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An evidence-based approach to identify aging-related genes in Caenorhabditis elegans.

Callahan A, Cifuentes JJ, Dumontier M - BMC Bioinformatics (2015)

Bottom Line: Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior.To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans.We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood.

View Article: PubMed Central - PubMed

Affiliation: Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford California, AC, USA. acallaha@stanford.edu.

ABSTRACT

Background: Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans.

Results: We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation.

Conclusions: We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org .

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Fig7: SPARQL query for DEF7.

Mentions: DEF7 evaluates whether the gene product is associated with any other aging-linked proteins by first calling DRF12 to retrieve PPIs involving protein products of the gene of interest, and then calling DRF9 to retrieve GO process annotations for the interacting proteins. It processes the retrieved data and returns TRUE if the interacting protein’s GO annotation is related to aging processes (specifically: ‘aging’ – GO:0007568; ‘cell aging’ – GO:0007569; ‘age-dependent behavioral decline’ – GO:0035982; ‘multicellular organismal aging’ – GO:0010259; ‘determination of adult lifespan’ – GO:0008340) and if the experimental method associated with the PPI is one of a set of high-confidence detection methods (only a subset are shown below), and FALSE otherwise (Figure 7). For the sams-1 gene, DEF7 returns FALSE, which is converted to an evaluation score of 0. The complete list as well as a description of each of the PPI detection methods used as VALUE filters in DEF7 is provided in Additional file 1: Table S9.Figure 7


An evidence-based approach to identify aging-related genes in Caenorhabditis elegans.

Callahan A, Cifuentes JJ, Dumontier M - BMC Bioinformatics (2015)

SPARQL query for DEF7.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4339751&req=5

Fig7: SPARQL query for DEF7.
Mentions: DEF7 evaluates whether the gene product is associated with any other aging-linked proteins by first calling DRF12 to retrieve PPIs involving protein products of the gene of interest, and then calling DRF9 to retrieve GO process annotations for the interacting proteins. It processes the retrieved data and returns TRUE if the interacting protein’s GO annotation is related to aging processes (specifically: ‘aging’ – GO:0007568; ‘cell aging’ – GO:0007569; ‘age-dependent behavioral decline’ – GO:0035982; ‘multicellular organismal aging’ – GO:0010259; ‘determination of adult lifespan’ – GO:0008340) and if the experimental method associated with the PPI is one of a set of high-confidence detection methods (only a subset are shown below), and FALSE otherwise (Figure 7). For the sams-1 gene, DEF7 returns FALSE, which is converted to an evaluation score of 0. The complete list as well as a description of each of the PPI detection methods used as VALUE filters in DEF7 is provided in Additional file 1: Table S9.Figure 7

Bottom Line: Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior.To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans.We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood.

View Article: PubMed Central - PubMed

Affiliation: Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford California, AC, USA. acallaha@stanford.edu.

ABSTRACT

Background: Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans.

Results: We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation.

Conclusions: We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org .

Show MeSH