ArticleBioinformatics (Oxford, England)2010
Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multimodal network diffusion predicts future disease-gene-chemical associations.Bioinformatics (Oxford, England) · 2019Article
- Brain transcriptome atlases: a computational perspective.Brain structure & function · 2017Review
- Combined neuroimaging and gene expression analysis of the genetic basis of brain plasticity indicates across species homology.Human brain mapping · 2014Article
- Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.BioMed research international · 2013Article
- The human brain online: an open resource for advancing brain research.PLoS biology · 2012Article
- Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method.TheScientificWorldJournal · 2012Article
- A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis.Biology direct · 2011Article
- Evaluation of candidate genes from orphan FEB and GEFS+ loci by analysis of human brain gene expression atlases.PloS one · 2011Article
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Authors and funding
5 authors.
Funding
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Abstract
motivationThe 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.
resultsHere, 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-phenotype 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.
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