Development of FuGO: An Ontology for Functional Genomics Investigations

Whetzel, Patricia L.; Brinkman, Ryan R.; Causton, Helen C.; Fan, Liju; Field, Dawn; Fostel, Jennifer; Fragoso, Gilberto; Gray, Tanya; Heiskanen, Mervi; Hernandez‐Boussard, Tina; Morrison, Norman; Parkinson, Helen; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Schober, Daniel; Smith, Barry; Stevens, Robert; Stoeckert, Christian J.; Taylor, Chris; White, Julie G.; Wood, Andrew J.

doi:10.1089/omi.2006.10.199

Cited by 52 publications

(27 citation statements)

References 11 publications

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“…Others have presented methodologies for building nutrigenomics ontologies using CMAPs (Castro et al 2006). The project documented by Castro et al has transitioned into Functional Genomics Ontology (FuGO), and concomitantly lost its specificity for characterization of nutrient-gene interaction data, information, and knowledge (Whetzel et al 2006). The project presented in this paper is part of a larger effort consisting of two distinct methodologies: a manual curation component and a computational component.…”

Section: Introductionmentioning

confidence: 99%

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

et al. 2014

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Advances in the development of bioinformatic tools continue to improve investigators' ability to interrogate, organize, and derive knowledge from large amounts of heterogeneous information. These tools often require advanced technical skills not possessed by life scientists. User-friendly, low-barrier-to-entry methods of visualizing nutrigenomics information are yet to be developed. We utilized concept mapping software from the Institute for Human and Machine Cognition to create a conceptual model of diet and health-related data that provides a foundation for future nutrigenomics ontologies describing published nutrient-gene/polymorphism-phenotype data. In this model, maps containing phenotype, nutrient, gene product, and genetic polymorphism interactions are visualized as triples of two concepts linked together by a linking phrase. These triples, or ''knowledge propositions,'' contextualize aggregated data and information into easy-to-read knowledge maps. Maps of these triples enable visualization of genes spanning the One-Carbon Metabolism (OCM) pathway, their sequence variants, and multiple literature-mined associations including concepts relevant to nutrition, phenotypes, and health. The concept map development process documents the incongruity of information derived from pathway databases versus literature resources. This conceptual model highlights the importance of incorporating information about genes in upstream pathways that provide substrates, as well as downstream pathways that utilize products of the pathway under investigation, in this case OCM. Other genes and their polymorphisms, such as TCN2 and FUT2, although not directly involved in OCM, potentially alter OCM pathway functionality. These upstream gene products regulate substrates such as B12. Constellations of polymorphisms affecting the functionality of genes along OCM, together with substrate and cofactor availability, may impact resultant phenotypes. These conceptual maps provide a foundational framework for development of nutrient-gene/polymorphism-phenotype ontologies and systems visualization.

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Section: Introductionmentioning

confidence: 99%

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

et al. 2014

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“…Our SNRNASM classification depends on several ontologies, including the Ontology of Biomedical Investigations (OBI) (Whetzel et al 2006), the Chemical Entities for Biomedical Investigations (CHEBI) (Degtyarenko et al 2008(Degtyarenko et al , 2009, the Protein Ontology (PRO) (Natale et al 2007(Natale et al , 2011, and the RNA Ontology (RNAO) (Leontis et al 2006;Brown et al 2009;Hoehndorf et al 2011). SNRNASM experiments are described as assays in OBI, with the input being the nucleic acid and the chemical or enzymatic probe, while the output is a measurement of reactivity.…”

Section: Discussionmentioning

confidence: 99%

“…Although this may seem obvious to those familiar with the field of RNA structure mapping, in the larger context of integrating multiple data sets for meta-analyses, it is essential to identify these elementary relationships explicitly. This strategy greatly facilitates the implementation of automated data meta-analyses algorithms (Leontis et al 2006;Whetzel et al 2006;Moreira and Musen 2007 (Degtyarenko et al 2008(Degtyarenko et al , 2009, and Protein Ontology (PRO) (Natale et al 2007(Natale et al , 2011. Additionally, for each entry, we have provided alternative names (for example, NMIA structure mapping is commonly known as SHAPE), and corresponding primary references.…”

Section: Classification Of Snrnasm Assaysmentioning

confidence: 99%

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Sharing and archiving nucleic acid structure mapping data

Rocca-Serra

Bellaousov

Birmingham

et al. 2011

RNA

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Nucleic acids are particularly amenable to structural characterization using chemical and enzymatic probes. Each individual structure mapping experiment reveals specific information about the structure and/or dynamics of the nucleic acid. Currently, there is no simple approach for making these data publically available in a standardized format. We therefore developed a standard for reporting the results of single nucleotide resolution nucleic acid structure mapping experiments, or SNRNASMs. We propose a schema for sharing nucleic acid chemical probing data that uses generic public servers for storing, retrieving, and searching the data. We have also developed a consistent nomenclature (ontology) within the Ontology of Biomedical Investigations (OBI), which provides unique identifiers (termed persistent URLs, or PURLs) for classifying the data. Links to standardized data sets shared using our proposed format along with a tutorial and links to templates can be found at http:// snrnasm.bio.unc.edu.

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“…The OBO Foundry also addresses housekeeping needs for ontology maintenance and editing, recommending, among other things Ontology Web Language (OWL, http:// www.w3.org/2003/08/owlfaq) and OBO as the format for distribution. The OWG directly participates in OBI (previously titled FuGO, http://obi.sourceforge.net, Whetzel et al 2006), an international collaborative project, initiated in 2005, which aims to build a cross-domain ontology as a resource for the annotation of biological, medical and environmental investigations. OBI is an OBO Foundry project set to provide terms that can be used to annotate investigations and the protocols, instrumentation and materials used in those investigations, along with the data generated and analyses performed.…”

Section: Phase 2-ontologymentioning

confidence: 99%

Metabolomics standards initiative: ontology working group work in progress

et al. 2007

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In this article we present the activities of the Ontology Working Group (OWG) under the Metabolomics Standards Initiative (MSI) umbrella. Our endeavour aims to synergise the work of several communities, where independent activities are underway to develop terminologies and databases for metabolomics investigations. We have joined forces to rise to the challenges associated with interpreting and integrating experimental process and data across disparate sources (software and databases, private and public). Our focus is to support the activities of the other MSI working groups by developing a common semantic framework to enable metabolomics-user communities to consistently annotate the experimental process and to enable meaningful exchange of datasets. Our work is accessible via a public webpage and a draft ontology has been posted under the Open Biological Ontology umbrella. At the very outset, we have agreed to minimize duplications across omics domains through extensive liaisons with other communities under the OBO Foundry. This is work in progress and we welcome new participants willing to volunteer their time and expertise to this open effort.

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Development of FuGO: An Ontology for Functional Genomics Investigations

Cited by 52 publications

References 11 publications

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Sharing and archiving nucleic acid structure mapping data

Metabolomics standards initiative: ontology working group work in progress

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