A guide to web tools to prioritize candidate genes

Tranchevent, Léon-Charles; Capdevila, Francisco Bonachela; Nitsch, Daniela; Moor, Bart De; Causmaecker, Patrick De; Moreau, Yves

doi:10.1093/bib/bbq007

Cited by 170 publications

(140 citation statements)

References 59 publications

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“…Many bioinformatics tools have been developed to prioritize candidate disease genes from disease gene loci. [14][15][16] Candidate gene selection is however critically dependent on prior knowledge and only few disease genes have been identified by specifically using these bioinformatics tools. 17 A particular category of diseases that has remained largely unresolved is that of the rare genetic disorders that occur sporadically, and for which neither a family-based approach nor an association-based approach can be used.…”

Section: Traditional Disease Gene Identificationmentioning

confidence: 99%

Disease gene identification strategies for exome sequencing

et al. 2012

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Section: Traditional Disease Gene Identificationmentioning

confidence: 99%

Disease gene identification strategies for exome sequencing

et al. 2012

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“…Next, different gene prioritization tools (Endeavour, GeneDistiller, GeneWanderer, PosMed, SUSPECTS, and ToppGene) were used to identify candidate genes in the selected regions. 15 For this purpose, known DSD genes were used as training genes (Supplementary Data and Supplementary Table S1 online).…”

Section: Genetic Studiesmentioning

confidence: 99%

Biallelic and monoallelic ESR2 variants associated with 46,XY disorders of sex development

Baetens

Güran

Mendonça

et al. 2018

Genetics in Medicine

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Purpose: Disorders or differences of sex development (DSDs) are rare congenital conditions characterized by atypical sex development. Despite advances in genomic technologies, the molecular cause remains unknown in 50% of cases.Methods: Homozygosity mapping and whole-exome sequencing revealed an ESR2 variant in an individual with syndromic 46,XY DSD. Additional cases with 46,XY DSD underwent whole-exome sequencing and targeted next-generation sequencing of ESR2. Functional characterization of the identified variants included luciferase assays and protein structure analysis. Gonadal ESR2 expression was assessed in human embryonic data sets and immunostaining of estrogen receptor-β (ER-β) was performed in an 8-week-old human male embryo.

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“…Disease gene prediction, the task of identifying the most plausible candidate disease genes, is an important issue in biomedical research and many studies have been done for this [1,2]. Identification of disease-associated genes also leads to more effective researches about therapies for genetic diseases and gradually approaches a future of personalized medicine [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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Finding underlying molecular mechanisms of diseases is one of the important issues in biomedical research. In which, prediction of novel disease-associated genes is mostly focused. Many methods have been proposed based on biological networks and shown effectively for the problem. These network-based methods are usually relied on a "disease module" principle that functionally similar genes are associated with similar phenotypes or diseases. Among them, methods solely based on gene/protein networks only exploit that principle by structural modules in the gene/protein networks. Meanwhile, others based on integration of these networks with a disease similarity network better exploit the principle and consequently result in higher prediction performance. In these studies, the disease similarity network is extracted from a disease similarity matrix which was calculated using text mining techniques on OMIM records. Considering that diseases have been recently well annotated by human phenotype ontology (i.e., a controlled vocabulary database) and semantic similarity measures can be used to calculate similarities among them. Therefore, it would be more accurate to construct disease similarity network based on semantic similarity measures on phenotype ontol- Experiment results show that the ontology-based disease similarity network much improves the prediction performance compared to the one based on OMIM records, irrespective of gene/protein networks. In addition, we show ability of our method in predicting novel Alzheimer's disease-associated genes, in which 19 out of top 100 ranked candidate genes are supported with evidences from literature.

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A guide to web tools to prioritize candidate genes

Cited by 170 publications

References 59 publications

Disease gene identification strategies for exome sequencing

Disease gene identification strategies for exome sequencing

Biallelic and monoallelic ESR2 variants associated with 46,XY disorders of sex development

Ontology-based disease similarity network for disease gene prediction

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