Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases

Gefen, Avitan; Cohen, Raphael; Birk, Ohad S.

doi:10.1002/humu.21171

Cited by 19 publications

(15 citation statements)

References 25 publications

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“…On the basis of our novel Syndrome to Gene (S2G) software, 8 using EIF2B1 as a reference gene, the 62 genes within the disease-associated locus were prioritized. No mutation was found in the coding region and flanking intron sequences of the top seven candidate genes (data not shown).…”

Section: Mutation Analysismentioning

confidence: 99%

The desmosterolosis phenotype: spasticity, microcephaly and micrognathia with agenesis of corpus callosum and loss of white matter

et al. 2011

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Desmosterolosis is a rare autosomal recessive disorder of elevated levels of the cholesterol precursor desmosterol in plasma, tissue and cultured cells. With only two sporadic cases described to date with two very different phenotypes, the clinical entity arising from mutations in 24-dehydrocholesterol reductase (DHCR24) has yet to be defined. We now describe consanguineous Bedouin kindred with four surviving affected individuals, all presenting with severe failure to thrive, psychomotor retardation, microcephaly, micrognathia and spasticity with variable degree of hand contractures. Convulsions near birth, nystagmus and strabismus were found in most. Brain MRI demonstrated significant reduction in white matter and near agenesis of corpus callosum in all. Genome-wide linkage analysis and fine mapping defined a 6.75 cM disease-associated locus in chromosome 1 (maximum multipoint LOD score of six), and sequencing of candidate genes within this locus identified in the affected individuals a homozygous missense mutation in DHCR24 leading to dramatically augmented plasma desmosterol levels. We thus establish a clear consistent phenotype of desmosterolosis (MIM 602398).

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Section: Mutation Analysismentioning

confidence: 99%

The desmosterolosis phenotype: spasticity, microcephaly and micrognathia with agenesis of corpus callosum and loss of white matter

et al. 2011

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“…The improved data is used to find similarity between syndromes in order to find candidate genes for hereditary syndromes as part of the S2G application [17]. The enhanced OMIM database we produced can be further used for bioinformatics purposes as a basis for identifying connections between syndromes.…”

Section: Discussionmentioning

confidence: 99%

“…S2G (Syndrome to Gene) [17], is a tool for finding candidate genes for hereditary syndromes in suspect loci. S2G is comprised of two parts: one for ranking genes in a locus in comparison to a known gene causing the syndrome, the other, for syndromes with no known genes uses our search application for choosing a known gene causing the most phenotypically similar syndrome.…”

Section: Introductionmentioning

confidence: 99%

CSI-OMIM - Clinical Synopsis Search in OMIM

et al. 2011

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BackgroundThe OMIM database is a tool used daily by geneticists. Syndrome pages include a Clinical Synopsis section containing a list of known phenotypes comprising a clinical syndrome. The phenotypes are in free text and different phrases are often used to describe the same phenotype, the differences originating in spelling variations or typing errors, varying sentence structures and terminological variants.These variations hinder searching for syndromes or using the large amount of phenotypic information for research purposes. In addition, negation forms also create false positives when searching the textual description of phenotypes and induce noise in text mining applications.DescriptionOur method allows efficient and complete search of OMIM phenotypes as well as improved data-mining of the OMIM phenome. Applying natural language processing, each phrase is tagged with additional semantic information using UMLS and MESH. Using a grammar based method, annotated phrases are clustered into groups denoting similar phenotypes. These groups of synonymous expressions enable precise search, as query terms can be matched with the many variations that appear in OMIM, while avoiding over-matching expressions that include the query term in a negative context. On the basis of these clusters, we computed pair-wise similarity among syndromes in OMIM. Using this new similarity measure, we identified 79,770 new connections between syndromes, an average of 16 new connections per syndrome. Our project is Web-based and available at http://fohs.bgu.ac.il/s2g/csiomimConclusionsThe resulting enhanced search functionality provides clinicians with an efficient tool for diagnosis. This search application is also used for finding similar syndromes for the candidate gene prioritization tool S2G.The enhanced OMIM database we produced can be further used for bioinformatics purposes such as linking phenotypes and genes based on syndrome similarities and the known genes in Morbidmap.

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“…Thus, these methods can only detect associations that are already known. The third type is similarity profiling and data fusion methods (Aerts et al, 2006;De Bie et al, 2007;Chen et al, 2009;Gefen et al, 2010;Li and Patra, 2010;Britto et al, 2012;Zitnik et al, 2015;Zakeri et al, 2015;Kim et al, 2015;Tranchevent et al, 2016;Kumar et al, 2018). This is the dominant type in the disease gene prioritization community and includes the famous Endeavour method (Aerts et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

Kuwahara

Yang

et al. 2019

Preprint

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Motivation: Proper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion. Results: Here, we propose a novel graph convolutional networkbased disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes. Availability: The main program and the data are available at https: //github.com/lykaust15/Disease_gene_prioritization_GCN.

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Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases

Cited by 19 publications

References 25 publications

The desmosterolosis phenotype: spasticity, microcephaly and micrognathia with agenesis of corpus callosum and loss of white matter

The desmosterolosis phenotype: spasticity, microcephaly and micrognathia with agenesis of corpus callosum and loss of white matter

CSI-OMIM - Clinical Synopsis Search in OMIM

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

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