title: POCUS: mining genomic sequence annotation to predict disease genes.
abstract: 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.
authors: MRC Human Genetics Unit, Crewe Road, Western General Hospital, Edinburgh EH4 2XU, UK.
authors: Frances S
authors: FS
authors: Turner
authors: Daniel R
authors: DR
authors: Clutterbuck
authors: Colin A M
authors: CA
authors: Semple
mesh_terms: D001321:Autistic Disorder / Q000235:genetics; D002352:Carrier Proteins / Q000235:genetics; D015816:Cell Adhesion Molecules, Neuronal; D019295:Computational Biology / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008565:Membrane Proteins / Q000235:genetics; D009154:Mutation; D009419:Nerve Tissue Proteins / Q000235:genetics; D011336:Probability; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D002352:Carrier Proteins; D015816:Cell Adhesion Molecules, Neuronal; D008565:Membrane Proteins; C430845:NLGN4X protein, human; D009419:Nerve Tissue Proteins; C098004:neuroligin 3