=Paper=
{{Paper
|id=Vol-1747/IP07_ICBO2016
|storemode=property
|title=The Cell Line Ontology Integration and Analysis of the Knowledge of LINCS Cell Lines
|pdfUrl=https://ceur-ws.org/Vol-1747/IP07_ICBO2016.pdf
|volume=Vol-1747
|authors=Edison Ong,Jiangan Xie,Zhaohui Ni,Qingping Liu,Yu Lin,Vasileios Stathias,Caty Chung,Stephan Schurer,Yongqun He
|dblpUrl=https://dblp.org/rec/conf/icbo/OngXNLLSCSH16
}}
==The Cell Line Ontology Integration and Analysis of the Knowledge of LINCS Cell Lines ==
https://ceur-ws.org/Vol-1747/IP07_ICBO2016.pdf
The Cell Line Ontology integration and analysis of
the knowledge of LINCS cell lines
Edison Ong1, Jiangan Xie1, Zhaohui Ni1, Qingping Liu1, Yu Lin2, Vasileios Stathias2, Caty Chung2,
Stephan Schurer*2, Yongqun He*1
1
University of Michigan, Ann Arbor, Michigan, USA; 2 University of Miami, Coral Gables, FL, USA
Abstract— Cell lines are crucial to study molecular signatures
and pathways, and are widely used in the NIH Common Fund B. CLO modeling nd design pattern generation
LINCS project. The Cell Line Ontology (CLO) is a community- Based on the data types obtained from the mapping
based ontology representing and classifying cell lines from process, an updated CLO design pattern model was generated
different resources. To better serve the LINCS research in order to accommodate new LINCS cell line data attributes.
community, from the LINCS Data Portal and ChEMBL, we
identified 1,097 LINCS cell lines, among which 717 cell lines were C. New information incorporation into CLO
associated with 121 cancer types, and 352 cell line terms did not Based on the new design patterns, Ontorat
exist in CLO. To harmonize LINCS cell line representation and
(http://ontorat.hegroup.org) was used to incorporate LINCS
CLO, CLO design patterns were slightly updated to add new
information of the LINCS cell lines including different database cell line data from different data sources to CLO. Manual
cross-reference IDs. A new shortcut relation was generated to checking was performed to ensure correctness.
directly link a cell line to the disease of the patient from whom D. Generation and analysis of a LINCS cell line set of CLO
the cell line was originated. After new LINCS cell lines and
related information were added to CLO, a CLO subset/view OntoFox (http://ontofox.hegroup.org) was used to generate
(LINCS-CLOview) of LINCS cell lines was generated and a CLO subset (LINCS-CLOview) that includes all LINCS cell
analyzed to identify scientific insights into these LINCS cell lines. lines, as shown here: https://raw.githubusercontent.com/CLO-
This study provides a first time use case on how CLO can be ontology/CLO/master/src/ontology/LINCS-CLOview.owl. The
updated and applied to support cell line research from a specific LINCS CLO subset was also submitted to Ontobee
research community or project initiative. (http://www.ontobee.org). The information of the subset was
visualized using Protégé OWL editor, queried using Ontobee
Keywords— Cell line, cell, ontology, CLO, LINCS, ChEMBL SPARQL web program, and further analyzed.
I. INTRODUCTION III. RESULTS
The NIH Common Fund Library of Integrated Network-
based Cellular Signatures (LINCS) program aims to create a A. LINCS cell line information extraction and mapping from
network-based biological understanding of gene expression different resources
and cellular processes when cells are exposed to various As of April 15, 2016, 1,097 cell lines were extracted from
perturbing agents (http://www.lincsproject.org/). Over 1000 the LINCS Data Portal. Among these LINCS cell lines, 794
cell lines have been used in LINCS and play a critical role as cell lines could be directly mapped to CLO. Meanwhile,
disease model systems to produce molecular and cellular ChEMBL included 637 cell line entries with LINCS IDs.
signatures and networks. Among these, 451 cell lines have CLO_IDs, and 51 out of the
The Cell Line Ontology (CLO) is a community-based remaining 186 cell lines could be mapped to CLO using name
ontology system for representing cell lines [1]. The overall matching. The data types available related to these cell lines in
goal of this study is to use CLO to represent and integrate the the LINCS Portal and ChEMBL are shown in Fig. 1.
knowledge of LINCS cell lines in order to power LINCS cell - cell ID
lines’ integrity across multiple resources. - cell line (CL) name - cell line name
- cell description
- CL LINCS ID
- CL alternate name - cell source tissue
- CL provider name - cell source organism
II. METHODS - CL provider catalog ID - cell source tax ID
- CL organ - CLO ID
A. Information extraction and data mapping - CL organism - EFO ID
- CL disease - cellosaurus ID
Two sources, including the LINCS Data Portal - CL disease DO ID - CL LINCS ID
- ChEMBL ID
(http://lincsportal.ccs.miami.edu/entities/) and ChEMBL [2], (A) LINCS data portal
(B) ChEBML data types
were used to obtain LINCS cell line information. The data in data types
these two sources were compared and mapped to the CLO
Fig. 1. Cell line-related data types of the data downloaded from LINCS Data
knowledge base, and new information was identified. Portal and ChEMBL. (A) Data types from LINCS Data Portal. (B) Data types
from ChEMBL. Red-highlighted items (e.g., ChEMBL ID) were not covered
in CLO, which were added later to CLO in this study.
