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Modifier Ontologies for frequency, certainty, degree, and coverage phenotype modifier
research-article
Author(s):
Lorena Endara
1
,
,
Anne E Thessen
2 ,
Heather A Cole
3 ,
Ramona Walls
4 ,
Georgios Gkoutos
5
,
6 ,
Yujie Cao
7 ,
Steven S. Chong
8
,
9 ,
Hong Cui
9
,
Publication date (Electronic):
28 November 2018
Journal:
Biodiversity Data Journal
Publisher:
Pensoft Publishers
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Abstract
Background: When phenotypic characters are described in the literature, they may be constrained or clarified with additional information such as the location or degree of expression, these terms are called “modifiers”. With effort underway to convert narrative character descriptions to computable data, ontologies for such modifiers are needed. Such ontologies can also be used to guide term usage in future publications. Spatial and method modifiers are the subjects of ontologies that already have been developed or are under development. In this work, frequency (e.g., rarely, usually), certainty (e.g., probably, definitely), degree (e.g., slightly, extremely), and coverage modifiers (e.g., sparsely, entirely) are collected, reviewed, and used to create two modifier ontologies with different design considerations. The basic goal is to express the sequential relationships within a type of modifiers, for example, usually is more frequent than rarely, in order to allow data annotated with ontology terms to be classified accordingly.
Method: Two designs are proposed for the ontology, both using the list pattern: a closed ordered list (i.e., five-bin design) and an open ordered list design. The five-bin design puts the modifier terms into a set of 5 fixed bins with interval object properties, for example, one_level_more/less_frequently_than, where new terms can only be added as synonyms to existing classes. The open list approach starts with 5 bins, but supports the extensibility of the list via ordinal properties, for example, more/less_frequently_than, allowing new terms to be inserted as a new class anywhere in the list. The consequences of the different design decisions are discussed in the paper. CharaParser was used to extract modifiers from plant, ant, and other taxonomic descriptions. After a manual screening, 130 modifier words were selected as the candidate terms for the modifier ontologies. Four curators/experts (three biologists and one information scientist specialized in biosemantics) reviewed and categorized the terms into 20 bins using the Ontology Term Organizer (OTO) ( http://biosemantics.arizona.edu/OTO). Inter-curator variations were reviewed and expressed in the final ontologies.
Results: Frequency, certainty, degree, and coverage terms with complete agreement among all curators were used as class labels or exact synonyms. Terms with different interpretations were either excluded or included using “broader synonym” or “not recommended” annotation properties. These annotations explicitly allow for the user to be aware of the semantic ambiguity associated with the terms and whether they should be used with caution or avoided. Expert categorization results showed that 16 out of 20 bins contained terms with full agreements, suggesting differentiating the modifiers into 5 levels/bins balances the need to differentiate modifiers and the need for the ontology to reflect user consensus. Two ontologies, developed using the Protege ontology editor, are made available as OWL files and can be downloaded from https://github.com/biosemantics/ontologies.
Contribution: We built the first two modifier ontologies following a consensus-based approach with terms commonly used in taxonomic literature. The five-bin ontology has been used in the Explorer of Taxon Concepts web toolkit to compute the similarity between characters extracted from literature to facilitate taxon concepts alignments. The two ontologies will also be used in an ontology-informed authoring tool for taxonomists to facilitate consistency in modifier term usage.
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Most cited references23
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A Gross Anatomy Ontology for Hymenoptera
Matthew J. Yoder, István Mikó, Katja C. Seltmann … (2010)
Hymenoptera is an extraordinarily diverse lineage, both in terms of species numbers and morphotypes, that includes sawflies, bees, wasps, and ants. These organisms serve critical roles as herbivores, predators, parasitoids, and pollinators, with several species functioning as models for agricultural, behavioral, and genomic research. The collective anatomical knowledge of these insects, however, has been described or referred to by labels derived from numerous, partially overlapping lexicons. The resulting corpus of information—millions of statements about hymenopteran phenotypes—remains inaccessible due to language discrepancies. The Hymenoptera Anatomy Ontology (HAO) was developed to surmount this challenge and to aid future communication related to hymenopteran anatomy. The HAO was built using newly developed interfaces within mx, a Web-based, open source software package, that enables collaborators to simultaneously contribute to an ontology. Over twenty people contributed to the development of this ontology by adding terms, genus differentia, references, images, relationships, and annotations. The database interface returns an Open Biomedical Ontology (OBO) formatted version of the ontology and includes mechanisms for extracting candidate data and for publishing a searchable ontology to the Web. The application tools are subject-agnostic and may be used by others initiating and developing ontologies. The present core HAO data constitute 2,111 concepts, 6,977 terms (labels for concepts), 3,152 relations, 4,361 sensus (links between terms, concepts, and references) and over 6,000 text and graphical annotations. The HAO is rooted with the Common Anatomy Reference Ontology (CARO), in order to facilitate interoperability with and future alignment to other anatomy ontologies, and is available through the OBO Foundry ontology repository and BioPortal. The HAO provides a foundation through which connections between genomic, evolutionary developmental biology, phylogenetic, taxonomic, and morphological research can be actualized. Inherent mechanisms for feedback and content delivery demonstrate the effectiveness of remote, collaborative ontology development and facilitate future refinement of the HAO.
