|
1
|
Ong E, Xie J, Ni Z, Liu Q, Sarntivijai S, Lin Y, Cooper D, Terryn R, Stathias V, Chung C, Schürer S, He Y. Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses. BMC Bioinformatics 2017; 18:556. [PMID: 29322930 PMCID: PMC5763302 DOI: 10.1186/s12859-017-1981-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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.
Collapse
Affiliation(s)
- Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jiangan Xie
- Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Zhaohui Ni
- Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Qingping Liu
- Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Sirarat Sarntivijai
- Samples, Phenotypes and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK
| | - Yu Lin
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA
| | - Daniel Cooper
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA
| | - Raymond Terryn
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA
| | - Caty Chung
- BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA.,Center for Computational Science, University of Miami, Miami, FL, USA
| | - Stephan Schürer
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA. .,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA. .,Center for Computational Science, University of Miami, Miami, FL, USA.
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. .,Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|