Bacterial integrative and conjugative elements (ICEs) are mobile genetic elements critical to horizontal gene transfer and organism evolution. To better understand and analyze ICEs, it is critical to systematically represent, integrate and classify gene components, functional modules and related information of available ICEs archived in the ICEberg database. Toward this goal, we developed a community-driven ICE ontology (ICEO). ICEO is aligned with the Basic Formal Ontology (BFO) to allow the integration with other ontologies. ICEO reused the existing reliable ontologies, such as Ontology of Gene and Genome, Protein Ontology and NCBITaxon. ICEO now represents the information about over 270 experimentally verified ICEs from 235 bacterial strains. Two query use cases were provided, including a DL query of ICE-contained genes that are also virulence factors and a SPARQL query of ICEs under an upper level taxonomy type of Gammaproteobacteria. Our study demonstrated that ICEO supports computer-assisted reasoning and efficient SPARQL query.
Integrative and conjugative elements (ICEs), also called
conjugative transposons before, are a large family of the bacterial
mobile genetic elements (MGEs) (
transferred as linear single-stranded DNA (ssDNA) typically depended on a relaxase and a conjugative type IV secretion system (T4SS). AICEs are delivered as double-stranded DNA (dsDNA) relied on proteins for replications and translocation.
The information about thousands of experimentally validated or
computationally predicted bacterial ICEs is freely accessible in
ICEberg (http://db-mml.sjtu.edu.cn/ICEberg/), a comprehensive
web-based ICE database that is developed by our group at the
Shanghai Jiao Tong University (
For example, one of the most successful and widely-used
ontology, Gene Ontology (GO; http://www.geneontology.org/)
(
In this study, we report the development strategy of a communitydriven Ontology of the Integrative and Conjugative Element
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
(ICEO), which is aimed to ontologically represent and integrate
the ICE gene information and functional modules to support
computer-assisted reasoning. There are 260 experimentally
verified T4SS-type ICEs (113 with the entire nucleotide
sequences) and 11 experimentally validated AICEs (7 with the
entire nucleotide sequences) in ICEberg. The current ICEO is
focused on ontological organization of the information about
these 271 experimentally validated ICEs for now here. ICEO is
developed by reusing many terms from existing ontologies and
using the state-of-the-art ontology engineering technologies (
The development of ICEO follows the Open Biological and
Biomedical Ontologies (OBO) Foundry principles (
To support ontology interoperability and avoid reinventing the
wheel, related existing terms from reliable ontologies were
imported into ICEO via an Ontofox (http://ontofox.hegroup.org)
import strategy (
Based on the available information, an ontology design pattern (ODP) was developed. Many new annotations and relations between different entities were added by utilizing the Ontorat (http://ontorat.hegroup.org/) (16), an online program designed to support ODP-based creation of new ontology terms, hierarchies, annotations, and logical axioms.
The Protégé-OWL editor (version 5.2) (http://protege.stanford.edu/) was used for the ICEO manual processing and editing, ontology term merging and visualization. ICEO-specific terms were generated using new ICEO identifiers with the prefix “ICEO_” followed by auto-generated 7 digits. The Hermit reasoner (http://hermit-reasoner.com/) was applied for semantic consistency checking and inferencing.
The ICEO is developed using the format of W3C standard Web
Ontology Language (OWL2)
(https://www.w3.org/TR/owlguide/) (
The knowledge stored in the ICEO ontology can be queried through different approaches. In this study, we used the Description Logic (DL) query and SPARQL (a recursive acronym for SPARQL Protocol and RDF Query Language) query. The DL query was performed using the Protégé OWL editor. For the SPARQL query, ICEO was stored in the Resource Description Framework (RDF; https://www.w3.org/RDF/) triples in the Ontobee RDF triple store (17, 18). The Ontobee SPARQL query interface (http://www.ontobee.org/sparql) was then used for ICEO specific SPARQL query.
a. ICEO is aligned with BFO and OBO foundry ontologies
Fig. 1B represents the basic top-level ICEO hierarchical structure
in accordance with the ICE genetic functional modules (Fig. 1A).
