# **BIOLOGICAL ONTOLOGIES AND SEMANTIC BIOLOGY**

# **Topic Editor John Hancock**

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**ISSN** 1664-8714 **ISBN** 978-2-88919-277-9 **DOI** 10.3389/978-2-88919-277-9

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# **BIOLOGICAL ONTOLOGIES AND SEMANTIC BIOLOGY**

Topic Editor: **John Hancock,** University of Cambridge, United Kingdom

Abstracted and simplified view of the Cytomer ontology illustrating the handling of organs of an anatomical entity. The green nodes are the start nodes for the search function specified in the text. In this section, the entities are connected by four different relations given in the legend. Figure taken from: Dönitz J and Wingender E (2012) The ontology-based answers (OBA) service: a connector for embedded usage of ontologies in applications. *Front. Genet*. 3:197. doi: 10.3389/fgene.2012.00197

As the amount of biological information and its diversity accumulates massively there is a critical need to facilitate the integration of this data to allow new and unexpected conclusions to be drawn from it.

The Semantic Web is a new wave of webbased technologies that allows the linking of data between diverse data sets via standardised data formats ("big data"). Semantic Biology is the application of semantic web technology in the biological domain (including medical and health informatics). The Special Topic encompasses papers in this very broad area, including not only ontologies (development and applications), but also text mining, data integration and data analysis making use of the technologies of the Semantic Web.

Ontologies are a critical requirement for such integration as they allow conclusions drawn about biological experiments, or descriptions

of biological entities, to be understandable and integratable despite being contained in different databases and analysed by different software systems. Ontologies are the standard structures used in biology, and more broadly in computer science, to hold standardized terminologies for particular domains of knowledge. Ontologies consist of sets of standard terms, which are defined and may have synonyms for ease of searching and to accommodate different usages by different communities. These terms are linked by standard relationships, such as "is\_a" (an eye "is\_a" sense organ) or "part\_of" (an eye is "part\_of" a head). By linking terms in this way, more detailed, or granular, terms can be linked to broader terms, allowing computation to be carried out that takes these relationships into account.

# Table of Contents

### Editorial: biological ontologies and semantic biology

#### *John M. Hancock\**

*Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK \*Correspondence<EMAIL_ADDRESS>
#### *Edited and Reviewed by:*

*Richard D. Emes, University of Nottingham, UK*

**Keywords: semantic biology, biological ontologies, semantic web, data representation, data analysis**

As the amount of biological data and its diversity accumulates massively there is a critical need to facilitate the integration of this data to allow new and unexpected conclusions to be drawn from it.

The Semantic Web comprises web-based technologies that allow linking of data between diverse data sets. Semantic Biology is the application of semantic web technology in the biological domain (including medical and health informatics). The Special Topic in Biological Ontologies and Semantic Biology brings together papers in this broad area—which spans computer science, computational biology and bioinformatics—providing a platform for strengthening what is still a new and underappreciated area of research.

A key aspect of semantic biology is the description of biological, and biology-related, entities using ontologies. Ontologies are a critical requirement for such integration as they allow conclusions drawn about biological experiments, or descriptions of biological entities, to be understandable and integratable despite being contained in different databases and analyzed by different software systems. Ontologies are the standard structures used in biology, and more broadly in computer science, to hold standard terminologies for particular domains of knowledge. They consist of sets of standard terms, which are defined and may have synonyms for ease of searching and to accommodate different usages by different communities. These terms are linked by standard relationships, such as "is\_a" (an eye "is\_a" sense organ) or "part\_of" (an eye is "part\_of" a head). In this way more detailed (granular) terms can be linked to broader terms, allowing computation to be carried out that takes these relationships into account.

The classical biological ontology is the Gene Ontology (GO) (Ashburner et al., 2000) which addresses aspects of gene function, the processes in which they participate and the localization of gene products. Increasingly, semantic biology requires the linkage of these concepts to other biological features. Three such biological entities are included in the Special Topic. The Anatomical Entity Ontology (AEO) (Bard, 2012) provides a typology of anatomical entities across species that is linked to cell types (via links to the cell ontology). Amongst others things, this allows linkage of anatomical structures across species, allowing inferences of homology and comparison of features such as gene and protein expression across species.

Another cross-species ontology, and one that complements work on anatomy, is described by Giudicelli and Lefranc (2012). They provide an update on the IMGT-Ontology which is an ontology of immunogenetics and immunoinformatics used in the international ImMunoGeneTics information system® (http://www*.*imgt*.*org). The IMGT-Ontology describes a range of immunogenetics concepts (immunoglobulins or antibodies, T cell receptors, major histocompatibility (MH) proteins of humans and other vertebrates, proteins of the immunoglobulin superfamily and MH superfamily, related proteins of the immune system of vertebrates and invertebrates, therapeutic monoclonal antibodies, fusion proteins for immune applications, and composite proteins for clinical applications).

A key problem for semantic biology is linking data on phenotypic measurements between model organisms, used to understand human disease, and clinical observations made in humans. This has been an active area of research in recent years (Hancock et al., 2009; Schofield et al., 2010). Shimoyama et al. (2012) make an important contribution to this area by describing a set of ontologies used to describe clinical measurements, measurement methods and experimental conditions for traits common to rat and man (and, by extension, in other mammalian model systems such as mouse and, potentially, more distantly related species). These measurements are similar to those used in large-scale phenotyping experiments (Hancock and Gates, 2011) so that this ontology system provides a potentially valuable mechanism for the study of genotype-phenotype relations in mammals.

Going beyond the underlying ontological structures used to describe biological data Imam et al. (2012) describe an integrated set of ontologies used within the Neuroscience Information Framework (www.neuinfo.org/), which describe major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. This application provides a valuable insight into how sets of existing ontologies can be integrated with novel, more application-specific ontologies and structures to underpin a semantic-based knowledge system. NIF links logically consistent sets of terms into single structures but forms links between these logically consistent sets using bridging modules. Deb (2012) argues for an alternative approach using a single upper level (foundational) ontology to link specific biological domain ontologies.

A key issue that any such framework raises is how to compare and choose appropriate ontologies for any given system. A typical default position in biological applications is to accept the ontologies held in the open biological ontologies set (Smith et al., 2007). Here Klie and Nikoloski (2012) argue that ontology choice is to a degree application-specific and that domain-specific ontologies may in some cases be more useful than general ontologies such as the GO.

The major purpose of developing biological ontologies (rather than simpler controlled vocabularies) is to make use of the relations implicit in ontologies to facilitate analysis and annotation. These topics are addressed by two papers in this series. Ross et al. (2013) describe the use of the PRotein Ontology to carry out cross-species comparisons of function in the spindle checkpoint pathway. Bastos et al. (2013) consider the use of subsets of functionally coherent proteins to improve functional annotation in a protein family.

Finally, advances in technology provide new opportunities for the use of semantically-enriched data in applications that are only minimally ontology-aware. Dönitz and Wingender (2012) describe a web-based service that can be accessed from any application to make use of standard ontologies, removing a significant burden to application development. At a higher level, Deb and Srirama (2013) provide us with a view of how the data and ontologies currently being produced might be linked and accessed via cloud infrastructures and describe some of the problems this raises in the domain of human eHealth.

#### **REFERENCES**

*Received: 09 January 2014; accepted: 21 January 2014; published online: 04 February 2014.*

*Citation: Hancock JM (2014) Editorial: biological ontologies and semantic biology. Front. Genet. 5:18. doi: 10.3389/fgene.2014.00018*

*This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics.*

*Copyright © 2014 Hancock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## The AEO, an ontology of anatomical entities for classifying animal tissues and organs

#### *Jonathan B. L. Bard \**

Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK

#### *Edited by:*

John Hancock, Medical Research Council, UK

#### *Reviewed by:*

Gaurav Sablok, Huazhong Agricultural University, China Qiangfeng Cliff Zhang, Columbia University, USA David Osumi-Sutherland, Information Technology and Services, UK Paula Mabee, University of South Dakota, USA

#### *\*Correspondence:*

Jonathan B. L. Bard, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK. e-mail<EMAIL_ADDRESS>
This paper describes the AEO, an ontology of anatomical entities that expands the common anatomy reference ontology (CARO) and whose major novel feature is a type hierarchy of ∼160 anatomical terms. The breadth of the AEO is wider than CARO as it includes both developmental and gender-specific classes, while the granularity of the AEO terms is at a level adequate to classify simple-tissues (∼70 classes) characterized by their containing a predominantly single cell-type. For convenience and to facilitate interoperability, the AEO contains an abbreviated version of the ontology of cell-types (∼100 classes) that is linked to these simple-tissue types. The AEO was initially based on an analysis of a broad range of animal anatomy ontologies and then upgraded as it was used to classify the ∼2500 concepts in a new version of the ontology of human developmental anatomy (www.obofoundry.org/), a process that led to significant improvements in its structure and content, albeit with a possible focus on mammalian embryos. The AEO is intended to provide the formal classification expected in contemporary ontologies as well as capturing knowledge about anatomical structures not currently included in anatomical ontologies. The AEO may thus be useful in increasing the amount of tissue and cell-type knowledge in other anatomy ontologies, facilitating annotation of tissues that share common features, and enabling interoperability across anatomy ontologies.The AEO can be downloaded from http://www.obofoundry.org/.

**Keywords: anatomical hierarchy, cell-type assignations, ontology, tissue classification**

#### **INTRODUCTION**

Formal anatomical ontologies are now an important component of the informatics infrastructure of model organism and other databases (Bard, 2008; for a review of anatomy ontologies, see the papers in Burger et al., 2008; for examples, see1) and are also a key part of the informatics tools intended to explore biomedical databases. These ontologies primarily use *part\_of* as their main structural relationship (e.g., every heart is *part\_o*f a cardiovascular system) because the smaller anatomical entities (usually referred to as tissues) are naturally seen as the constituent parts of larger ones, albeit that one tissue may be a part of more than one anatomical system (e.g., the *femur* is *part\_of* the *lower limb* and the *skeletal system*). In addition, this relation is particularly important within database schemas for querying such tissue-associated knowledge as gene-expression data (e.g., the totality of the genes expressed in the heart at some developmental stage is the sum of the genes expressed in its parts).

In addition to *part\_of* relationships, anatomical ontologies also need a classification or type hierarchy in which every term is related by an *is\_a* or type relationship to a higher class term (e.g., the *femur is\_a bone*, the *deltoid is\_a muscle*). This relationship is required for three reasons: first, to ground the ontology within a standard formal structure (ontologies are based on classes within

superclasses); second, many ontology visualization tools require this relationship; and third, this classification assigns to a term anatomical knowledge that would otherwise be missing.

An informal way of handling this issue is to indicate tissue type within an anatomy ontology through the use of high-level terms (e.g., leg skeleton, limb muscle system, cranial ganglia) but, while this is sometimes adequate for navigation around the ontology, it cannot be viewed as satisfactory or rigorous because it is based on a part\_of rather than an is\_a or type relationship. A better approach has been to use the common anatomy reference ontology (CARO) to classify anatomical structures (Haendel et al., 2008). This very high-level ontology of anatomical types is intended to provide a coarse framework of low-granularity for referencing the tissues of adult organism on the basis of anatomical level. Its 80 or so terms cover all anatomical classes from a hermaphroditic organism to an epithelium's basal lamina; only about 16 of them, however, can be used to classify tissues and cell-types (e.g., *organ system, compound organ, multi-tissue structure*). The only histological classification in the CARO covers the different types of epithelia; no other tissues (e.g., neuronal, muscular, and mesenchymal) merit a mention.

While the CARO provides a high-level class for a structure of any scale and so can be used to satisfy the requirement that every class have a superclass, its very low-granularity means that it can only annotate the thousands of tissue types that are known with very limited knowledge about anatomical structure. The restrictions of the CARO have been informally discussed within the field

1http://www.obofoundry.org/

for some time and additions are beginning to be made. Thus the curator of the *Drosophila* anatomy ontology needed to add a few new type terms (e.g., *row*) for classifying adult fly tissues. More recently, the vertebrate musculoskeletal anatomy ontology (namespace:VAO) has been produced (see text footnote 1), also using the CARO for its high-level terms, and this ontology meets the need for a new and much richer set of classes for this subset of anatomy. A more serious omission in CARO is that, because it was designed for adult anatomies, it lacks terms for developing tissues, a major focus of many anatomical ontologies. These and other class terms have been included in Uberon (Washington et al., 2009), an integrated cross-species ontology with high-level CARO terms and classified by structure, function, and developmental lineage, but not in any detail by tissue type. It is thus clear that an ontology for anatomy tissues that is both richer and finer-grained than CARO is required if one wishes to include structural knowledge about tissues in anatomy ontologies.