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Using ontologies to describe mouse phenotypes
Georgios Gkoutos, Eain Green, Ann-Marie Mallon … (2004)
Background Mutant mice are the premier genetic models for human diseases. An increasing number of laboratories and companies worldwide are now carrying out detailed analyses of mouse phenotypes that have been generated from large-scale mutagenesis of the mouse genome. Description of mouse phenotypes has not traditionally adhered to predefined rules or been recorded in databases. However, the sheer volume of data from high-throughput screens (such as N-ethyl-N-nitrosourea (ENU) mutagenesis [1]) is now driving the need to manage information about mutants in a paperless environment and to build databases that will allow this data to be shared between laboratories and used to formulate hypotheses about gene function. The key to satisfying this need is the ability to describe different phenotypes in a consistent and structured way. There is a need for consistency in the way different communities of biologists attempt to present this kind of data since consistent representation of phenotypes across different domains (such as pathology and anatomy) and species is crucial for the semantic interpretation and the efficient use of this complex information in different kinds of study, such as comparison of gene functions between species. Ontologies have been an important tool for structuring biological information since the time of Linnaeus. With the advent of the Gene Ontology (GO) in 2000 [2] these techniques for strictly specifying the semantic relationships between terms have become a standard to support knowledge representation in the field of genomics. Hierarchical ontologies hold information about the structure of a particular domain of knowledge at varying degrees of detail (granularity), thus permitting us to integrate concepts and descriptions at different levels of resolution. This approach is forming the basis of new methods for mining biological data [3,4]. In this article, we describe developments in describing mouse phenotypes using ontologies. Ontologies and knowledge bases The term ontology is derived from the Greek and is used in philosophy to mean 'a description of what exists'. There are many definitions of the word, however, and for the purpose of this article, an ontology is 'a specification of entities and their relationships' [5]. The key word 'specification' implies a formal organization. Thus, an ontology is a formalism to describe entities and the relationships between them. Ontologies for computing applications are schemas for metadata [6]. They provide a controlled organization of terms and their relationships that has explicitly defined and machine-processable semantics [7]. The controlled semantic portrayal of entities and their relationships allows the description of a domain of knowledge. For our purposes ontologies mainly attempt to replace free-text descriptions of phenotypes with equivalent computable descriptions that can be used to draw inferences about these data. An ontology together with a set of individual instances of the kinds of entities it specifies constitutes a knowledge base [8]. It may be difficult to distinguish between the knowledge contained in an ontology and the knowledge contained in a knowledge base [9]. In phenotype ontologies the distinction between the ontology and the knowledge base must be clear. The ontology should capture the general conceptual structures necessary to describe the domain, whereas the knowledge base should provide the individual instances that are described using the ontology. So, in the ontology one can first define the entity (class) of 'pain perception' and further, assign to this entity the attribute 'relative sensitivity' and specify for this attribute a range of allowed values using concepts such as 'sensitive' or 'insensitive', and so on, thereby allowing us to describe pain-perception phenotypes. The knowledge base, however, holds data about particular instances [10], for example a particular mouse with a particular genotype, under defined handling conditions and of a certain age, that has a particular level of sensitivity to pain according to a particular assay. In other words, the ontology constitutes a general theory (how to describe phenotypes), whereas the knowledge base describes particular circumstances, in our case particular instances of phenotype. Why use ontologies? An important question here is why do we need to use ontologies; why not simply use a series of unconnected, standard terms such as provided by a controlled vocabulary? The advantages of using ontologies have been argued extensively, but the main reason is that ontologies are attempting to capture the precise meaning of terms. Furthermore, ontologies can be used for reasoning and inference (for example, consistency checking or drawing conclusions from the knowledge). The most important factor from our perspective is the need to combine information from different phenotypes or from different protocols (assays). For example, if a mutant mouse has six digits in each forelimb we will wish to use this information in a variety of ways (for example, to group mice with limb pattern defects, or with affected forelimbs, or with abnormal numbers of digits in any limb). For this, we need not just a controlled vocabulary of terms, but also information about how these terms relate to one another (for example, that forelimb is an instance of 'limb', that the normal number of digits in the forelimb is five, that the number of digits is an instance of 'pattern', and so on). Current approaches to the description of mouse phenotypes Traditionally, the main source of information for most scientists is the peer-reviewed journal literature. Electronic versions of published information have opened the road to accessing and retrieving information in a much easier and more cost-effective manner. The growth and wider availability of the world-wide web has led to a significant growth in the amount of readily available electronically stored information [11]. With this surge of readily available information the location and retrieval of relevant