Specifically, ICEO is aligned to the upper-level Basic Formal
Ontology (BFO) 2.0 version (
To enable the reusability of existing ontologies, ICEO imports
many related terms and relations from OBO library ontologies.
As shown in Fig. 1B, ICEO imports OGG and PR to represent the
genes and proteins of ICEs. NCBITaxon terms are imported to
represent various ICE-containing organisms in the taxonomic
organism hierarchy. GO terms are imported to represent the
processes in the whole life cycle of ICEs.
b. Modified and extensive gene and protein ID assignments
and label naming strategy
Given the ever-growing number of genes sequenced and
annotated, the phenomena of having genes or proteins in different
organisms but with the same names archived in the NCBI
GenBank database is inevitable. To avoid name conflicts, the
original OGG designed a special scheme to automatically assign
gene IDs by mapping ontology ID with NCBITaxon IDs and
NCBI Gene IDs (
ICE is essentially a genetic feature so that the gene representation is our priority. Since the Protein Ontology (PR) does not include all the proteins included in ICEO, we applied a similar strategy to represent protein names in ICEO (Fig. 2). We have also contacted the Protein Ontology (PR) team, and will request new classes in the PR to improve ICEO.
top-level hierarchy (Fig. 1) which shows the hierarchical relationships among different terms, Fig. 3 shows the logical relations of related terms across different hierarchical structures in ICEO. Together, the combination of Fig. 1 and Fig. 3 presents us a general framework of the ontological design of ICEO. As shown in Fig. 3A, the basic ICEO design pattern is to represent ICE from the view of typical function modules. ICE ‘has part’ integration, conjugation, regulation and accessory module components, and these components and or their encoding proteins ‘participants in’ specific ICE life process.
An example of applying this general design pattern to a concrete
example is shown in Fig. 3B where the design pattern is used to
represent ICEKp1, a virulence-associated ICE found in Klebsiella
pneumoniae subsp. pneumoniae NTUH-K2044 causing a primary
liver abscess (
ICEO is formatted in the machine-interpretable OWL format, which is easily understood by computer programs and can support various advanced queries and analyses. Therefore, ICEO can be used for various applications, such as DL query and SPARQL query, which is designed for RDF triple and cannot be done directly in ICEberg. Two ICEO use cases are provided in the study as follows: Use Case 1: Use DL Query to query the specified group of genes of an ICE ICEO supports OWL-based automated reasoning using reasoning programs within OWL editors. As an example, we designed the following question for query the ontology:
In ICEO, a virulence factor gene is logically defined as
“something that has role some virulence factor gene role”. To
answer this question, the following query was simply performed
using the DL Query function in ProtégéOWL editor version 5.2
(Fig. 5):
('has role' some 'virulence factor gene role') and ('part
of' some ICEKp1)
As shown in Fig. 5, our query identified 15 genes that are part of
the specific ICEKp1 and also encode for virulence factors in the
host bacterium of the ICEKp1. The result indicates that ICEKp1
is responsible for transporting this set of virulence factor genes to
Klebsiella pneumoniae strain NTUH-K2044, the bacterium that
hosts the ICE. ICEKp1 is indeed critical to make the bacterium
virulent (
Fig. 5. DL Query window in Protégé5. The query of all the virulence factor genes of ICEKp1 was performed using the DL Query of OWL editor Protégé5.2. The query code is shown on the top, and the query results are displayed at the bottom.
Use Case 2: Use SPARQL query for advanced analysis As the OWL-formatted ICEO is stored in the Ontobee RDF triple store (17, 18), the ICEO information can be also queried and analyzed using the RDF query language, SPARQL (https://www.w3.org/TR/rdf-sparql-query-protocol/).