This paper describes the ontology of anatomical entities (AEO), an expansion of the CARO. The AEO is intended to capture and classify knowledge about anatomical structures not currently included in anatomical ontologies and includes ∼100 new classes structured using the *is\_a* relationship. The AEO terms were selected partly through analysis of histology and anatomy books, partly through logical analysis, partly for their use in classifying the new ontology of human developmental anatomy (∼2500 terms) and partly through examination of a range of animal ontologies (whose ids are included where appropriate). The granularity of the AEO terms is at a level adequate for tissues of a predominantly single cell-type and, and these are given through *has\_part* relationships to an abbreviated version of the ontology of cell-types (∼90 classes) included in the AEO. The AEO may be useful in increasing the amount of tissue and cell-type knowledge in other anatomy ontologies, facilitating annotation of tissues that share common features, and enabling interoperability across anatomy ontologies.

#### **MATERIALS AND METHODS**

TheAEO uses the CARO as its basisfor high-level classes. Termsfor the histological information used to link cell-types to tissues came from standard textbooks (e.g., Ross et al., 1995; Standring, 2008; human anatomy is, for obvious reasons, analyzed in far greater depth than that of other organisms). Additional terms came from an analysis of other adult and anatomical ontologies from the biomedical ontologies site, particularly the VAO and, in these cases, the original ids are stored as dbxrefs. All ontologies mentioned in the paper are available from the OBO foundry (see text footnote 1). In this context, it might have been appropriate to incorporate within the AEO the terms and the structure of the VAO. The major skeletal terms from the VAO have been included (with definitions and ids), but the structure of the VAO was not used, mainly because it is much larger, more complex, and more fine-grained than is appropriate for the AEO and partly because some of the finer details of classification is at odds with expectation.

The process of constructing the AEO is described below. In brief, a first draft was made on the basis of inspection of a wide range of anatomical ontologies combined with general reading. This was used to classify the ontology of human developmental anatomy which has ∼2500 concepts. This process exposed weaknesses and omissions that were successively corrected.

Because the granularity of the AEO is designed to include anatomical entities of a single cell-type (*simple-tissue* or its synonym *portion of tissue*), it seemed sensible to include these celltypes within the ontology. While this could have been done using dbxrefs to the cell-type ontology, it seemed more appropriate to include the cell-type terms within the ontology so that a partonomy relationship could be assigned. A subset of the cell-type ontology was therefore included within the AEO and its terms linked to appropriate simple-tissue via the *has\_part* relationship which carries the meaning that tissue A includes within it at least some of cell-type B.

The AEO terms not originally present in the CARO carry AEO ids whose numbers do not overlap with CARO ids (see Discussion) and is authored in the obo format2 using the OBO-Edit3 (Day-Richter et al., 2007.) and CoBrA4 (Aitken et al., 2005) browsers (the former for complex ontologies, the latter for simple ones). Terms also carry appropriate dbxrefs from the *Drosophila*, VAO, zebrafish, Uberon, and human developmental anatomy ontologies. Obo-Edit includes the ability to make *disjoint\_from* links that facilitate inconsistency checking (Rector, 2003) and such links have been made for *male* and *female anatomical structures*, and for *material* and *immaterial anatomical structures*.

The obo ontology is available from the OBO foundry (see text footnote 1). For Protégé users, the OWL version is generated automatically by the OBO Foundry pipeline, and is available from the same URL.

#### **RESULTS**

#### **DESIGN FEATURES**

The key aim of the AEO was to provide at least one unambiguous type term for every tissue in the anatomical ontology for an animal, whether adult, or developing. This turned out to be a more complicated process than originally expected, and what is described below is the final result of a series of iterations as drafts of the AEO were used for annotation (see below). The initial stage in making the AEO involved making a series of choices.

The first decision resulted from considering whether further high-level terms were needed in the CARO, and two omissions were noted: the exclusion of *gender-specific* and *embryonic anatomical entities*. The former was straightforward to add, but the latter, important for anatomy ontologies that cover developing organisms, was more difficult. The problem in choosing subterms here lies in the fact that all tissues in an embryo are developing tissues (even if they are fully functional and just growing, e.g., the late metanephros) and there is little point in annotating every term in an ontology with *is\_a developing tissue*. As a result, a minimalist view was taken here and the terms in the *developing tissue* branch of the ontology were limited to those that were likely to be populated, were not present in an adult organism and had a useful developmental implication (**Figures 1** and **2**). Excluded from the list are any terms that imply lineage (such as may be found in

2http://purl.obolibrary.org/obo/oboformat/spec.html

3http://oboedit.org

4http://www.xspan.org/cobra/index.html

Uberon); this is mainly because there are few if any tight lineage restrictions on tissue morphology. The list is of developmental classes is thus short and may need to be extended.

anatomical entities (red arrow) are expanded.

The second decision focused on the depth of the ontology, and here a CARO definition proved key: the CARO defines a *portion of tissue* as "anatomical structure that consists of similar cells and intercellular matrix, aggregated according to genetically determined spatial relationships." This definition fits comfortably with an anatomist's view of the simplest tissue by implying that it has

a defined boundary and has cells predominantly of a single class (although this definition does raise the occasional problem – see below). One advantage of going down to this simple level of structure was that it enabled each leaf term to be annotated with its cell-types (as detailed in the cell-type ontology).

The third decision in making the AEO lay in choosing the breadth of the hierarchy. The coverage should be good enough to be useful without being overwhelmingly detailed, and anatomists have produced very detailed catalogs of tissue classes: Gray's anatomy (Standring, 2008), for example, lists >8 types of joint, most of which are rare. In making the AEO, all the major animal ontologies (i.e., plant and fungal ontologies are excluded) available

at the OBO library (see text footnote 1) were examined, and terms were chosen on the basis that they were likely to be useful (i.e., populated) and clear in meaning to anatomists. Thus, only the two most common classes of joint (*synovial* and *fibrous joints*) are included in the AEO as specific subclasses of joint; the former *is\_a multi-tissue structure* and the latter *is\_a* a *simple-tissue,* while cartilage is not subdivided. Also excluded are accessory bones (a subclass of sesamoid bones), bursas (a subclass of epithelial sac), and venules and arterioles (because they are all both unnamed and dispersed). Because skeletal terms are so common and useful to anatomists and to evolutionary biologists, it seemed sensible to group them all as parts under a new term *skeletal system*, a subclass of *anatomical system*.

There is afurther small point: the terms of theAEO are intended to be clear in meaning to any biologist: as the ontology is intended for experimentalists who wish to annotate terms and access data, it is therefore important that the terms be those in common use. In this context, no anatomist has an intuitive sense of what the CARO term *portion of tissue* means, so the AEO uses that tem as a synonym for its replacement *simple-tissue* (similarly, the term *portion of organism substance* has been made a synonym of the more intuitively obvious term *non-tissue substance*).

#### **MAKING THE ONTOLOGY**

The major structural additions to the CARO that seemed necessary beyond adding *developmental* and *gender-specific tissues* were the expansion of some top-level class terms such as *immaterial anatomical entity*, *anatomical groups,* and *organism subdivision*, and here it seemed sensible to include the obvious major categories (*head*, *body*, etc., see below). Similarly, the class *multi-tissue structure* was felt to be too broad and in need of subterms, perhaps the most important of which is *tissues with stem cells*.

The class of *immaterial anatomical entities* (i.e., terms that refer to features rather than tissues) is treated lightly in the CARO: its few terms merely specify dimension (*anatomical line*, *point*, *surface,* and *space*). This terseness does less than justice to the richness of surfaces and volumes in organisms so the AEO includes several more terms (**Figure 1**) that can be used to group immaterial entities with common topological features (e.g., *open* and *enclosed cavities*, **Figure 1**). One interesting question here concerned how to class *surface pits* and *grooves* (e.g., the *otic pit*): should they be viewed as anatomical spaces (3D) or as surface features? Perhaps the most logical way to handle this would be to view the cells bounding the feature as a simple-tissue and the enclosed space (with a virtual enclosing surface) as an immaterial anatomic space. This would mean distinguishing between, say, the otic pit space and the otic pit epithelium, but standard anatomical usage implies that the *otic pit* is actually a surface feature within the surface epithelium. After some thought, the latter option was chosen with the user having the further option of annotating the term with a tissue type, so allowing both the cell-type and the geometric feature to be captured. Should a user specifically wish to refer to the space within the pit, the volume can be classified as a *lumen of an epithelial sac*. There is, it should be said, some vagueness in saying that an entity can be both a material and an immaterial entity; the values in doing this are terseness and the ability to captures some sense of tissue geometry, the price is the risk, albeit small, of ambiguity.

The other key task was the choice of simple-tissue leaf terms and this was mainly done on the basis of analyzing anatomy ontologies and histology texts. The net result was a major expansion in the CARO class *simple-tissue* (*portion of tissue*) which now has eight subclasses rather than one, with these subclasses opening up to two further levels which cover a further 60 or so classes (**Figure 3**). One anomalous term that has been included under *neuronal tissue* is *nerve fiber tract*: even though such tracts are composed of axons rather than of complete cells and so are not a tissue in the normal meaning of the word, this term was included because *nerve fiber tracts* are both named and important. As neither the CARO nor the cell-type ontology has a natural class that includes anatomical entities composed of cell parts, the GO definition for *neuron projection bundle* (and GO id dbxref) has been used here (and the synonym included). In a sense, all neuronal tissues are anomalous because the cell bodies and axons are not found within the same structure and it would have seemed odd to have included *nerve fiber tract* under any heading other than *simple-tissue*.

As a result of this, a draft extension to the CARO was constructed with ∼70 new terms.

#### **IMPROVING DRAFT VERSIONS OF THE AEO**

The AEO is intended to provide an *is\_a* link for any anatomical concept. As the initial draft was based on inspection of a range of anatomical ontologies for animals, it met this criterion for most animal tissues. A harsher and finer granularity test was its ability to provide type terms for all the concepts in a detailed anatomical ontology. For this, drafts of the AEO were used to provide an obvious type term for the ∼2500 tissues in the new and integrated ontology of human developmental anatomy (namespace: EHDAA2; current draft available from http://www.obofoundry.org/) which is currently being constructed by the author from one made a decade ago (Hunter et al., 2003) that included a separate ontology for each Carnegie stage (1–20). The process of annotating a very wide range of anatomical classes from major organ systems down to simple-tissues in EHDAA2 identified inadequacies in draft AEO ontologies and required many changes to both the terms and the structure of the AEO. The introduction of *developing anatomical structure* and *gender-specific embryological structure* has already been mentioned (**Figures 1** and **2**). Another example was the amplification of *organism subdivision*. This last category proved useful,for example, in grouping the many and disparate entities within the *head* using *part\_of* relationships (**Figure 5**). As things currently stand, there is at least one easily assignable class term for all anatomical terms so far examined, be it a leaf node (e.g., *metanephric mesenchyme is\_a developing mesenchymal condensation*) or a higher level concept (*somite group is\_a row*).

During this exercise, another ∼30 terms were added. In the current version, 13 of the new terms are classes of *immaterial anatomical entity*, ∼20 are *developing anatomical entities*, ∼40 are new types of simple-tissue, 15 are *multi-tissue structures*, seven are *anatomical groups,* and a few others are distributed under various headings. Further terms can easily be added if users feel that they would be needed. The current version of the AEO thus includes ∼160 anatomical classes and ∼100 cell-types.

**developmental anatomy (EHDAA2).** The ontology is opened in the OBO-Edit browser to demonstrate (i) those tissues classed (is\_a relationship) within the gender-specific anatomical structure hierarchy (red arrow), and (ii) the head category of organism subdivision with its constituent organ groups (part\_of relationship).

#### **Table 1 | The AEO obo file entry for** *autonomic ganglion***.**

#### **(Term)**

#### **ADDING CELL-TYPE RELATIONSHIPS**

Once the AEO was in place, it seemed sensible to supplement the anatomical type information by annotating the leaf tissues of *simple-tissue* and the appropriate subclasses of *multi-tissue structure* with their cell-types and these are formally detailed in the cell-type ontology. This ontology is unnecessarily rich for the fairly simple annotation exercise here and a sub-ontology of the ∼100 cell-type classes that were needed (∼15% of the original ontology) was made on the basis of standard histology textbooks and incorporated within the AEO (**Figure 4**). There is no ideal relationship to convey the sense that a particular cell-type is a major but not exclusive constituent in a particular class of tissue (there may be several cell-types in such relatively simple-tissues as ganglia, epithelia, and mesenchymal domains, **Table 1**). The relationship chosen for the link was *has\_part* and this carries the meaning that tissue A has some part made of cell-type B, as can be seen by inspection of the Obo file (**Table 1**). Unfortunately, browsers require that the relationships be read upward and so the link is under the cell-type rather than under the tissue (**Figure 5**).