information has become a major (commercial) activity [12]. One of the most important issues in information retrieval is constructing effective indexing methods that are required for the sophisticated querying of the stored data. Free-text searching forms the basis of information retrieval but is extremely limited because of the inherent lack of accuracy and specificity. Complex free-text descriptions, such as are used for phenotypes, are almost impossible to index and retrieve in a useful way directly from the biomedical literature. The potential power of complex searches against information from multiple experiments requires the annotation of free text into structured representations that can be understood and where the power of computational algorithms can maximize the potential of the information to be compared and contrasted. The most comprehensive attempt to annotate mammalian phenotypic data so far, the Mammalian Phenotype Ontology (MP) [13], is currently under development by the Jackson Laboratory [14,15]. The current structure of the ontology is generated using DAG-Edit [16], the current GO standard, and allows a hierarchical display of terms and their definitions. These terms include a combination of entities and values, for example, id MP:0001509 corresponds to 'abnormal body position', which at a high level provides a sufficient description of phenotypic data. This approach allows high-level access to the knowledge held in the ontology, but also has certain limitations similar to the GO paradigm. If one attempts to create too much specificity within an ontology of this type it can expand to unmanageable proportions and parentage relationships can be overlooked as their number grows. For example, merely creating new terms by prepending the two qualifiers, 'increased' and 'decreased', everywhere that is applicable, will massively increase the size of the ontology. To allow a systematic approach to the model, combinations would also have to be instantiated that might never be used. Because there is a practical limit to the number of values that can be managed, such an approach is limited. Inevitably, decisions have to be made as to which individual combination describes a particular phenotypic entity best. We note here though that the development of MP is being developed pragmatically, with instances being added as needed to annotate mouse phenotypes, following the paradigm used by GO developers. MP is a cross-product ontology that includes mouse anatomy ontology, GO and other controlled terms as part of the construction of MP terms. Although the cross-reference IDs are not visible, they are part of the design of MP. Some of the work described here reflects insights gained during extensive discussions about the representation of phenotypes at the Phenotype Consortium meeting held in Bar Harbor, ME in September 2003. The developers of the MP ontology are part of this consortium and have intentionally created their ontology in such a way that it can be easily extended to form instances of the compositional approach discussed in the next section. With the objective of capturing information about phenotypes in any organism, Ashburner proposed the Phenotype And Trait Ontology (PATO) [17] in 2002. PATO is a schema according to which, "phenotypic data can be represented as qualifications of descriptive nouns or nounal phrases" (M. Ashburner, unpublished work). Each noun represents an observable characteristic and for each noun there will be a set of attributes, for each of which is defined a set of appropriate values. In addition to these three semantic classes (namely observable entities together with the associated attributes and values), the concepts that are needed to describe phenotypes include the assays by means of which the phenotypes were determined and the environmental and genetic conditions (Microarray Gene Expression Data Society [18]) under which these assays were performed. Taken together, the semantic concepts and relationships defined for PATO, assays, genetic and environmental conditions, will form the basis for the systematic description of phenotypes. Results A proposal for describing mouse phenotypes The description of mutant phenotypes must provide a practical way to capture the biologically relevant information about the phenotype in machine-readable form [19]. It should allow us to compare, combine and analyze different phenotypes. For this, the ontology must first be consistent, and second be able to generate statements that have a logically well-formed structure in order to support reasoning from descriptions of different phenotypes. To provide these functionalities we propose a compositional method of describing phenotypes [19]. By this we mean that the description of the phenotype combines terms from different standard ontologies, each of which supports a particular domain of knowledge. A list of ontologies that should be included in such a phenotype ontology is given in Table 1. These ontologies are combined in a specified formula or schema that provides the logical structure of the whole. The schema itself can be considered as a meta-ontology that describes how other ontologies relate to one another. Figure 1 illustrates such a schema. According to the schema in Figure 1, the whole organism has certain attributes, such as genotype, identity number, and exists under certain handling conditions (Table 2). The organism also has a set of core components including its anatomy, development, physiology and behavior. Each of these core components is represented by a separate ontology and each has a set of attributes, again represented by an ontology. For example, the organism may have an anatomical component 'left eye' which is a term from the anatomy ontology. The left eye, in turn, may have attributes of 'color', 'size', and so on, taken from the attributes ontology. This combination of core entity and attribute constitutes a phenotypic character - something that can be measured. Phenotypic characters, in turn, link to 'assays', which return a variety of 'values', again represented by an ontology, which may be applied to the phenotypic character in question. When this schema is used to