Fig. 6 demonstrates a SPARQL query over ICEO. This example includes only a few lines of SPARQL query code. However, it enabled the identification of the organisms under the taxonomic class of ‘Gammaproteobacteria’ (NCBITaxon_1236) that include experimentally verified ICEKp1 family ICEs. A variety of queries can be achieved with new SPARQL query scripts for more practical and advanced analysis.
Fig. 6. SPARQL query of all the ICEKp1 family ICEs in the taxonomy rank of Gammaproteobacteria. The ICEO term ICEO_0000141 is ‘ICEKp1 family ICE’ class, ICEO_0000020 refers to an object property ‘is ICE of organism’, and the NCBITaxon term NCBITaxon_1236 points to ‘Gammaproteobacteria’ class. The query was performed using the Ontobee SPARQL query interface (http://www.ontobee.org/sparql/). In this study, we developed a community-driven Integrative and Conjugative Element Ontology (ICEO). ICEO ontologically represents the complex hierarchical structure of ICEs, ICE components, and the relations among ICE and ICE components. As demonstrated in two use cases, the ICEO representation of the experimentally verified ICE knowledge supports computer-assisted data integration, efficient query, and reasoning.
ICEO now is built by standardizing and integrating the rich information from ICEberg database. And ICEO can perform tasks that cannot be done in current ICEberg. For example, in our use case 1, we were able to easily query any virulence factors for any level of MGEs. Currently, ICEberg will label VF for those MGEs that are virulence factors. However, it is still impossible for users to query all VFs for a specific bacterial group. In our use case 2, we further illustrate an efficient way of using ICEO to query ICEs under any specific bacterial taxon level like class, species or family. ICEberg can only query based on species level. However, rather than being only a complement or translation of ICEberg database, ICEO and ICEO-based features will be explored to be integrated into ICEberg in the future.
ICEO is the first BFO-based ICE ontology. Toussaint et al.
developed the MeGO, a Gene Ontology dedicated to the functions
of mobile genetic elements, and used it in the ACALME database
(A CLAssification of Mobile genetic Elements,
http://aclame.ulb.ac.be/) (
ICEO represents the complicated gene components, functional modules and related information about T4SS-type ICEs and AICEs, making it possible to perform the automatically computer-assisted reasoning, query, and advanced analysis of ICE.
However, ICEO is currently at its early development stage and
will be further developed in the future. We will represent more
known information about T4SS-type ICEs. Due to differences in
cell membrane structure, Gram-positive and Gram-negative
bacteria have different T4SS organization and features and are
associated with different T4SS-type ICEs information. Such
differential characteristics will be further categorized, modeled,
and represented in ICEO. Besides, only 11 experimentally
verified AICE are included in ICEO. Compared with T4SS-type
ICEs, AICE is less commonly seen in bacteria. However, AICE
is also important in terms of developing useful tools for genetic
engineering of Actinobacteria (
ICEO will be also be used for more applications. A major application is to apply ICEO as the base of the next version of the ICEberg database to standardize and integrate the rich ICE information for the advanced data sharing and organization.
Second, we are going to combine and integrate the structured
ICEO ontology into the ICEfinder (
Furthermore, ICEO may also facilitate ontology-based literature
mining, which has been shown in many other ontology-based
research domains (
To conclude, ICEO is a biological ontology and a knowledgecentric platform of the bacterial integrative and conjugative element. ICEO can serve as an ICE knowledgebase and facilitate the systematical representation, integration and automatical computer-assisted reasoning of ICE data.
National Key R&D Program of China [2017YFC1600100 to H.Y.O.]; ML was supported by a jointly funded Ph.D.studentship of the China Scholarship Council and University of Michigan Medical School (Grant No. 201806230209).