Making the *cell-type* to *simple-tissue* relationships was usually straightforward, in the sense that one cell-type could usually be seen as the predominant typefor a tissue, but this was not always so, particularly for the tissues of the nervous system such as ganglions where neurons are always accompanied by support cells.

#### **DISCUSSION**

There were two key reasons for producing theAEO: first, to provide a formal type definition for the ontology of human developmental anatomy so that it would meet modern ontology standards and, second, to enable this and perhaps other anatomy ontologies, which are mainly built from *part\_of* relationships, to increase the amount of anatomical knowledge that they contain. This new ontology had, of course, to be based on the CARO scaffold, as its use is now standard for anatomy ontologies.

In practice, the only problem in using the CARO as a scaffold turned out to derive from the definition of the term *simple-tissue* (or *portion of tissue*) whose definition was "anatomical structure, that consists of similar cells and intercellular matrix, aggregated according to genetically determined spatial relationships." This definition assumes that a single structure (the anatomical term) is composed of essentially similar cells, and, while this is usually so, there are some important exceptions, particularly in the nervous system where ganglia and brain nuclei where neurons,

the key functional cell-type in the nervous system: neurons in ganglia and brain nuclei are always accompanied by support cells (glia, astrocytes, etc.). Another type of structure that could, on the basis of its boundaries, be viewed as simple is the membrane bone which includes osteoblasts, osteoclasts, and osteocytes. Placing such structures within the AEO could not be done in any natural way, and the solution adopted was based on what seemed to be the most appropriate location for a user. *Membrane bone* was made a subclass of *bone*, a multi-tissue structure, while *ganglion* and *neuronal nucleus* were made subclasses of *neuronal structure*, a *simple-tissue*. While these choices are not logically consistent, it is to be hoped that they will not lead to any downstream problems.

Attaching the cell-type terms to the new classes was done relatively late in the production of the AEO as it became clear that it would be quite simple and straightforward to add them from a subset of the cell-type ontology. Although there is always the concern that a user might suppose that the relationships are complete, they are not! It should be emphasized that only key cell-types have been included. Thus, for example, most tissues in an organism have associated blood vessels, nerve endings, and phagocytic cells and these have not been included. The relationship used here is *has\_part* (every *endochondral bone has\_part one or more osteocytes*) and this allows the cell-types to be associated with the tissues in the obo file. Browsers views this relationship in an inverted way and show the tissues associated with a cell-type (**Figure 5**).

A key part of making the ontology was using its classes to annotate the anatomical terms in the ontology of human developmental anatomy (EHDAA2) being revised from that of Hunter et al. (2003). This annotation exercise frequently demanded that new AEO terms be added and occasionally that their location be changed. As a result of annotating the EHDAA2 ontology and glancing through other vertebrate ontologies the terms seem adequate for typing vertebrate tissues. The same amount of attention has not, it should be said, been given to invertebrate ontologies and those working in this area may well find that, if they choose to use the AEO, they will need additions or changes (see below).

The only reason for producing a new ontology is that it should be useful and I hope that the integration of AEO within anatomy ontologies other than that for human developmental anatomy may prove helpful in two contexts at least. First, it would help curators who wish to annotate and users who wish to search on the basis of anatomical structure (e.g., all ducted glands). Second, it would be of value to anyone who wishes to know something about the histology of a tissue and the sort of cells that it contains. In this context, the *simple-tissue* hierarchy may be particularly useful to both groups. In addition, the AEO can rightly be seen as no more than an expansion in breadth and granularity of the CARO and it

#### **REFERENCES**

and Smith, B. (2008). "CARO, the common anatomy reference ontology," in *Anatomy Ontologies for Bioinformatics*, eds A. Burger, D. Davidson, and R. Baldock (New York: Springer), 327–349.

is a fair question as to whether the AEO should absorbed within the CARO with AEO ids becoming CARO ids. This is really a question that the curators of the CARO and of anatomy ontologies other than that for human developmental anatomy will need to answer; if they do decide to do this, the transfer will be easy: as there is no current overlap in id number between the CARO and AEO namespaces, it will merely require a global change in the AEO OBO file of <AEO:> to <CARO:> (provided, of course, that no new terms are added to CARO in the meantime).

The ontology is named the *Anatomical Entity Ontology* and this might seem a little ambitious, given that its focus is primarily on vertebrate and secondarily on invertebrate anatomy, with little attention so far being paid to plants and fungi anatomy. In practice, there are terms within the AEO that can be used to type such tissues (e.g., nectar and sap are non-tissue substance, cambium, and root meristem are developing tissues with stem cells, a *dictyostelium* slug *is\_a* migrating tissue and hyphae *is\_a* epithelial plexus). That said, the AEO does not yet contain specific plant and fungal terms and it is intended that future drafts will include appropriate type term for classifying organisms from these phyla. It is also planned that they will include semantic features automating classification (Rector, 2003; Meehan et al., 2011).

Although drafts of the AEO have been discussed with others (see Acknowledgments), the ontology will inevitably have errors and omissions. Suggestions, comments and criticisms should be sent to<EMAIL_ADDRESS>This and further versions of the ontology will be posted at and downloadable from the OBO foundry http://www.obofoundry.org/. A summary of the ontology can be found at http://www.obofoundry.org/wiki/index.php/AEO:Main\_ Page and this wiki will be used to post details of future changes.

#### **ACKNOWLEDGMENTS**

I thank Richard Baldock,Albert Burger, Duncan Davidson,Melissa Haendel, Gillian Morriss-Kay, and David-Osumi-Sutherland for discussion. I am grateful to Chris Mungall for always being available for email conversations, for commenting on the manuscript, regularizing the relationships, and for maintaining the obo site. I also thank the reviewers for their helpful criticisms.

formalisms including OWL. *Proc. KCAP ACM* 2003, 121–129.

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 07 November 2011; accepted: 28 January 2012; published online: 14 February 2012.*

*Citation: Bard JBL (2012) The AEO, an ontology of anatomical entities for classifying animal tissues and organs. Front. Gene. 3:18. doi: 10.3389/fgene.2012.00018*

*This article was submitted to Frontiers in Bioinformatics and Computational Biology, a specialty of Frontiers in Genetics.*

*Copyright © 2012 Bard. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

## IMGT-ONTOLOGY 2012

#### *Véronique Giudicelli and Marie-Paule Lefranc\**

IMGT®, the international ImMunoGenetics information system®, Université Montpellier 2, Laboratoire d'ImmunoGénétique Moléculaire, Institut de Génétique Humaine, UPR CNRS, Montpellier, France

#### *Edited by:*

John Hancock, Medical Research Council, UK

#### *Reviewed by:*

Ruth Lovering, University College London, UK Anna Maria Masci, Duke University, USA

#### *\*Correspondence:*

Marie-Paule Lefranc, IMGT®, the international ImMunoGenetics information system®, Université Montpellier 2, Laboratoire d'ImmunoGénétique Moléculaire, Institut de Génétique Humaine, UPR CNRS 1142, 141 rue de la Cardonille, 34396 Montpellier cedex 5, France. e-mail: Marie-Paule.Lefranc@igh. cnrs.fr

Immunogenetics is the science that studies the genetics of the immune system and immune responses. Owing to the complexity and diversity of the immune repertoire, immunogenetics represents one of the greatest challenges for data interpretation: a large biological expertise, a considerable effort of standardization and the elaboration of an efficient system for the management of the related knowledge were required. IMGT®, the international ImMunoGeneTics information system® (http://www.imgt.org) has reached that goal through the building of a unique ontology, IMGT-ONTOLOGY, which represents the first ontology for the formal representation of knowledge in immunogenetics and immunoinformatics. IMGT-ONTOLOGY manages the immunogenetics knowledge through diverse facets that rely on the seven axioms of the Formal IMGT-ONTOLOGY or IMGT-Kaleidoscope: "IDENTIFICATION," "DESCRIPTION," "CLASSIFICATION," "NUMEROTA-TION," "LOCALIZATION," "ORIENTATION," and "OBTENTION." The concepts of identification, description, classification, and numerotation generated from the axioms led to the elaboration of the IMGT® standards that constitute the IMGT Scientific chart: IMGT® standardized keywords (concepts of identification), IMGT® standardized labels (concepts of description), IMGT® standardized gene and allele nomenclature (concepts of classification) and IMGT unique numbering and IMGT Collier de Perles (concepts of numerotation). IMGT-ONTOLOGY has become the global reference in immunogenetics and immunoinformatics for the knowledge representation of immunoglobulins (IG) or antibodies, T cell receptors (TR), and major histocompatibility (MH) proteins of humans and other vertebrates, proteins of the immunoglobulin superfamily (IgSF) and MH superfamily (MhSF), related proteins of the immune system (RPI) of vertebrates and invertebrates, therapeutic monoclonal antibodies (mAbs), fusion proteins for immune applications (FPIA), and composite proteins for clinical applications (CPCA).

**Keywords: IMGT, immunogenetics, immunoinformatics, IMGT-ONTOLOGY, immunoglobulin, antibody, T cell receptor, immune repertoire**

#### **INTRODUCTION**

Immunogenetics is the science that studies the genetics of the immune system and immune responses. Among them, the adaptive immune response, acquired by vertebrates with jaws or *gnathostomata*, is characterized by an extreme diversity of the specific antigen receptors that comprise the immunoglobulins (IG) or antibodies and the T cell receptors (TR). The potential repertoire of each individual is estimated to comprise about 2 × 1012 different IG and TR, and the limiting factor is only the number of B and T cells that an organism is genetically programmed to produce. This huge diversity results from the complex and unique molecular synthesis and genetics of the antigen receptor chains that include DNA molecular rearrangements (combinatorial diversity) in multiple loci (three for IG and four for TR in humans) located on different chromosomes (four in humans), nucleotide deletions and insertions at the rearrangement junctions (or N-diversity) and, for the IG, somatic hypermutations (for review see Lefranc and Lefranc, 2001a,b).

Owing to the complexity and diversity of the immune repertoires and their implications in fundamental and medical research, immunogenetics represents one of the greatest challenges for data

interpretation: a large biological expertise, a considerable effort of standardization and the elaboration of an efficient system for the management of the related knowledge were required. To answer that challenge, IMGT®, the international ImMunoGeneTics information system® (http://www.imgt.org) was created in 1989 by the Laboratoire d'ImmunoGénétique Moléculaire LIGM (Université Montpellier 2 and CNRS) at Montpellier, France (Lefranc et al., 2009; Lefranc, 2011a). IMGT® has become the global reference in immunogenetics and immunoinformatics. IMGT® is a highquality integrated knowledge resource that provides a common access to standardized data from genome, proteome, genetics, two-dimensional (2D) and three-dimensional (3D) structures. It comprises 7 databases (sequence, gene, structure and specialist databases), 17 online tools and more than 15,000 pages of web resources (Lefranc et al., 2009).

IMGT® has reached that goal through the building of a unique ontology, IMGT-ONTOLOGY started in 1989 and, since then, in constant evolution and extension (Giudicelli and Lefranc, 1999; Lefranc et al., 2004, 2005a, 2008; Duroux et al., 2008; Lefranc, 2011b,c,d,e,f, 2013). IMGT-ONTOLOGY represents the first ontology for the formal representation of knowledge in

immunogenetics and immunoinformatics. IMGT-ONTOLOGY manages the immunogenetics knowledge through diverse facets that rely on the seven axioms of the Formal IMGT-ONTOLOGY or IMGT-Kaleidoscope: "IDENTIFICATION," "DESCRIPTION," "CLASSIFICATION," "NUMEROTATION," "LOCALIZATION," "ORIENTATION," and "OBTENTION" (Duroux et al., 2008). These axioms postulate that any object, any process and any relation has to be identified, described, classified, numbered, localized, and orientated, and that the way it is obtained can be characterized. The IMGT-ONTOLOGY concepts were generated from these axioms. The concepts of identification, description, classification, and numerotation led to the elaboration of the IMGT® standards that constitute the IMGT Scientific chart: IMGT® standardized keywords (concepts of identification), IMGT® standardized labels (concepts of description), IMGT® standardized IG and TR gene and allele nomenclature (concepts of classification) and IMGT unique numbering and IMGT Collier-de-Perles (concepts of numerotation). One major feature of IMGT-ONTOLOGY is the formalization of the specific relations that link, on a semantic point of view, the different concepts and capture the immunogenetics complexity. These relations are fundamental for data consistency and biological interpretation.

