{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T21:57:33Z","timestamp":1697493453878},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2220,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification.<\/jats:p>\n               <jats:p>Results: Here, we report a large-scale analysis of spatial, i.e. 3D, gene-expression data from an entire organ (the mouse brain) for the purpose of evaluating and ranking positional candidate genes, showing that the spatial gene-expression patterns can be successfully exploited for the prediction of gene\u2013phenotype associations not only for mouse phenotypes, but also for human central nervous system-related Mendelian disorders. We apply our method to the case of X-linked mental retardation, compare the predictions to the results obtained from a previous large-scale resequencing study of chromosome X and discuss some promising novel candidates.<\/jats:p>\n               <jats:p>Contact: \u00a0rosario.piro@unito.it<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq396","type":"journal-article","created":{"date-parts":[[2010,9,7]],"date-time":"2010-09-07T17:41:46Z","timestamp":1283881306000},"page":"i618-i624","source":"Crossref","is-referenced-by-count":12,"title":["Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR"],"prefix":"10.1093","volume":"26","author":[{"given":"Rosario M.","family":"Piro","sequence":"first","affiliation":[{"name":"Molecular Biotechnology Center and Department of Genetics, Biology and Biochemistry, University of Torino, Via Nizza 52, 10126 Torino, Italy"}]},{"given":"Ivan","family":"Molineris","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology Center and Department of Genetics, Biology and Biochemistry, University of Torino, Via Nizza 52, 10126 Torino, Italy"}]},{"given":"Ugo","family":"Ala","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology Center and Department of Genetics, Biology and Biochemistry, University of Torino, Via Nizza 52, 10126 Torino, Italy"}]},{"given":"Paolo","family":"Provero","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology Center and Department of Genetics, Biology and Biochemistry, University of Torino, Via Nizza 52, 10126 Torino, Italy"}]},{"given":"Ferdinando","family":"Di Cunto","sequence":"additional","affiliation":[{"name":"Molecular Biotechnology Center and Department of Genetics, Biology and Biochemistry, University of Torino, Via Nizza 52, 10126 Torino, Italy"}]}],"member":"286","published-online":{"date-parts":[[2010,9,4]]},"reference":[{"key":"2023012508262140100_B1","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1038\/nbt1203","article-title":"Gene prioritization through genomic data fusion","volume":"24","author":"Aerts","year":"2006","journal-title":"Nat. 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