Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR

@article{Piro2010CandidateGP,
  title={Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR},
  author={Rosario M. Piro and Ivan Molineris and Ugo Ala and Paolo Provero and Ferdinando Di Cunto},
  journal={Bioinformatics},
  year={2010},
  volume={26},
  pages={i618 - i624}
}
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. Results: Here… 

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References

SHOWING 1-10 OF 45 REFERENCES
Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis
TLDR
The results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.
Gene prioritization through genomic data fusion
TLDR
A bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena, offers an alternative integrative method for gene discovery.
Association of genes to genetically inherited diseases using data mining
TLDR
A scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene indicates that for some diseases, the chance of identifying the underlying gene is higher.
Integration of text- and data-mining using ontologies successfully selects disease gene candidates
TLDR
This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.
Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics
TLDR
Data sets of RNA and protein expression are used to identify the gene causing Leigh syndrome, French-Canadian type (LSFC), a human cytochrome c oxidase deficiency that maps to chromosome 2p16-21, providing definitive genetic proof that LRPPRC indeed causes LSFC.
TOM: a web-based integrated approach for identification of candidate disease genes
TLDR
TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases, allows the geneticist to bypass the costly and time consuming tracing of genetic markers through entire families and might improve the chance of identifying disease genes, particularly for rare diseases.
Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data
TLDR
Combining gene expression data, functional annotation and known phenotype-gene associations the authors provide candidate genes for several genetic diseases of unknown molecular basis.
A gene atlas of the mouse and human protein-encoding transcriptomes.
The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a
Predicting disease genes using protein–protein interactions
TLDR
Exploiting protein–protein interactions can greatly increase the likelihood of finding positional candidate disease genes, and when applied on a large scale they can lead to novel candidate gene predictions.
A text-mining analysis of the human phenome
TLDR
It is found that similarity between phenotypes reflects biological modules of interacting functionally related genes, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein–protein interaction.
...
...