#### **MATERIALS AND METHODS**

An ontology is defined as "an explicit specification of a conceptualization" (Gruber, 1993; Guarino and Giaretta, 1995; Guarino, 1997). The building of IMGT-ONTOLOGY has consisted in the conceptualization and in the formalization of the related knowledge in immunogenetics, and in the definition of the relations between concepts. The first concepts were defined as "relevant and fundamental criteria which are needed to characterize IG and TR data" (Giudicelli and Lefranc, 1999). Since then, the IMGT-ONTOLOGY concepts have been largely extended to molecular components other than IG and TR, that include major histocompatibility (MH) proteins of humans and other vertebrates, proteins of the immunoglobulin superfamily (IgSF), and MH superfamily (MhSF), related proteins of the immune system (RPI) of vertebrates and invertebrates, therapeutic monoclonal antibodies (mAbs), fusion proteins for immune applications (FPIA), and composite proteins for clinical applications (CPCA).

Concepts are characterized by their properties which may be simple attributes or relations between concepts. The relation of subsumption (is\_a) allows to structure the IMGT-ONTOLOGY concepts, and to represent them as nodes of the graph with their level of granularity. The concepts that correspond to the finest level of granularity (and the highest level of precision) in branches of the graph are designated as "leafconcept."Concepts from which a hierarchy is generated with several levels before reaching the leafconcepts are designated as "highconcept."

IMGT-ONTOLOGY is being formalized in OWL-DL1 language using the Protégé editor2 (Noy et al., 2003). The formalized concepts of identification are available for downloading or browsing on the National Center for Biomedical Ontology (NCBO) BioPortal3 (Noy et al., 2009; Musen et al., 2012) and on the IMGT® web site (http://www.imgt.org; Lefranc, 2011a,b,c,d,e,f).

The semantic relations (other than subsumption) are formalized as OWL object properties (see OWL 2 Web Ontology Language http://www.w3.org/TR/owl-primer/): Object properties allow to link specifically two concepts through the statement "Subject > Property > Object" where "Subject" is the concept being characterized by the object property, "Property" the name of a given property defined in the ontology and "Object" the name of the concept that is linked. These properties are restricted using in particular universal quantification (all connected individuals by the property must be instances of a given class), existential quantification (all individuals of the class for which the property is defined are connected to at least one individuals of the class mentioned in the restriction) and cardinality restrictions (quantification of the number of connected individual with the property). These relations can be displayed on NCBO BioPortal in "IMGT-ONTOLOGY > Terms > Details"page. They are indicated in the "Equivalent Class" section if they are necessary and sufficient to define the concept, or in the "Sub Class Of" section if they are necessary only (for instance, the relations "is\_defined\_by" and "\_has\_" of the "D-gene" (which is a "Molecule\_EntityType" leafconcept, see below"Molecule\_EntityType"Concept), are examples of relations in "Equivalent Class" and "Sub Class Of" sections, respectively). The formalization of these relations highlights and focuses on the dependencies between the terms that are closely interconnected at the level of immunogenetics knowledge and set up the constraints that must be respected in the IMGT® databases and tools and in immunoinformatics.

#### **RESULTS**

#### **IMGT-ONTOLOGY IDENTIFICATION AXIOM**

The IDENTIFICATION axiom of the Formal IMGT-ONTOLOGY or IMGT-Kaleidoscope (Duroux et al., 2008) postulates that, for molecular components, any molecule and its relations have to be identified (Lefranc, 2011b). IMGT-ONTOLOGY concepts of identification generated from the IDENTIFICATION axiom led to the IMGT® standardized keywords for molecular components (IG, TR, MH, RPI, FPIA, and CPCA) in IMGT® databases and tools.

#### *IMGT-ONTOLOGY concepts of identification*

*"Molecule\_EntityType" concept.* The objective of IMGT-ONTOLOGY was to identify the type of any molecular entity at each step of its synthesis. An insight of the knowledge related to the synthesis of an IG is schematized in **Figure 1**. It illustrates the concept of "Molecule\_EntityType" and the other related concepts of identification and the relations that link them.

The "Molecule\_EntityType" concept is fully defined by the concepts of "MoleculeType," "GeneType," and "ConfigurationType" (**Figure 2**).

– "MoleculeType" allows one to identify the type of molecule, based on the type of the constitutive elements and on the concepts of obtention. The "MoleculeType" concept comprises four major leafconcepts: "gDNA," "mRNA," "cDNA," "protein."

1http://www.w3.org/TR/owl2-overview/

2http://protege.stanford.edu

3http://bioportal.bioontology.org/ontologies/1491

The "Molecule\_EntityType" concept comprises 38 leafconcepts (**Table 1**). For examples, "V-gene" identifies, for gDNA, molecule entities with a germline V gene, "V-D-J-gene" identifies, for gDNA, molecule entities with rearranged V, D, and J genes, and "L-V-D-J-C-sequence" identifies, for cDNA, molecule entities with rearranged V, D, and J genes spliced to a C gene. The four "MoleculeUnit"leafconcepts that are"gene" (10),"transcript" (11), "sequence" (11), and "chain" (6) identify the type of entities based on the "MoleculeType" only, as indicated by the suffix (**Table 1**).

In addition to the relation "is\_defined\_by," a "Molecule\_EntityType" "has" properties identified in the "FunctionalityType" and "StructureType" concepts (**Figure 2**).

The semantic relations of "Molecule\_EntityType" are formalized as properties (in OWL).

#### *"ChainType," "DomainType," and "ReceptorType" concepts.*

One of the goals of IMGT-ONTOLOGY has been to represent knowledge in order to manage molecular components from

sequences to 3D structures in IMGT® databases and tools. The three concepts "ChainType," "DomainType," and "ReceptorType" have been fundamental in that knowledge representation.

"ChainType" is a concept of identification that allows one to identify the type of chain. "ChainType" is a "highconcept" that comprises four levels (**Figure 3**): "MolecularComponentLevel-ChainType," "ReceptorLevelChainType," "ClassLevelChainType," and "GeneLevelChainType." The concepts are organized in an acyclic graph based on the subsumption relation, the depth of which depends on the precision that needs to (or that can be) reported for the data identification. The finest level of granularity, the "GeneLevelChainType" concept, identifies the type of chain by reference to the gene(s) which code(s) the chain. It represents the main concept for a very precise identification because it establishes a relationship with "Gene" (concept of classification) (the reciprocal relations are: "is\_coded\_by" and "codes"). The number of "ChainType" leafconcepts of the "GeneLevelChainType" depends on the number of functional genes and ORF ("FunctionalityType") per haploid genome, in a given species (in the case of the IG and TR genes, it is the number of functional and ORF C genes which is taken into account).

The "ChainType" concept is defined by the "Molecule\_EntityType"and the"DomainType"concepts of identification, and also defined by concepts of classification (see IMGT-ONTOLOGY CLASSIFICATION Axiom) as the type of chain

depends on the taxon (**Figure 4**). "DomainType" allows one to identify the type of domain. A domain is a chain subunit characterized by its three-dimensional (3D) structure, and by extension its amino acid sequence and the nucleotide sequence which encodes it.

The "ChainType" concept represents a key concept that allows to link the "Molecule\_EntityType" (sequences in databases) to the concept of "ReceptorType" (3D structures in databases; **Figure 4**). "ReceptorType" allows one to identify the type of receptor. "ReceptorType"is defined by the "ChainType"leafconcept(s) that identify the associated chains of a receptor. "ReceptorType" is a "highconcept" with a hierarchy of four levels of granularity (depending on the "ChainType" hierarchy). The "ReceptorType" concept has properties identified in the "FormatType," "SpecificityType," and "FunctionType" concepts (**Figure 4**; Lefranc, 2011b).

#### *IMGT® standardized keywords in databases and tools*

The leafconcepts of identification are IMGT® standardized keywords in the IMGT® databases and tools (Lefranc, 2005). The list of IMGT® standardized keywords is available from the IMGT/LIGM-DB database (Giudicelli et al., 2006) query page (IMGT® Home page; http://www.imgt.org) and in the IMGT Scientific chart at http://www.imgt.org/IMGTScientificChart/ SequenceDescription/IMGT3Dkeywords.html. More than 325 IMGT® standardized keywords (189 for sequences and 137 for

\*Indicates the 10 leafconcepts that are the most classical representatives of IG and TR identification. These leafconcepts are illustrated in *Figure 1*.

3D structures) were precisely defined. They represent the controlled vocabulary assigned during the annotation process and allow standardized search criteria for querying the IMGT® databases and for the extraction of sequences and 3D structures. IMGT/HighV-QUEST, the IMGT® tool for analysis of IG and TR nucleotide sequences obtained from next generation sequencing (NGS; Alamyar et al., 2012), provides an evaluation of the configuration ("ConfigurationType") and, accordingly, of the sequence functionality ("FunctionalityType"): such precision and standardization in the NGS results are of the utmost importance for the reuse of data for the statistical analyses required for the comparison of immune repertoires (Prabakaran et al., 2012) and for data interpretation.

#### **IMGT-ONTOLOGY DESCRIPTION AXIOM**

The DESCRIPTION axiom of the Formal IMGT-ONTOLOGY or IMGT-Kaleidoscope (Duroux et al., 2008) postulates that, for molecular components, any molecule and its relations have to be described (Lefranc, 2011c). IMGT-ONTOLOGY concepts of description generated from the DESCRIPTION axiom led

to the IMGT® standardized labels for molecular components (IG, TR, MH, RPI, FPIA, and CPCA) in IMGT® databases and tools.

#### *IMGT-ONTOLOGY concepts of description*

Concepts of description have been progressively elaborated in order to take into account the entities of the different steps of

The "ChainType" concept is itself defined by the "Molecule\_EntityType" and "DomainType" concepts and by concepts of classification organized in a hierarchy (see CLASSIFICATION axiom). Arrows indicate reciprocal

the molecular synthesis of the antigen receptors (IG and TR) and, more generally, of all molecular components and to describe all motifs of biological interest of sequences and 2D and 3D structures in databases and tools.

*"Molecule\_EntityPrototype" concept.* The "Molecule\_EntityPro totype" is a concept, generated from the DESCRIPTION axiom, that provides the description of the "Molecule\_EntityType" concept (IDENTIFICATION axiom). There are as many leafconcepts in the "Molecule\_EntityPrototype" as there are leafconcepts in the "Molecule\_EntityType." Thus the "Molecule\_EntityPrototype" comprises 38 leafconcepts that describe the organization of each entity with its constitutive motifs and relations. Each "Molecule\_EntityPrototype" leafconcept is linked to a "Molecule\_EntityType" leafconcept by the reciprocal relations "describes" and "is\_described\_by." For example, a "V-gene" is described by "V-GENE," and a "V-D-J-gene" by "V-D-J-GENE." Leafconcepts of description (labels in the IMGT® databases and tools) are written in capital letters.

*Prototypes and relations between concepts of description.* In order to visualize the organization of each entity, prototypes were concepts are "\_has\_" and "\_for\_." The "Cell\_EntityType" (not developped in the current version) is part of the "CellularComponent" concept (Pappalardo et al., 2010).

defined. A prototype is a graphical representation of a "Molecule\_EntityPrototype" leafconcept. Two prototypes of "V-GENE" and "V-D-J-GENE" are shown in **Figure 5** as examples of a germline entity and of a rearranged entity, respectively. Twentyseven labels for "V-GENE" and 33 labels for "V-D-J-GENE" (20 of them being shared by the two prototypes), on a total of 277 different labels for sequences in IMGT/LIGM-DB, are necessary and sufficient for a complete description of these prototypes. The organization of a prototype is based on the relations that order two labels.

IMGT-ONTOLOGY formalizes the topological relations that define the relative position of two labels. A set of twelve relations are necessary and sufficient to describe the relations between labels in a prototype (Duroux et al., 2008; Lane et al., 2010; **Table 2**). The reciprocal relations "is\_in\_5\_prime\_of" and "is\_in\_3\_prime\_of" describe the relative position of labels on a 5- –3- DNA strand when there is no intersection or contiguity between labels (Lane et al., 2010).

