Cited by 184Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGenomics and Phylogenetic Studies

A gene ontology inferred from molecular networks

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Dutkowski, Janusz, Kramer, Michael, Surma, Michal A, Balakrishnan, Rama, Cherry, J Michael, Krogan, Nevan J, Ideker, Trey·2013·Nature Biotechnology

Abstract

Ontologies have proven very useful for capturing knowledge as a hierarchy of terms and their interrelationships. In biology a major challenge has been to construct ontologies of gene function given incomplete biological knowledge and inconsistencies in how this knowledge is manually curated. Here we show that large networks of gene and protein interactions in Saccharomyces cerevisiae can be used to infer an ontology whose coverage and power are equivalent to those of the manually curated Gene Ontology (GO). The network-extracted ontology (NeXO) contains 4,123 biological terms and 5,766 term-term relations, capturing 58% of known cellular components. We also explore robust NeXO terms and term relations that were initially not cataloged in GO, a number of which have now been added based on our analysis. Using quantitative genetic interaction profiling and chemogenomics, we find further support for many of the uncharacterized terms identified by NeXO, including multisubunit structures related to protein trafficking or mitochondrial function. This work enables a shift from using ontologies to evaluate data to using data to construct and evaluate ontologies.

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  • Novel functional organization is embedded within large-scale, unbiased datasets, which may not be captured completely by functional standards, including GO as it is currently organized.

    “As observed previously (16), this indicates that novel functional organization is embedded within large-scale, unbiased datasets, which may not be captured completely by functional standards, including GO as it is currently organized (14).”
  • The community detection approach was well illustrated in recent analyses of protein-protein interaction networks in yeast and human.

    “The community detection approach was well illustrated in recent analyses of protein-protein interaction networks in yeast (Dutkowski et al., 2013) and human (Huttlin et al., 2015).”

    From Mapping the multiscale structure of biological systems (2021) · cites this paper as Dutkowski et al., 2013

  • The NeXO and Mashup efforts found that the majority of cellular components catalogued in GO could be recovered from hierarchical detection of communities in protein network data.

    “For instance, the multiscale models of cellular components constructed by the NeXO and Mashup efforts (see above) found that the majority of cellular components catalogued in GO could be recovered from hierarchical detection of communities in protein network data (Cho et al., 2016; Dutkowski et al., 2013).”

    From Mapping the multiscale structure of biological systems (2021) · cites this paper as Dutkowski et al., 2013

  • Hierarchical maps generated from proximity networks can be thought of as a type of ontology, as demonstrated in the NeXO project.

    “Conversely, hierarchical maps generated from proximity networks can be thought of as a type of ontology, as demonstrated in the NeXO project (Dutkowski et al., 2013), albeit one that is driven by direct data analysis rather than manual curation of literature.”

    From Mapping the multiscale structure of biological systems (2021) · cites this paper as Dutkowski et al., 2013

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1 of the 58 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. [9]Buitelaar P Cimiano P Ontology Learning and Population: Bridging the Gap between Text and Knowledge 167 IOS Press Amsterdam 2008

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Authors
Dutkowski, J., Kramer, M., Surma, M. A., Balakrishnan, R., Cherry, J. M., Krogan, N. J., & Ideker, T.
Affiliations
  • University of California San Diego (US)
  • Stanford University (US)
  • Gladstone Institutes (US)
Corresponding author
  • Dutkowski, Janusz
  • Ideker, Trey
Venue
Nature Biotechnology
Published
2012-12-16
Volume
31
Issue
1
Pages
38-45
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
OpenAlex
W2143962926
Cited by
184
References
58
Citations by year
2026: 4 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 7 · 2021: 23 · 2020: 12 · 2019: 15 · 2018: 21 · 2017: 23 · 2016: 18 · 2015: 18 · 2014: 17 · 2013: 15
FWCI
6.42
Type
journal article
Language
en
Open access
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institutes of Health (GM085764, GM 103504, GM081879)
  • National Human Genome Research Institute (U41HG002273)
  • National Institute of General Medical Sciences (R01GM084448, P50GM081879, P50GM085764, P41GM103504, T32GM007198)
  • National Institute of Allergy and Infectious Diseases (P30AI027763)
  • National Institute of Environmental Health Sciences (R01ES014811)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
Gene Ontology, nEXO, protein-protein interaction network, gene interaction networks, Saccharomyces cerevisiae, term relations, chemogenomics, protein trafficking, mitochondrial function, multi-subunit protein complexes
MeSH terms
Fungal Proteins, Genes, Fungal, Saccharomyces cerevisiae, Gene Regulatory Networks

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