Dataset Information

Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses.

ABSTRACT: BACKGROUND:Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration. RESULTS:CLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts. CONCLUSIONS:CLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.

SUBMITTER: Ong E

PROVIDER: S-EPMC5763302 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses.

Ong Edison E Xie Jiangan J Ni Zhaohui Z Liu Qingping Q Sarntivijai Sirarat S Lin Yu Y Cooper Daniel D Terryn Raymond R Stathias Vasileios V Chung Caty C Schürer Stephan S He Yongqun Y

BMC bioinformatics 20171221 Suppl 17

<h4>Background</h4>Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration.<h4>Results</h4>CLO has consistently represented all 1097 LINCS cell lines and included information extracte ...[more]

PMID: 29322930

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Project description:BACKGROUND:The symbiotic interactions that occur between humans and organisms in our environment have a tremendous impact on our health. Recently, there has been a surge in interest in understanding the complex relationships between the microbiome and human health and host immunity against microbial pathogens, among other things. To collect and manage data about these interactions and their complexity, scientists will need ontologies that represent symbiotic interactions as they occur in reality. METHODS:We began with two papers that reviewed the usage of 'symbiosis' and related terms in the biology and ecology literature and prominent textbooks. We then analyzed several prominent standard terminologies and ontologies that contain representations of symbiotic interactions, to determine if they appropriately defined 'symbiosis' and related terms according to current scientific usage as identified by the review papers. In the process, we identified several subtypes of symbiotic interactions, as well as the characteristics that differentiate them, which we used to propose textual and axiomatic definitions for each subtype of interaction. To both illustrate how to use the ontological representations and definitions we created and provide additional quality assurance on key definitions, we carried out a referent tracking analysis and representation of three scenarios involving symbiotic interactions among organisms. RESULTS:We found one definition of 'symbiosis' in an existing ontology that was consistent with the vast preponderance of scientific usage in biology and ecology. However, that ontology changed its definition during the course of our work, and discussions are ongoing. We present a new definition that we have proposed. We also define 34 subtypes of symbiosis. Our referent tracking analysis showed that it is necessary to define symbiotic interactions at the level of the individual, rather than at the species level, due to the complex nature in which organisms can go from participating in one type of symbiosis with one organism to participating in another type of symbiosis with a different organism. CONCLUSION:As a result of our efforts here, we have developed a robust representation of symbiotic interactions using a realism-based approach, which fills a gap in existing biomedical ontologies.

Dataset Information

Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses.

Publications

Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses.

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