#### *IMGT® standardized labels in databases and tools*

The leafconcepts of description are IMGT® standardized labels in the databases and tools (Lefranc, 2005). The IMGT®

#### **Table 2 | IMGT-ONTOLOGY relations between labels used for the description of prototypes.**

standardized labels are available from the IMGT/LIGM-DB database (Giudicelli et al., 2006) query page (IMGT® Home page; http://www.imgt.org) and in the IMGT Scientific chart at: http://www.imgt.org/IMGTScientificChart/SequenceDescription/ IMGT3Dkeywords.html (definitions of these labels are available at: http://www.imgt.org/IMGTScientificChart/SequenceDescrip tion/IMGT3Dlabeldef.html). More than 560 IMGT® standardized labels (277 for sequences and 285 for 3D structures) were precisely defined.

IMGT/Automat, the IMGT® tool for the annotation of rearranged cDNA (Giudicelli et al., 2005a) implements corresponding labels and prototypes. IMGT® standardized labels and the organization of "Molecule\_EntityPrototype" have recently been implemented in IMGT/LIGMotif for the automation of the annotation of large genomic sequences (Lane et al., 2010). A set of specific labels was defined to describe the different organizations of IG and TR genes in clusters at the scale of the locus or of the chromosome.

#### **IMGT-ONTOLOGY CLASSIFICATION AXIOM**

The CLASSIFICATION axiom of the Formal IMGT-ONTOLOGY or IMGT-Kaleidoscope (Duroux et al., 2008) postulates that, for molecular components, any molecule and its relations have to be classified (Lefranc, 2011d). IMGT-ONTOLOGY concepts of classification generated from the CLASSIFICATION axiom led to the IMGT® standardized IG and TR gene and allele nomenclature.

#### *IMGT-ONTOLOGY concepts of classification*

The IMGT® standardized gene and allele nomenclature is based on the concepts of classification, generated from the CLASSIFICA-TION axiom, which defines the principles for the nomenclature of highly polymorphic multigene loci and families. In particular, the concepts of classification have allowed to classify the genes whatever the antigen receptor (IG or TR), whatever the locus (e.g., for mammals, immunoglobulin heavy IGH, immunoglobulin kappa IGK, immunoglobulin lambda IGL, T cell receptor alpha TRA, T cell receptor beta TRB, T cell receptor gamma TRG, and T cell receptor delta TRD), whatever the gene configuration (germline, undefined, or rearranged), and whatever the species, from fish to human. Among the concepts of classification, the "Group," "Subgroup," "Gene," and "Allele" concepts are essential for the IMGT® gene nomenclature (Giudicelli and Lefranc, 1999). They are shown with their semantic relations in **Figure 6** that are used for the V gene designation.

#### *IMGT® standardized IG and TR gene and allele nomenclature*

In the context of the gene and allele classification, ontological principles defined in IMGT-ONTOLOGY have preceded the IMGT® standardized gene and allele nomenclature. This has been true for the human genes, and all IMGT® IG and TR gene names (Lefranc, 2000a,b; Lefranc and Lefranc, 2001a,b) were defined before the complete human genome sequencing (Lander et al., 2001; Venter et al., 2001). This is still the case for newly sequenced genomes and the denomination of IG and TR genes from a newly sequenced species is considerably facilitated by the preexisting nomenclature principles and rules. Full IMGT® standardized gene name comprises the latin names of the genus and species (e.g., *Homo sapiens* IGHV1-2). Gene names used in natural language and in publications may include abbreviation if needed for tables or figures (6-letter code for genus and species, 9-letter code for genus, species, and subspecies).

#### *Interoperability between IMGT, HGNC, and NCBI*

Since the creation of IMGT®, the international ImMunoGeneTics information system® in 1989, at New Haven during the 10th Human Genome Mapping Workshop (HGM10), the standardized classification and nomenclature of the IG and TR of humans and other vertebrate species have been under the responsibility of the IMGT Nomenclature Committee (IMGT-NC). The IMGT® gene nomenclature for human IG and TR genes (Lefranc, 2000a,b; Lefranc and Lefranc, 2001a,b) was approved by the Human Genome Organisation (HUGO) Nomenclature Committee (HGNC) in 1999 (Wain et al., 2002) and endorsed by the World Health Organization-International Union of Immunological Societies (WHO-IUIS; Lefranc, 2007, 2008). IMGT® IG and TR gene names are the official international reference and have been entered in IMGT/GENE-DB, the IMGT® gene database (Giudicelli et al., 2005b), in the Human Genome Database (GDB; Letovsky et al., 1998), in LocusLink at the National Center for Biotechnology Information (NCBI) in 1999–2000 (Maglott et al., 2000), in NCBI Entrez Gene when this gene database superseded LocusLink (Maglott et al., 2007), in NCBI Gene and in NCBI MapViewer, in Ensembl at the European Bioinformatics Institute (EBI) in 2006 (Hubbard et al., 2002), and in the Vega Genome Browser at the Wellcome Trust Sanger Institute (Ashurst et al., 2005). Amino acid sequences of human IG and TR C genes were provided to UniProt in 2008 (Bairoch et al., 2009). Close collaborations have been developed to maintain interoperability between the databases, with HGNC (Wain et al., 2004; Bruford et al., 2008), NCBI Gene (Maglott et al., 2011), Ensembl,Vega (Wilming et al.,2008), theMouse Genomic Nomenclature Committee (MGNC), the Nomenclature Committees of newly sequenced genomes, for example, ZFIN for the zebrafish *Danio rerio* (Bradford et al., 2011) or external team contribution, for example, TRB locus of the rhesus macaque *Macaca mulatta* (Greenaway et al., 2009). IG and TR genes are also integrated in the HUGO ontology and NCI Metathesaurus available on the NCBO BioPortal4. Mapping between the HUGO ontology and IMGT-ONTOLOGY will be developed with the formalization of the concepts of classification in OWL.

#### **IMGT-ONTOLOGY NUMEROTATION AXIOM**

The NUMEROTATION axiom of the Formal IMGT-ONTOLOGY or IMGT-Kaleidoscope (Duroux et al., 2008) postulates that, for molecular components, any molecule and its relations have to be numbered (Lefranc, 2011e,f). Two major IMGT-ONTOLOGY concepts of numerotation generated from the NUMEROTATION

4http://bioportal.bioontology.org/ontologies/

axiom comprises the "IMGT\_unique\_numbering" and "IMGT\_ Collier\_de\_Perles" (IMGT unique numbering and IMGT Colliers de Perles in IMGT® databases and tools).

#### *"IMGT\_unique\_numbering"*

The "IMGT\_unique\_numbering" concept (Lefranc, 2011e) defines a systematic and coherent numbering (amino acids and codons) for the description of "DomainType" leafconcepts. The "IMGT\_unique\_numbering" was originally defined for the IG and TR V-DOMAIN (Lefranc, 1997). It provides a standardized delimitation of the framework regions (FR-IMGT) and complementarity determining regions (CDR-IMGT), and therefore allows to correlate each position (amino acid or codon) with the structure (beta strand, loop, beta turn) and the function (antigen binding) of the V-DOMAIN. FR-IMGT and CDR-IMGT lengths became a major property of the IG and TR V-DOMAIN. The "IMGT\_unique\_numbering" concept has been extended to the V-LIKE-DOMAIN of the IgSF other than IG and TR (Lefranc, 1999; Lefranc et al., 2003), to the C domain (C-DOMAIN of IG and TR and C-LIKE-DOMAIN of IgSF other than IG and TR; Lefranc et al., 2005b) and to the G domain (G-DOMAIN of MH and G-LIKE-DOMAIN of MhSF other than MH) (Lefranc et al., 2005c). Thus, the "IMGT\_unique\_numbering" concept allows to number domain types that are characteristic of protein superfamilies, whatever the species, the molecule type or the chain type. Three leafconcepts have been defined for the variable (V) domain, the constant (C) domain, and the groove (G) domain: "IMGT\_unique\_numbering\_ for\_V\_domain" (Lefranc, 1997, 1999; Lefranc et al., 2003) and "IMGT\_unique\_numbering\_for\_C\_domain" (Lefranc et al., 2005b) of the IG, TR and IgSF, and "IMGT\_unique\_numbering\_ for\_G\_domain" (Lefranc et al., 2005c) of the MH and MhSF.

#### *"IMGT\_Collier\_de\_Perles"*

The "IMGT\_Collier\_de\_Perles" concept (Lefranc, 2011f) corresponds to the graphical 2D representation of domains based on the set of rules defined by the "IMGT\_unique\_numbering." This original and unique approach allows one to bridge the gap between sequences and 2D and 3D structures and greatly facilitates the domain comparison, position per position. Three leafconcepts are defined: "IMGT\_Collier\_de\_Perles for\_V\_domain" (Lefranc, 1999; Lefranc et al., 2003), "IMGT\_Collier\_de\_Perles\_for\_C\_ domain" (Lefranc et al., 2005b) and "IMGT\_Collier\_de\_Perles for\_G\_domain" (Lefranc et al., 2005c).

**Figure 7** shows graphical representations of "IMGT\_Collier\_ de\_Perles\_for\_V\_domain" (Lefranc et al., 2003). The five highly conserved amino acids found in IG and TR V domains, whatever the species and molecule type, are highlighted (online in red letters): at position 23 (1st-CYS, or first conserved cysteine C), 41 (CONSERVED-TRP, or conserved tryptophan W), 89 (hydrophobic amino acid, here methionine M), 104 (2nd-CYS, or second conserved cysteine C), and 118 (here J-PHE, or J-REGION tryptophan W). This leafconcept allows, for the first time, to compare domains of IG and TR (V-DOMAIN) and of IgSF proteins other than IG or TR (V-LIKE-DOMAIN), on one layer (facilitating comparison with sequences) or on two layers (bridging comparison with 3D structures).

**Figure 8** shows graphical representations of "IMGT\_Collier\_ de\_Perles\_for\_G\_domain" (Lefranc et al., 2005c). This leafconcept allows, for the first time, to compare domains of the same chain (G-ALPHA1 and G-ALPHA2 of MH1), domains of different chains of the same receptor (G-ALPHA and G-BETA of MH2), or domains of MhSF proteins other than MH (G-ALPHA1-LIKE and G-ALPHA2-LIKE of RPI-MH1Like).

#### *IMGT unique numbering and IMGT Collier de Perles in databases and tools*

The IMGT unique numbering and the IMGT Colliers de Perles are used for the numbering of both the codons (in nucleotide sequences) and the amino acids (in protein sequences and structures;Ruiz and Lefranc,2002;Garapati and Lefranc,2007;Kaas and Lefranc, 2007; Kaas et al., 2007). By facilitating the comparison of residues between sequences, the IMGT unique numbering and the IMGT Colliers de Perles have been the basis for the description of the IG and TR gene allelic polymorphism and for the studies of IG somatic hypermutations in V-DOMAIN. They represent a major breakthrough for the analysis and the comparison of the huge repertoires of antigen receptors (potentially 2 × 1012 per individual). Indeed, the IMGT unique numbering and the IMGT Colliers de Perles represent a key component in immunogenetics studies by creating a strong and reliable interoperability between the IMGT® databases, tools, and web resources (Lefranc et al., 2009).

Rules for the IMGT unique numbering are implemented in IMGT® online tools: for the analysis of IG and TR rearranged cDNA sequences by IMGT/V-QUEST (Brochet et al., 2008; Giudicelli et al., 2011) and IMGT/JunctionAnalysis (Yousfi Monod et al., 2004; Bleakley et al., 2006; Giudicelli and Lefranc, 2011), for the analysis of cDNA sequences from high-throughput NGS sequencing by IMGT/HighV-QUEST (Alamyar et al., 2012) and for the analysis of amino acid sequences and 2D structures by IMGT/DomainGapAlign (Ehrenmann and Lefranc, 2011a), IMGT/DomainDisplay and IMGT/Collier-de-Perles (Ehrenmann et al., 2011). They are also implemented in IMGT® databases, and particularly in IMGT/3Dstructure-DB (Ehrenmann et al., 2010a; Ehrenmann and Lefranc, 2011b) where they have been fundamental in the setting up of the standardized definition of contact analysis (Kaas and Lefranc, 2005; Kaas et al., 2008; Ehrenmann et al., 2010a) and of paratope and epitope in crystal structures (Lefranc, 2009; Ehrenmann et al., 2010b).

