DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Yu, Michael; Ma, Jianzhu; Ono, Keiichiro; Zheng, Fan; Fong, Samson; Gary, Aaron; Chen, Jing; Demchak, Barry; Pratt, Dexter; Ideker, Trey

doi:10.1016/j.cels.2019.02.003

Cited by 19 publications

(15 citation statements)

References 47 publications

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“…Interestingly, PEP8 is clustered with RCN1, which encodes a calcineurin-dependent signaling protein, among other genes involved in filamentation and drug resistance. We note that not all the strains phenotypically tested appear to have roles in virulence-related processes with the recall and precision of these approaches estimated to be 0.43 and 0.31, respectively (Yu et al, 2019). However, given that NeXOs are data-driven and can be generated quickly with little expert knowledge or human intervention, they provide useful approaches for the identification of novel pathways, prediction of functions and generation of testable hypotheses, especially in those organisms that lack comprehensive annotation.…”

Section: Discussionmentioning

confidence: 86%

Identifying Candida albicans Gene Networks Involved in Pathogenicity

et al. 2020

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Candida albicans is a normal member of the human microbiome. It is also an opportunistic pathogen, which can cause life-threatening systemic infections in severely immunocompromized individuals. Despite the availability of antifungal drugs, mortality rates of systemic infections are high and new drugs are needed to overcome therapeutic challenges including the emergence of drug resistance. Targeting known disease pathways has been suggested as a promising avenue for the development of new antifungals. However, <30% of C. albicans genes are verified with experimental evidence of a gene product, and the full complement of genes involved in important disease processes is currently unknown. Tools to predict the function of partially or uncharacterized genes and generate testable hypotheses will, therefore, help to identify potential targets for new antifungal development. Here, we employ a network-extracted ontology to leverage publicly available transcriptomics data and identify potential candidate genes involved in disease processes. A subset of these genes has been phenotypically screened using available deletion strains and we present preliminary data that one candidate, PEP8, is involved in hyphal development and immune evasion. This work demonstrates the utility of network-extracted ontologies in predicting gene function to generate testable hypotheses that can be applied to pathogenic systems. This could represent a novel first step to identifying targets for new antifungal therapies.

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Section: Discussionmentioning

confidence: 86%

Identifying Candida albicans Gene Networks Involved in Pathogenicity

et al. 2020

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“…For example, CO has the relationship "[keratinocyte] (is_a) [epidermal_cell]," and thus, all cells annotated as "keratinocyte" are also annotated as "epidermal cell." The CO was obtained from http://www.obofoundry.org/ontology/cl.html and processed by the Data Driven Ontology Toolkit (DDOT) [50] retaining "is_a" relationships only.…”

Section: Single-cell Rna-seq Datamentioning

confidence: 99%

HiDeF: identifying persistent structures in multiscale ‘omics data

et al. 2021

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In any ‘omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

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“…To enable a web-based, interactive investigation of hierarchical models of network communities, a compelling future direction is to create a pipeline that connects the CDAPS to our HiView platform [ 36 ]. HiView provides an intuitive visualization of hierarchical/nested structures with a powerful “circle packing” layout [ 37 ], as well as the ability to use any community-defined gene list to probe the support of the same community in orthogonal network datasets hosted on the NDEx database [ 38 ].…”

Section: Availability and Future Directionsmentioning

confidence: 99%

Multiscale community detection in Cytoscape

et al. 2020

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Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques and their accompanying visualization schemes available to a broad range of biologists. Here we present a service-oriented, end-to-end software framework, CDAPS (Community Detection APplication and Service), that integrates the identification, annotation, visualization, and interrogation of multiscale network communities, accessible within the popular Cytoscape network analysis platform. With novel design principles, CDAPS addresses unmet new challenges, such as identifying hierarchical community structures, comparison of outputs generated from diverse network resources, and easy deployment of new algorithms, to facilitate community-sourced science. We demonstrate that the CDAPS framework can be applied to high-throughput protein-protein interaction networks to gain novel insights, such as the identification of putative new members of known protein complexes.

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DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Cited by 19 publications

References 47 publications

Identifying Candida albicans Gene Networks Involved in Pathogenicity

Identifying Candida albicans Gene Networks Involved in Pathogenicity

HiDeF: identifying persistent structures in multiscale ‘omics data

Multiscale community detection in Cytoscape

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