

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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.

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