The IMGT Colliers de Perles are particularly useful in molecular engineering and antibody humanization design based on CDR grafting. Indeed they allow to precisely define the CDR-IMGT and to easily compare the amino acid sequences of FR-IMGT and CDR-IMGT between the mouse (or other species) and the closest human V-DOMAIN (Lefranc, 2009; Ehrenmann et al., 2010b). Analyses performed on humanized therapeutic antibodies underline the importance of a correct delimitation of the CDR regions to be grafted (Magdelaine-Beuzelin et al., 2007). The IMGT Colliers de Perles also allow a comparison to the IMGT Colliers de Perles statistical profilesfor the human expressed IGHV, IGKV, and IGLV repertoires. These statistical profiles are based on the definition of 11 IMGT amino acid physicochemical characteristics classes which take into account the hydropathy, volume, and chemical characteristics of the 20 common

**representation of a V-DOMAIN as an example.** A similar topology and 3D structure characterize a V-LIKE-DOMAIN. **(B)** and **(C)** V-DOMAIN on one layer and on two layers, respectively (Mus musculus VH [8.8.12]). **(D)** V-LIKE-DOMAIN on two layers (Homo sapiens CD28 [9.9.13]). Amino acids are shown in the one-letter abbreviation. Positions at which hydrophobic amino acids (hydropathy index with positive value: I, V, L, F, C, M, A) and tryptophan (W) are found in more than 50% of analyzed sequences are shown online in blue. All proline (P) are shown online in yellow. The loops BC, C- C-and FG (corresponding to the CDR-IMGT) are limited by amino acids

amino acids (Pommié et al., 2004). This comparison is useful to identify potential immunogenic residues at given positions in chimeric or humanized antibodies or to evaluate immunogenicity of therapeutic antibodies.

#### **DISCUSSION**

show, in the forefront, the GFCC-

Ehrenmann and Lefranc, 2011b)].

The standardization, the consistency and the reliability of the immunogenetics data in IMGT®, the international ImMuno-GeneTics information system® (http://www.imgt.org) rely on

(FR-IMGT). BC loops are represented online in red, C-

FG loops in purple. Hatched circles or squares correspond to missing positions according to the IMGT unique numbering for V domain (Lefranc et al., 2003). Arrows indicate the direction of the beta strands and their designations in 3D structures. The IMGT Colliers de Perles on two layers

> C--

strands. The chain identifiers to which the domains belong are 1a3l\_H for **(A)**, **(B)**, **(C)**, and 1yjd\_C for **(D)** [IMGT/3Dstructure-DB (Ehrenmann et al., 2010a;

C--

strands and, in the back, the ABED

loops in orange and

HLA-DRB1∗0101). **(D)** G-LIKE-DOMAIN of RPI-MH1Like: G-ALPHA1-LIKE and G-ALPHA2-LIKE (Mus musculus CD1D1). Amino acids are shown in

IMGT-ONTOLOGY, elaborated since 1989 in order to manage, to share and to represent the immunogenetics knowledge (Giudicelli and Lefranc, 1999; Lefranc et al., 2004, 2005a; 2008; Duroux

IMGT-ONTOLOGY has been developed to be used by any scientific domain which deals with immunogenetics. This includes fundamental, medical, veterinary, clinical, pharmaceutical and biotechnological research. Closely related terms have been integrated in some other biological ontologies (**Table 3**). Chain types have been included in NCI Thesaurus, Logical Observation Identifier Names and Codes (LOINC), Molecule role (INOH Protein name/family name ontology) (IMR), National Drug File Reference Terminology (NDRFT). IMGT® standardized labels that describe

et al., 2008; Lefranc, 2011a,b,c,d,e,f, 2013).

specifically IG and TR sequences and 3D structures and 64 of the IMGT® standardized labels, in particular those for genomic sequences, have been included in Sequence Ontology (SO; Eilbeck et al., 2005) and in SNP-Ontology. IG and TR gene names were entered in HUGO and NCI Metathesaurus (**Table 3**). These ontologies are available on the NCBO BioPortal (Noy et al., 2009),

2011b)].

IMGT® standards derived from IMGT-ONTOLOGY concepts allow interoperability between external databases and tools. Interoperability between IMGT®, HGNC, NCBI, Ensembl, and Vega for the concepts of classification has been described (see Interoperability between IMGT, HGNC, and NCBI). The IMGT numbering is integrated in external Web resources: it is proposed, for

opening opportunities of mapping with them.

(/number) indicates, for ontologies at NCBO BioPortal, the identifiant to be added to http://bioportal.bioontology.org/ontologies.

a Giudicelli and Lefranc (1999), Lefranc et al. (2004, 2005a, 2008), Duroux et al. (2008), Lefranc (2013).

b–fLefranc (2011b,c,d,e,f).

example, as domain system numbering in the sequence analysis tool IgBlast5.

The IMGT® standards generated from IMGT-ONTOLOGY are extensively reused by scientists in very diverse domains for the interpretation of immunogenetics data. The first example is the acknowledgment of the IMGT® gene names as the official nomenclature for IG and TR genes (Wain et al., 2002; Lefranc, 2007, 2008), referenced and recorded in genome sites (NCBI Gene; Maglott et al., 2011). The second example concerns the medical and clinical research which requires a high level of standardization for the results of data analysis in order to take therapeutical decisions: the European Research Initiative on chronic lymphocytic leukemia (CLL) (ERIC) includes 130 laboratories in 26 countries. ERIC has recommended the use of IMGT/V-QUEST (Brochet et al., 2008; Giudicelli et al., 2011), the IMGT® tool for the analysis of IG and TR rearranged sequences, as a reference for determining the rate of IGHV gene mutations, an important prognostic factor for CLL patients (Ghia et al., 2007; Giudicelli and Lefranc, 2008; Langerak et al., 2011). Results provided with the IMGT® standards are integrated in clinical reports (Rosenquist, 2008). The third example is the definition of monoclonal antibodies (mAb, suffix -mab) and fusion proteins for immune applications (FPIA, suffix -cept) of the World Health Organization/International Nonproprietary Name (WHO/INN) programme that are based on the IMGT-ONTOLOGY concepts (Lefranc, 2011g). INN mAb and FPIA have been entered in IMGT/mAb-DB and IMGT/2Dstructure-DB, allowing queries of sequences, 2D structures (or IMGT Collier de Perles) and, if available, 3D structures. The fourth example of great interest

for pharmaceutical companies involved in antibody engineering and humanization for therapeutical use is the characterization of the three hypervariable loops (or CDR-IMGT) of an IG or TR variable domain using the IMGT/DomainGapAlign and IMGT/Collier-de-Perles tools. The objective of antibody humanization is to graft the CDR-IMGT of an antibody, usually murine, and of a given specificity onto a human domain framework, thus preserving the original murine antibody specificity while decreasing its immunogenicity (Lefranc, 2009; Ehrenmann et al., 2010b).

IMGT-ONTOLOGY and IMGT® standards ensure the coherency of the IMGT® information system whose data permanently evolve with the most recent advances in science and methodologies. They form a unique and necessary whole for the modeling, the representation and the sharing of the immunogenetics knowledge by both humans and automated resources.

#### **ACKNOWLEDGMENTS**

We are grateful to Gérard Lefranc for helpful comments. We thank the IMGT® team, and all the previous collaborators and biocurators for the expertise and constant motivation. IMGT® is an Institutional Academic Member of the International Medical Informatics Association (IMIA). IMGT® is a registered mark of the Centre National de la Recherche Scientifique (CNRS). IMGT® is certified ISO 9001:2008 and has received the National (CNRS, INSERM, CEA, INRA) Bioinformatics Platform labels: RIO in 2001 and IBiSA in 2007. IMGT® is Bioinformatics Platform of ELIXIR, ReNaBi, GDR ACCITH,Cancéropôle GSO, GPTR Sud de France and SFR Biocampus. IMGT® was funded in part by the BIOMED1 (BIOCT930038), Biotechnology BIOTECH2 (BIO4CT960037), 5th PCRDT Quality of Life and Management of Living Resources programmes (QLG2-2000-01287) and 6th

5http://www.ncbi.nlm.nih.gov/igblast/

PCRDT Information Society Technology (ImmunoGrid, IST-2004-028069) programmes of the European Union (EU). IMGT® was granted access by GENCI to the CINES HPC resources (2010-036029). IMGT® is currently supported by the Ministère

#### **REFERENCES**

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 10 February 2012; accepted: 24 April 2012; published online: 23 May 2012.*

*Citation: Giudicelli V and Lefranc M-P (2012) IMGT-ONTOLOGY 2012. Front. Gene. 3:79. doi: 10.3389/fgene.2012.00079*

*This article was submitted to Frontiers in Bioinformatics and Computational Biology, a specialty of Frontiers in Genetics. Copyright © 2012 Giudicelli and Lefranc. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

### Three ontologies to define phenotype measurement data

#### *Mary Shimoyama1\*, Rajni Nigam1, Leslie Sanders McIntosh2, Rakesh Nagarajan2,Treva Rice2, D. C. Rao2 and Melinda R. Dwinell 1*

1 Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, USA

2 Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

#### *Edited by:*

John Hancock, Medical Research Council, UK

#### *Reviewed by:*

Philippe Rocca-Serra, Oxford e-Research Centre, UK Peter N. Robinson, Charité, Germany

#### *\*Correspondence:*

Mary Shimoyama, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. e-mail<EMAIL_ADDRESS>
**Background:**There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol. **Results:** Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository. **Conclusion:** An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies.

**Keywords: ontology, phenotype**

#### **BACKGROUND**

The quest to link characteristics of an individual or organism to genetic structures dates to the mid-1800s and the work of Gregor Mendel (Sorsby, 1965). In the past 20 years, a great deal of progress has been made in identifying, naming, and standardizing the information about genetic structures. The International Nucleotide Sequence Database Collaboration1 was created to develop standard formats for genomic data to integrate data generated at multiple laboratories using a variety of technologies resulting in public databases housed at the National Center for Biotechnology Information (NCBI; Sayers et al., 2010), the DNA Databank of Japan (Kaminuma et al., 2010), and the European Bioinformatics Institute (EBI; Goujon et al., 2010). This integration of data has led to the development of numerous data mining, presentation, and analysis tools and provides a platform for comparisons of genetic and genomic structures across species. Unfortunately, a similar development in data standards and integration has not occurred for the characteristics of an individual or organism scientists wish to link to these structures. The potential value of integrating phenotype data from multiple sources (e.g., different laboratories or studies, varying techniques to measure similar phenotypes, multiple populations, or strains of a particular organism) is enormous. The power to identify novel genes associated with human disease and the role a gene plays in disease is greatly increased with clearly defined phenotype information and the inclusion of the environmental and experimental context (Butte and Kohane, 2006). However, most phenotype data is gathered or generated without thought to integrating the results with those from other studies even within the same laboratory or program, creating barriers to integrating and comparing results reported in publications. In both animal and human physiological and disease studies, there

has been a long tradition of designing new protocols and adopting evolving best practices available at the time the study is launched. As a result, the same basic information gets collected differently across protocols. This leads to a common belief that each study is unique and cannot be compared to any other for anything more than the most general elements. Moreover, the data sets and study information are structured in such a way that, often, only those who are intimately familiar with the study understand the full depth of the data; this includes details such as the measurement methods used and the experimental conditions imposed. For most researchers, ferreting out this information from different studies requires extensive time and effort, as is generally experienced by *post hoc* collaborations among multiple studies. Even when these details are published, they are often described in widely different ways without full inclusion of details making comparisons across studies not only difficult but sometimes impossible.

Variations in experimental conditions, population, age, and study design all contribute to the difficulty in comparing phenotype data from multiple sources. For example, the comparison of blood pressure measured in different laboratories or programs can be impacted by the way in which blood pressure is measured (e.g., direct measurement via catheter in artery, telemetry, blood pressure cuff), the experimental conditions imposed as part of the study (e.g., low salt/high salt diet, exercise, oxygen levels), surgical manipulations (e.g., removal of a kidney), gender, and age. One approach to aggregating and integrating phenotype data would be to develop standard phenotyping protocols to be followed by all researchers. However, standardizing the methods used for phenotyping protocols has significant drawbacks. Many would see it as impractical since each researcher is testing fairly unique hypotheses which cannot be easily investigated by using a set protocol. Additionally, not all laboratories measure phenotypes using the same assays, nor do all investigators agree on one perfect method to measure each phenotype. Any movement

1http://www.insdc.org/

toward this type of standardization would take years before results were evident, keeping existing data resources inaccessible. A more practical approach is to develop a method using ontologies and standardized data formats to integrate phenotype measurement data sets.

A number of groups have focused on standardizing biological information through standardized vocabularies and ontologies. Ontologies are hierarchically structured vocabularies of terms and relationships that are clearly defined and designed to represent and communicate information about a particular scientific domain (**Figure 2**). The entities and concepts represented by the terms in the lower nodes are assumed to inherit the properties and qualities of those of nodes higher up the branch. The National Institutes of Health, in recognition of the utility of ontologies and the need for more ontologies to represent biological concepts, provided funding for the creation of the National Center for Biomedical Ontology (NCBO)2 in 2006 (Rubin et al., 2006). There are currently 242 ontologies cataloged at NCBO including several which focus on phenotypes.