*: corresponding authors: stephan.schurer@gmail.com; and
yongqunh@med.umich.edu
Among the total of 1097 LINCS cell lines each with a MCF 10A and MCF 10F cells). The other 118 diseases are
unique LINCS cell line ID (e.g., LCL-1512 for HeLa cell), 466 various types of cancers. The hierarchical structure of these
have ChEMBL, LINCS, and CLO IDs, 279 have LINCS and diseases under the Disease Ontology (DOID) also helped the
CLO IDs, and 352 LINCS cell lines do not have any CLO IDs. understanding of all the diseases associated with LINCS cell
lines. For example, 19 LINCS cell lines (e.g., HeLa cell) were
B. CLO modeling and design pattern generation derived from patients with cervical adenocarcinoma, 4 with
To represent the new database information to a specific cell cervical clear cell adenocarcinoma (a specific type of cervical
line (Fig. 1), we used ‘seeAlso’ relation. For example, for the adenocarcinoma), and 14 with cervical squamous cell
HeLa cell (CLO_0003684), we added to CLO: ‘Cell line carcinoma. These diseases all belong to cervix carcinoma.
LINCS ID: LCL-1512’ and ‘seeAlso: EFO: EFO_0001185; We also examined the tissue and organ types from which
CHEMBL: CHEMBL3308376; CVCL: CVCL_0030’. the LINCS cell lines were derived. In CLO, the multi-species
To more conveniently link a specific cell line and a disease, anatomy ontology UBERON is used to represent tissues and
we have also generated a new shortcut relation ‘derived organs. In total 131 UBERON terms have been used in
originally from patient with disease’ (Fig. 2). LINCS-CLOview to refer to various anatomic locations from
which LINCS cell lines were derived.
cell line cell from an
cell line cell is a anatomical structure The cell types of LINCS cell lines were analyzed. The Cell
in organism derived from cell from an Type Ontology (CL) was used in CLO to demonstrate the cell
shortcut: derived originally anatomical
from patient with disease
types of different cell lines. In total, 43 CL cell types, such as
structure in
has part organism epithelial cell, B cell, and T cell, are included in LINCS-
organism having
disease has disease
a disease CLOview. Each of these cell types is linked to different cell
line cells. For a project to study cellular signatures related to a
(A)
specific cell type, the LINCS-CLOview provides a feasible
immortal human uterine method to identify which cell line cells to use.
HeLa cell is a cervix-derived epithelial
cell line cell derived
shortcut: derived originally from human uterine IV. DISCUSSION
from patient with disease cervix-derived
has part epithelial cell This article is the first report of developing a CLO
human patient
cervical has
with cervical
community view to serve a specific community, in this case,
adenocarcinoma disease
the LINCS research community. Since tens of thousands of cell
adenocarcinoma
(B) lines have been represented in CLO, it is inefficient to use the
whole CLO for LINCS cell line related research. The
Fig. 2. CLO design pattern model for using the new shortcut relation ‘derives generation of LINCS-CLOview allows standardization and
originally from patient having disease’. (A) General design pattern; (B) an
example to illustrate the design pattern. The shortcut relation makes it more modularization of the LINCS cell lines, which facilitates the
efficient to represent the relation between a cell line cell and a disease when the better analysis and reuse of the LINCS cell line information.
parent term of the cell line cell includes sufficient information about the cell
type and tissue/organ. In this illustration, the classes as shown in the dotted
boxes are redundant and are not needed. ACKNOWLEDGMENT
This work was supported by grant U54HL127624 (BD2K
C. New data integration to CLO and CLO subset generation LINCS Data Coordination and Integration Center, DCIC)
Based on the mapping and the design pattern models (Fig. awarded by the National Heart, Lung, and Blood Institute
1 and 2), extra data available in the LINCS Data Portal and through funds provided by the trans-NIH LINCS Program and
ChEMBL were integrated into to CLO. the trans-NIH Big Data to Knowledge (BD2K) initiative
(http://www.bd2k.nih.gov). LINCS is an NIH Common Fund
A CLO subset of LINCS cell lines (LINCS-CLOview) was projects. This project was also supported by a BD2K-LINCS
also generated. LINCS-CLOview can be considered as a CLO DCIC external data science research award.
“community view” [3] for the LINCS research community. As
of May 1, 2016, LINCS-CLOview contained 1,924 terms,
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