YH and HYO are the co-corresponding authors. Their email addresses are yongqunh@med.umich.edu and hyou@sjtu.edu.cn respectively. Any suggestions or questions are highly welcomed. 15. NCBITaxon: An ontology representation of the NCBI organismal taxonomy. http://obofoundry.org/ontology/ncbitaxon.html. 16. Xiang,Z., Zheng,J., Lin,Y. and He,Y. (2015) Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns. Journal of biomedical semantics, 6, 4. 17. Xiang,Z., Mungall,C., Ruttenberg,A. and He,Y. (2011) Ontobee: A linked data server and browser for ontology terms. In ICBO. 18. Ong,E., Xiang,Z., Zhao,B., Liu,Y., Lin,Y., Zheng,J., Mungall,C., Courtot,M., Ruttenberg,A. and He,Y. (2016) Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration. Nucleic acids research, 10.1093/nar/gkw918. 19. Grenon,P. (2003) Spatio-temporality in basic formal ontology Ifomis.
----------------------------------------------------------------We appreciate the time and efforts of the reviewers who reviewed our manuscript. Their suggestions and comments are constructive and helpful. We have revised our manuscript by incorporating these comments accordingly as described below. It is noted that the italicized paragraphs below are the reviewer’s comments and our followed replies are in regular font style. ----------------------- REVIEW 1 --------------------
lyzing the bacterial integrative and conjugative element
----------- Overall evaluation ----------
Reply: Thanks for your kind and positive comments. The authors mention their use of the "eXtensive Ontology Development (XOD) strategy" which includes the involvement of community efforts to develop ontologies, but how they in fact involve the community is not discussed. The paper would be improved by the authors discussing how they had approached their interaction with the community - e.g. by providing the ontology to experts for review.
Reply: We have submitted the ICEO to the OBO Foundry community and have been checking and refining the ICEO according to the community’s comments. ----------------------- REVIEW 2 --------------------
lyzing the bacterial integrative and conjugative element
----------- Overall evaluation ----------
bacterial integrative and conjugative elements (ICEs) into an ontology (ICEO). ICEO builds on existing OBO ontologies such as the Basic Formal Ontology, the Protein Ontology, and the
Reply: We appreciate the reviewer’s summary and positive comments.
demonstrate the benefits of the ontological translation. Are the
Reply: This is a good point. We have added a new paragraph in the Discussion part to address the reviewer’s comment: “ICEO now is built by standardizing and integrating the rich information from ICEberg database. And ICEO can perform tasks that cannot be done in current ICEberg. For example, in our use case 1, we were able to easily query any virulence factors for any level of MGEs. Currently, ICEberg will label VF for those MGEs that are virulence factors. However, it is still impossible for users to query all VFs for a specific bacterial group. In our use case 2, we further illustrate an efficient way of using ICEO to query ICEs under any specific bacterial taxon level like class, species or family. ICEberg can only query based on species level. However, rather than being only a complement or translation of ICEberg database, ICEO and ICEO-based features will be explored to be integrated into ICEberg in the future.” (Discussion part, column 1, paragraph 2)
tory and OWL files. The `iceo_merged.owl` file loaded in Protege and reasoned under HermiT. ICEO claims to be developed according to OBO principles. It uses an OBO namespace <http://purl.obolibrary.org/obo/ICEO_>, however no OBO ID has been requested for ICEO. The paper claims a CC-BY 4.0 license, but the GitHub repository and OWL files contain no license information. I did not see any textual definitions for ICEO terms.
Reply: We have submitted the OBO ID request for ICEO and have been refining the ICEO according to the community’s comments. CC-BY 4.0 license and all the textual definitions have been added to the ICEO.
- p2 "eXtensive Ontology Development" should be "eXtensible
Reply: All the above points have been corrected in the latest version manuscript. ----------------------- REVIEW 3 --------------------
lyzing the bacterial integrative and conjugative element
----------- Overall evaluation ----------
ples quite well, and the authors make an appropriate nod to the
senting the domain of ICE.
Reply: Thanks for your positive comments and advice.
The Authors state: "Since the Protein Ontology (PR) does not include all the proteins included in ICEO. We applied a similar strategy to represent protein names in ICEO (Fig. 2)." The should be a single sentence, but more importantly, the authors should request classes in the PR to cover the protein entities of interest, and then revise their ontology appropriately. This is part of working within the OBO Foundry community.