#### **MAMMALIAN PHENOTYPE ONTOLOGY**

The Mammalian Phenotype (MP) Ontology was initially created for annotating gene alleles at the Mouse Genome Informatics (MGI) database (Smith et al., 2005). For the MP ontology, "phenotype refers to the observable morphological, physiological, and behavioral characteristics of an individual in the context of the environment" (Smith and Eppig, 2009). Because MP was designed to be used with mouse knockouts, mutations, and other types of alleles, there is an underlying assumption of a comparison to the trait exhibited by a mouse with the genetic background from which the allele has been constructed or a comparison to a normal or "wild type" trait. Thus the terms often contain words such as "abnormal," "increased," or "decreased" with the implication of "relative to" an assumed observation. The actual measured values for observed traits are not connected to these annotations. MP follows the open-source Open Biological and Biomedical Ontology (OBO) file format and is organized on the Directed Acyclic Graph (DAG) structure with the highest nodes related to physiological systems such as cardiovascular, immune system as well as behavioral, life span, and cellular phenotypes. Each physiological system node is followed by a basic division into physiological and morphological phenotype branches. MGI currently has over 41,000 genotypes annotated with MP terms for a total of more than 193,000 annotations. MP is considered a pre-coordinated term ontology since both the entity (i.e., anatomical site or physiological process) and the quality of it (i.e., abnormal, increased, decreased) are included in the term. MP is also being used for the EuroPhenome project to annotate mutant mouse phenotype data generated using standard phenotyping platforms (Morgan et al., 2010). The advantages in terms of annotation are significant since curators only have to search a single ontology and has terms that more closely mimic those seen in literature and commonly used in laboratory settings.

#### **PHENOTYPE AND TRAIT ONTOLOGY**

Another approach to phenotype ontologies has been the Phentoype and Trait Ontology (PATO) project3 (Gkoutos et al., 2004). Unlike MP which is considered a pre-coordinated term phenotype ontology, PATO uses the EQ approach (entity + quality; Gkoutos et al., 2004; Smith and Eppig, 2009). Thus PATO presents terms related to qualities and attributes that are then linked to terms from other ontologies such as anatomy ontologies to describe phenotypic characteristics. Thus, "big ears" would be represented by the term "increased size" from PATO and the word "ear" from an anatomy ontology (Mungall et al., 2010). One of the advantages of this approach is the re-use of existing ontologies such as the anatomy ontology. Representing morphological traits through the use of anatomy ontologies and the qualities described in PATO is relatively straight forward. This is one reason that resources housing data for some organisms such as drosophila and zebrafish have found this approach useful (Mungall et al., 2010); the majority of their reported traits and phenotypes are morphological in nature. However, the representation of physiological traits and specific clinical measurement types is more problematic (Smith and Eppig, 2009). First, there is not necessarily a single ontology that adequately represents the physiological trait corresponding with the quality expressed by PATO; and second, a single EQ term may not adequately express the phenotype observed. For example while the morphological trait of "big ears" is relatively easy to represent by EQ, an MP term of "abnormal cochlear outer hair cell electromotility" provides a greater challenge. The disadvantages of the PATO approach for annotation are also significant. Curators would have to browse multiple ontologies to create a term on the fly and this approach creates terms and phrases that sometimes are stilted or not commonly used in the literature or laboratory settings.

#### **HUMAN PHENOTYPE ONTOLOGY**

The Human Phenotype Ontology (HPO) was developed in part to address the shortcomings of information presented in the Online Mendelian Inheritance in Man (OMIM) database4 (Amberger et al., 2009). OMIM has traditionally been the most commonly used resource for information on genetic diseases. Unfortunately, the information housed there is in free text format, making it difficult to mine computationally because of the non-standard way in which traits and abnormalities are described. For instance, OMIM uses the synonymous descriptions"generalized amyotrophy,""generalized muscular atrophy," and "muscular atrophy, generalized" so even simple searches may not return the results a user desires. While a human reader going through the free text of multiple entries will recognize similar meanings, computers will not. The initial version of HPO was created using the information at OMIM in an effort to merge synonyms and create links and relationships among the terms and concepts. This initial structure has been expanded and refined through manual curation of information from a variety of sources and consistent development of definitions and relationships (Robinson and Mundlos, 2010). As

2http://bioontology.org/

3http://www.bioontology.org/wiki/index.php/PATO:Main\_Page

4http://www.ncbi.nlm.nih.gov/omim

with MP, the emphasis has been on phenotypes which diverge from the normal or expected and disease states and terms are pre-constructed as with the MP.

Ontologies such as MP, PATO, and HPO were originally designed for use in simple annotations to a single data (e.g., gene product or allele) or an individual and the term was expected to appear alone in the annotation with the minimal accompanying information of an evidence code indicating level of experimental evidence to support the annotation and the reference from which the annotation was made. The existing phenotype ontologies were not developed to be attached to actual measurement values but to indicate a state or characteristic observed relative to that which has been determined to be "normal" or "wild type," or relative to that exhibited by an individual with a known genotype. Information on experimental conditions and measurement assays used are vital parts of the phenotype record and the use of multiple ontologies to represent these has been advocated as a way to accomplish this (Shimoyama et al., 2005; Hancock et al., 2007). Clearly, developing separate ontologies for the elements of phenotype measurement, method of measurement, and conditions under which the measurement was made along with provisions for additional information on actual values, duration of conditions, and so on, will allow these aspects of the phenotype record to be linked. Database structures which allow re-use of information and multiple associations will facilitate data integration, data mining, and data presentation.

In this paper, we present three ontologies created to standardize phenotype measurement records for use in human studies and those using laboratory animals: Clinical Measurement Ontology (CMO), Measurement Method Ontology (MMO), Experimental Condition Ontology.

### **MATERIALS AND METHODS**

The standard elements of phenotype measurement records were identified as: (1) what was measured, (2) how it was measured, and (3) under what conditions it was measured. Ontologies were developed to standardize each of these elements (**Figure 1**) and include (1) CMO, (2) MMO, and (3) Experimental Conditions Ontology (XCO).

The ontologies are available through the NCBO Bioportal5, and at the Rat Genome Database in ftp files6. The ontologies undergo revisions and updates for both consistencies in format and to extend the breadth and depth of coverage as new measurement records are added. The ontologies were developed using the OBO format and the OBO Edit tool (Day-Richter et al., 2007) available at the NCBO, through its Foundry project (Smith et al., 2007). While developed in OBO, developments in OBO to Web Ontology Language (OWL) mapping tools should facilitate conversion to this other highly used ontology format. The major ontology

5http://bioportal.bioontology.org/ 6http://rgd.mcw.edu/pub/ontology

development tools, OBO Edit for OBO and Protege for OWL now offer widgets that facilitate conversion from one file format to the other (see text footnote 2). The OBO Relation Ontology (RO) was used to create consistency in relationship representations (Smith et al., 2007). These ontologies follow the form of DAGs in which there is a set of nodes with edges forming the linkage between nodes (Robinson and Mundlos, 2010). The nodes are the terms in the ontology with the edges representing the relationships between nodes and the overall visualization of such ontologies resembles branches. In DAGs, the edges or relationships are one way, moving from one node to another, and they do not cycle back. The general relationship pattern in many of these ontologies is a movement from the more general (higher nodes) to the more specific (lower nodes). The entities and concepts represented by the terms in the lower nodes are assumed to inherit the properties and qualities of those of nodes higher up the branch.

The development of these ontologies has included cross references with other ontologies when an exact match of the entity exists. For example, relationships were created with ChEBI in the Experimental Condition Ontology and to the Electrocardiography Ontology (ECG) exist in the CMO. These relationships were created manually and are not used to create cross products. Cross referencing to other ontologies, such as the Cell Ontology, Evidence Code Ontology, and other ontologies used for the reporting of phenotypes, will continue with both manual and semi-automated methods as the ontologies are extended.

#### **CLINICAL MEASUREMENT ONTOLOGY**

The CMO provides the standardized vocabulary necessary to indicate the type of measurement made to assess a trait. For the purposes of this project and these ontologies, trait and clinical measurement are defined as follows:

#### *Trait*

A physiological or morphological state or property found in all members of a species. Traits can be described or assessed quantitatively (numerically) or qualitatively based on the results of an appropriate form of measurement. The assessment of the trait is not equivalent to the trait itself. Traits exist even when they are not assessed or measured. Often multiple forms of measurement are used to assess a single property or state.

#### *Measurement*

The act or result of the act of assessing a morphological or physiological state or property in a single individual or group of individuals and assigning a quantitative or qualitative value. A measurement does not exist until it is performed or taken. Often a single measurement can be used to assess multiple properties or states, sometimes in conjunction with other measurements.

 Similarly, all humans have a body mass but they do not all have a body weight because it has not yet been measured.

Each term in the CMO describes a distinct type of measurement used to assess one or more traits. The terms are arranged in a hierarchical structure of classes so that lower classes are subclasses of higher classes in the branch (**Figure 2**).

This represents an "is\_a" type relationship so that a lower term "is\_a" subclass of a higher term.  The measurements in the ontology are primarily organized on the highest level according to the body system in which the measurement is made. Trait areas were targeted for ontology development based on the availability and extent of data in large scale rat phenotyping projects, published rat literature with phenotype measurements and targeted human epidemiological studies. Ontology development began with the identification of clinical measurements used to assess targeted traits, with terms and definitions being created and relationships among terms being set (**Figure 3**).

Existing ontologies at NCBO were reviewed for associated terms and definitions. Because the clinical measurements in the targeted sources may be limited, additional literature including medical and physiological textbooks, laboratory manuals, and published research literature were reviewed to ensure completeness in the ontology. For example, to assess kidney mass the data source may only use right kidney weight. Further review of a variety of sources reveals that other typical measurements would include left kidney weight, weight of both kidneys, kidney weight expressed as a percentage of body weight which are also often used to assess the trait of kidney mass so these were also added to the ontology. For every clinical measurement term created, associated measurement method terms and experimental condition terms were created based on data in the originating sources as well as the review of additional literature and existing ontologies. There are currently 523 terms in the CMO for measurement types ranging from morphological to physiological for blood, cardiovascular, respiratory, renal, and other systems as well as for growth, reproduction, consumption, tumors, and tissue composition.

#### **MEASUREMENT METHOD ONTOLOGY**

A critical element in the description of a phenotype is the measurement method used. Several types of methods are commonly used to measure such things as blood pressure resulting in differing clinical measurement values so the inclusion of method as part of the measurement reading is necessary for the integration of data from multiple studies. The MMO is designed to provide this information. As described above, this ontology was developed in parallel with the CMO as trait areas are targeted. The MMO is organized by the underlying principle or mechanism of the method (**Figure 4**) with two major branches, "*ex vivo* method" and "*in vivo* method." Methods were identified from protocol descriptions and data labels from the targeted data sources. As with the CMO, for completeness in the ontology, additional sources of method information such as vendors' catalogs, laboratory manuals, and published literature were reviewed for associated methods. Thus, if one of the protocols for one of the originating data sources indicated that a balloon tipped catheter was used to measure blood pressure, a quick review of a variety of publications revealed that the basic category is vascular indwelling catheter with a variety of types including fluid filled catheter, intravascular electromagnetic flow sensor, and transducer tipped catheter. There are currently 195 terms in the MMO.

**FIGURE 2 | The Clinical Measurement Ontology is presented in a hierarchical structure with classes lower down a branch being subclasses of those above with an "is\_a" relationship.**

**FIGURE 4 | The Measurement Method Ontology structure is based on two major branches, "***ex vivo***" and "***in vivo***" and the underlying mechanism or technique used in the method.**

#### **EXPERIMENTAL CONDITION ONTOLOGY**

While many phenotype measurements are made under baseline conditions, changes to diet, atmosphere, activity level, and other conditions are common aspects of phenotype experiments. Often this information is added as part of the phenotype label in the individual laboratory's database or only included as part of a lengthy text protocol. Creation of standardized terminology and format for presenting this information with phenotype measurements is crucial to the integration of phenotype data from multiple datasets. An XCO was created to provide standardization and structure for this important information (**Figure 5**).

