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Title: Modeling biochemical pathways in the gene ontology

Abstract

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.

Authors:
 [1];  [2];  [3];  [4];  [1];  [1]
  1. The Jackson Lab., Bar Harbor, ME (United States)
  2. NYU School of Medicine, New York, NY (United States)
  3. Phoenix Bioinformatics, Redwood City, CA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1377445
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Database
Additional Journal Information:
Journal Volume: 2016; Journal ID: ISSN 1758-0463
Publisher:
Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES

Citation Formats

Hill, David P., D’Eustachio, Peter, Berardini, Tanya Z., Mungall, Christopher J., Renedo, Nikolai, and Blake, Judith A.. Modeling biochemical pathways in the gene ontology. United States: N. p., 2016. Web. doi:10.1093/database/baw126.
Hill, David P., D’Eustachio, Peter, Berardini, Tanya Z., Mungall, Christopher J., Renedo, Nikolai, & Blake, Judith A.. Modeling biochemical pathways in the gene ontology. United States. doi:10.1093/database/baw126.
Hill, David P., D’Eustachio, Peter, Berardini, Tanya Z., Mungall, Christopher J., Renedo, Nikolai, and Blake, Judith A.. Thu . "Modeling biochemical pathways in the gene ontology". United States. doi:10.1093/database/baw126. https://www.osti.gov/servlets/purl/1377445.
@article{osti_1377445,
title = {Modeling biochemical pathways in the gene ontology},
author = {Hill, David P. and D’Eustachio, Peter and Berardini, Tanya Z. and Mungall, Christopher J. and Renedo, Nikolai and Blake, Judith A.},
abstractNote = {The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.},
doi = {10.1093/database/baw126},
journal = {Database},
number = ,
volume = 2016,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 2016},
month = {Thu Sep 01 00:00:00 EDT 2016}
}

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