POCUS: mining genomic sequence annotation to predict disease genes
| Autor: | Frances S, Turner, Daniel R, Clutterbuck, Colin A M, Semple |
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| Rok vydání: | 2003 |
| Předmět: |
Cell Adhesion Molecules
Neuronal Data_MISCELLANEOUS Computational Biology Membrane Proteins Method Nerve Tissue Proteins Sequence Analysis DNA ComputingMethodologies_PATTERNRECOGNITION Mutation Humans Genetic Predisposition to Disease ComputingMethodologies_GENERAL Autistic Disorder Carrier Proteins Probability |
| Zdroj: | Genome Biology |
| ISSN: | 1474-760X |
| Popis: | POCUS (prioritization of candidate genes using statistics) is a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrichment of real disease genes in the candidate-gene shortlists it produces compared with the original large sets of positional candidates. In contrast to existing methods, POCUS can also suggest counterintuitive candidates. |
| Databáze: | OpenAIRE |
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