In addition, to the "is\_a" relationship, in certain areas a "part\_of" relationship is utilized so that parts of a whole can be described as in"air oxygen content"is"part\_of""atmosphere composition." The ontology was designed so that conditions related to existing ontologies such as those involving chemicals or drugs represented in ChEBI (Degtyarenko et al., 2009), follow the structure and terminology of these ontologies and provide appropriate linkages through identifiers (**Figure 5**). Initial emphasis was on the conditions used in the targeted data sets and expanded to those conditions most commonly used in experiments involving the targeted trait domains. Structural provisions in the database structures of projects using the ontology provide ordinality information to indicate whether multiple conditions were simultaneous or sequential. Use of this ontology in annotating phenotype data allows users to retrieve multiple, disparate phenotype information in which similar experimental conditions were imposed. There are currently 110 terms in the XCO.

#### **DATA INTEGRATION**

The three ontologies have been used to integrate multiple data sets for two major projects, one involving human data and the other involving rat data. The Cardiovascular Ontologies and Vocabularies in Epidemiological Research project was designed to integrate demographic and phenotype measurement data from three family blood pressure studies, Hypertension Genetic Epidemiology Network (HyperGEN; Williams et al., 2000); Genetic Epidemiology Network of Salt Sensitivity (GenSalt; Gu et al., 2007);

and HEalth, RIsk factors exercise Training And GEnetics (HER-ITAGE; Bouchard et al., 1995). While all three studies focused on cardiovascular disease and associated risk factors, they were disparate in the types of interventions used, variety of measurements taken and the methods used to make the measurements. Measurements related to blood pressure, blood chemistry and lipid levels, body weight and body fat were included as well as interventions ranging from sodium controlled diets to exercise. Invasive, non-invasive and imaging techniques were also used. The HyperGEN study was designed to characterize the genes influencing hypertension by recruiting hypertensive sibships (i.e., each participant with two or more hypertensive sibs) from across multiple field centers and ethnic groups. The GenSalt study is an intervention study of the genetic and environmental factors related to dietary sodium and potassium effects on blood pressure in rural Chinese families. The HERITAGE study is an intervention study designed to assess genetic and environmental factors underlying the effects of endurance exercise training on several cardiorespiratory and cardiovascular disease risk factors. The CMO, MMO, and Experimental Condition Ontology were used to map data elements from each of the studies to a common format for integration into a single resource7. To date, 16 phenotype classes with records for 8,778 subjects have been integrated for the three studies and made available at the

website. Additionally, all variables across the studies are being mapped and modeled with their associated ontology terms to facilitate querying and access to raw data fields of interest; to date 11 classes have been created representing over 100 phenotype measurements.

The rat PhenoMiner project8 (**Figure 6**) also has used the three ontologies to define data formats and standards for integrating rat phenotype measurement data from a variety of sources including two large scale phenotyping projects and published literature. PhysGen Program for Genomic Applications9 (Kwitek et al., 2006), one of the large scale phenotyping projects was designed to conduct high throughput phenotype screening for a targeted set of inbred strains, as well as consomic and mutant strains. The screens involved hundreds of different types of phenotype measurements for heart, lung, renal, vascular, and blood function under baseline conditions as well as varying diet, atmosphere, and activity conditions. Data was organized, stored, and presented by protocol so even though some similar measurements such as weight or blood pressure were measured in multiple protocols, the data was not integrated across protocols. The National BioResource Project for the Rat in Japan (Serikawa et al., 2009) was the second large scale rat phenotyping project. Phenotype screens for body weight, activity, behavior, blood pressure, blood chemistry, urine analysis,

7http://cover.wustl.edu/Cover/

8http://rgd.mcw.edu/phenotypes/ 9http://pga.mcw.edu/

and organ weights have been conducted under baseline conditions for inbred and mutant strains.

Because the rat is an ideal model organism for pharmacology, biochemistry, and physiology research, the published literature is a rich resource of data on phenotype measurements for particular strains. Papers reporting cardiovascular, respiratory, renal, morphological, and blood chemistry measurement data as well as those with measurements related to cancer were targeted for the initial phase of the PhenoMiner resource. Over 13,000 measurement records from these three sources have been mapped to the three ontologies and integrated into PhenoMiner (**Figure 7**).

#### **DISCUSSION AND CONCLUSION**

The three ontologies created have proven to be excellent tools for standardizing phenotype measurement data for projects involving a wide variety of data types and data sources. Targeting the three basic elements of: (1) what was measured; (2) how it was measured; and (3) under what conditions it was measured, facilitated standardization while allowing for flexibility in providing associated information such as units of measurement or duration of condition to be formatted in ways particular to the integrating resource. These ontologies allowed phenotype measurement data from disparate studies to be integrated without compromising study-specific aspects related to methodology. Multiple datasets of human epidemiological data and rat phenotype data were successfully integrated into resources designed to meet the needs of diverse research communities even though the underlying technological framework for the databases and associated tools differed. While integrating varied phenotype datasets was the primary motivation for the development of these ontologies, they can be deployed in a variety of other projects as well. Because of their availability at NCBO, they can be utilized with the NCBO Annotator, a Web service that annotates journal abstracts10 which facilitates curation efforts and queries for appropriate literature for specific projects. Because of their focus on experimental data, the use of these ontologies in text mining tools would also help investigators identify and prioritize literature.

Creating structures to integrate phenotype measurement data from multiple sources is an important task as investigators draw on the strength of the genomic and sequence variation resources to identify underlying genotype factors related to phenotypes and diseases. In order to make these connections, researchers need to easily access and analyze phenotype measurement data related to individuals and various model strains, and information on experimental conditions and methodologies that may affect the measurement values. Employing multiple ontologies to standardize data formats facilitates the integration of these vital datasets and provides the structure on which innovative data mining, analysis, and presentation tools can be built. These types of resources can provide researchers with a more accurate

10http://bioportal.bioontology.org/annotator#

**FIGURE 7 | Example of phenotype measurement data from multiple studies mapped to the three ontologies for clinical measurement, measurement method, and experimental condition.**

picture of phenotype variations among populations and as well as the impact of measurement methods may have on measurement results. The influence of experimental and environmental conditions on phenotypes and disease will also be easier to elucidate when researchers have access to large numbers of measurements from a wide variety of studies. This is an important step in helping investigators link genotypes to phenotypes. Finally, the use of multiple ontologies to standardize data elements into single quantifiable records can be used in many paradigms to integrate datasets. Convergence among phenotyping efforts can be fostered using this methodology through the use of existing ontologies, such as MP, PATO, and HPO, in conjunction with ontologies that further refine the phenotype data. Engaging other communities will provide the platform for data mining across species or across phenotyping programs using a variety of phenotyping protocols.

#### **AUTHORS' CONTRIBUTIONS**

Mary Shimoyama created the ontologies, organized the data mapping, and integration at the Medical College of Wisconsin

#### **REFERENCES**

Amberger, J., Bocchini, C. A., Scott, A. F., and Hamosh, A. (2009). McKusick's online Mendelian inheritance in man (OMIM). *Nucleic Acids Res.* 37, D793–D796.

Bouchard, C., Leon, A. S., Rao, D. C., Skinner, J. S., Wilmore, J. H., and

Gagnon, J. (1995). The HERITAGE family study. Aims, design, and measurement protocol. *Med. Sci. Sports Exerc.* 27, 721–729.

Butte, A. J., and Kohane, I. S. (2006). Creation and implications of a phenome-genome network. *Nat. Biotechnol.* 24, 55–62.

and participated in data mapping for the COVER project. Rajni Nigam assisted in ontology term creation and participated in data mapping. Leslie Sanders McIntosh organized data mapping and informatics component creation for the COVER project. Rakesh Nagarajan participated in the informatics infrastructure design for COVER project. Treva Rice coordinated data mapping and integration for the COVER project. D. C. Rao participated in design and coordinated data mapping and integration for the COVER project. Melinda R. Dwinell participated in the design of the PhenoMiner tool and data integration at the Medical College of Wisconsin. All authors have read and approved the manuscript.

#### **ACKNOWLEDGMENTS**

The authors would like to acknowledge the contributions of the RGD curation team at the Medical College of Wisconsin and the staff and informatics team at Washington University. This project is funded in part by R01HL094271, R01HL094286, and R01HL064541.

and cheminformatics resource. *Curr. Protoc. Bioinformatics* Chapter 14, Unit 14.19.

Gkoutos, G. V., Green, E. C., Mallon, A. M.,Hancock, J. M., and Davidson, D. (2004). Building mouse phenotype ontologies. *Pac. Symp. Biocomput.* 178–189.

Okubo, K., Takagi, T., and Nakamura, Y. (2010). DDBJ launches a new archive database with analytical tools for next-generation sequence data.*Nucleic Acids Res.* 38,D33–D38.

Dicuccio,M., Federhen,S., Feolo,M., Geer, L. Y., Helmberg, W., Kapustin, Y., Landsman, D., Lipman, D. J., Lu, Z.,Madden, T. L.,Madej, T.,Maglott, D. R., Marchler-Bauer, A., Miller, V., Mizrachi, I., Ostell, J., Panchenko, A., Pruitt, K. D., Schuler, G. D., Sequeira, E., Sherry, S. T., Shumway, M., Sirotkin, K., Slotta, D., Souvorov, A., Starchenko, G., Tatusova, T. A., Wagner, L., Wang, Y., John Wilbur, W., Yaschenko, E., and Ye, J. (2010). Database resources of the National Center for Biotechnology Information. *Nucleic Acids Res.* 38, D5–D16.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 14 February 2012; paper pending published: 05 March 2012; accepted: 30 April 2012; published online: 28 May 2012.*

*Citation: Shimoyama M, Nigam R, McIntosh LS, Nagarajan R, Rice T, Rao DC and Dwinell MR (2012) Three ontologies to define phenotype measurement data. Front. Gene. 3:87. doi: 10.3389/fgene.2012.00087*

*This article was submitted to Frontiers in Bioinformatics and Computational Biology, a specialty of Frontiers in Genetics.*

*Copyright © 2012 Shimoyama, Nigam, McIntosh, Nagarajan, Rice, Rao and Dwinell. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

### Development and use of ontologies inside the neuroscience information framework: a practical approach

#### **Fahim T. Imam\*, Stephen D. Larson, Anita Bandrowski, Jeffery S. Grethe, Amarnath Gupta and Maryann E. Martone\***

Neuroscience Information Framework, Center for Research in Biological Systems, University of California San Diego, La Jolla, CA, USA

#### **Edited by:**

John Hancock, Medical Research Council, UK

#### **Reviewed by:**

Douglas M. Bowden, University of Washington School of Medicine, USA Qiangfeng Cliff Zhang, Columbia University, USA

#### **\*Correspondence:**

Fahim T. Imam and Maryann E. Martone, Neuroscience Information Framework, Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093-0446, USA. e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF's production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies.

**Keywords: ontologies, ontology reuse, neuroscience ontology, semantic search**

#### **INTRODUCTION**

The Neuroscience Information Framework Project (NIF)1 facilitates the utilization of the growing number of neurosciencerelevant data available through the web. NIF, supported by the National Institutes of Health Blueprint, was initiated in recognition of the current difficulties of locating and searching across the diverse array of web-based resources and databases (Gardner et al., 2008). The NIF was also charged with developing tools and strategies for creating resources that can be integrated across neuroscience domains. The end product is a semantic search engine and a knowledge discovery portal that consists of a framework for describing neuroscience resources and provides simultaneous access to multiple types of information organized by relevant categories. Through its extensive resource catalog and data federation, NIF currently represents the largest source of neuroscience information available on the web.

The semantic framework through which these diverse resources are accessed is provided by the NIF Standardized Ontologies (NIF-STD; Bug et al., 2008). NIFSTD represents an extensive collection of terms and concepts from the major domains of neuroscience. The overall ontology has been assembled in a form that promotes reuse of multiple existing biomedical ontologies and standard vocabulary sources, while allowing for extension and modification over the course of its evolution. This paper presents the development principles of NIFSTD along with its application within the NIF system.

#### **NIFSTD DESIGN PRINCIPLES**

As originally proposed inBug et al. (2008),NIFSTD was envisioned as an extensive set of ontologies, specific to the domain of neuroscience. NIFSTD started its journey with a carefully designed set of principles which enabled its ontologies to be maximally reusable, extendable, and practically applicable within information systems. Over the course of its evolution, NIFSTD augmented its principles in order to conform to the current, up-to-date trends, and practices recommended by the semantic web communities as well as by the community of standard biomedical ontologies. NIFSTD closelyfollows the OBO Foundry (Smith et al.,2007) best practices; however, the constraints of the NIF project required that we take a practical approach, designed to easily extend the NIFSTD ontologies, while at the same time mitigating against any disruptions to the production NIF system. Our approach is outlined following the discussion of the NeuroLex Semantic Wiki framework in Section "The NeuroLex Semantic Wiki Framework."

#### **NIFSTD MODULAR STRUCTURE**