describe actual phenotypes, instances of single phenotypic characters are linked together to provide a full phenotypic description of an individual organism. Each character can be represented by a line in a table where the table represents the full phenotype. Figure 2 presents this schematically. According to the schema in Figure 1, five classes of ontology (in circles), namely organism, entity, attribute, assay and value, are required to express a phenotypic instance. Organism This class holds the information (organism attributes) of an organism in which the phenotypic characters are observed (see Table 2). Entity Entities will be formed by importing ontologies discussed in Table 1: behavior, anatomy, and so on. Each entity may be associated with a set of attributes, for example, color and size, that may also be shared with other entities. Attribute Attributes will be provided by PATO [17]. PATO should hold general attributes that can be applied through different phenotypic ontologies. This has the advantage of economy and also enables cross-referencing between domains. New attributes should be assigned to classes only when they cannot be modeled with existing options. Assay Assays will have a hierarchical structure and will define a range of values that correspond to a particular combination of entity and attribute (that is, phenotypic character). They hold multiple relations to values, qualifiers and free text as well as their own metadata. The slot for free text is included to capture knowledge that cannot be expressed through the ontology as yet. Values Splitting PATO into two different ontologies, PATO attributes (above) and PATO values, allows the PATO ontology to be incorporated into the schema [19]. Values can thus be either specific values provided by the assay or common values, provided by PATO. A possible relationship between these sets of values would be 'interpretation_of'. Although values provided directly by the assay are usually the objective recordings of a test for a specific phenotypic character, there can be an interpretation of these recordings in terms of a higher level phenotypic character. For example, in an assay of memory in the mouse that uses a water test, the values returned by the test may be that a mouse completed the task in a certain time and manner, but these results may be interpreted to indicate a value corresponding to the phenotypic character comprised by the entity 'memory' that was assayed for the attribute of 'short-term recall' and returned the interpretative value 'loss of memory'. By introducing the 'interpretation_of' relationship, we could make this distinction in a machine-understandable manner and allow the possibility, if required, of expressing the original objective values of the test, thus avoiding information loss. This aspect of the schema remains under study. A central idea in this schema is that of the 'phenotypic character', which we can define as any feature of the organism that is observed or 'assayed'. An example for the mouse is tail length. A phenotypic character is a compound composed of an entity, in this case an anatomical entity 'tail', and an attribute of tail, here 'length'. Similarly the physiological entity 'hearing' (GO:0007605) has the attributes 'sensitivity', 'range', and so on. Thus, 'hearing range' and 'hearing sensitivity' are distinct phenotypic characters. The ideal phenotypic character is one that can be measured independently of others. In practice, however, phenotypic characters are rarely independent. Furthermore, the observations from any particular assay will most probably depend on several different phenotypic characters. For example, the results returned by the click-box test for hearing sensitivity in the mouse actually depend, not only on hearing, but also on the mouse's ability to make a detectable locomotor response (the Preyer reflex [20]). These multiple dependencies are captured in the schema, enabling the ontology to support the appropriate possible groupings of phenotypes. This will allow us, for example, to group all mutants that have (by direct assay), or may have (for example, those failing the click-box test), an effect on the locomotor system. Conversely, different assays may provide information about a single phenotypic character. For example, an acoustic brain-stem response (ABR, a sound-evoked potential within the acoustic nerve) [21] can be measured to assay basic hearing ability as well as to give a threshold-response curve for differing frequencies. Linking assays with characters in this way will support machine reasoning, enabling us, for example, to make the hypothesis that a particular mouse has a locomotor rather than hearing defect. Indeed, the need to capture this network of relationships between assays and phenotype is a strong indication of the need for an ontology rather than merely a controlled vocabulary of unrelated terms. The expressivity of representation languages such as DAML+OIL [22], OWL [23] and OBO [17] could also dynamically account for the possibility of a cross product or dependence required for representing a phenotype. For example, if a cross product between ontologies does not exist (that is, one of the required terms is not to be found in an ontology), one can assign an 'anonymous class' that is dynamically defined as being both a class in one case and an instance in another. As an example, one might want to refer to the term cocaine dependence, but that cross product may not exist. An 'anonymous class' can be dynamically defined as being both 'cocaine' (coming from a chemical ontology) and 'dependence' (coming from the behavior ontology) to generate this cross product. Finally, we note here that it should be possible to link current high-level structures (such as the current MP ontology), which are necessary in many cases for annotation purposes, to the more expressive form we propose here, so that it can also be explored computationally. Example In this section we describe an example of the application of the compositional schema. We chose a phenotype example at random from the MP database: 'nest building' [MP:0001447]. Several descriptions of nest-building patterns can be found in the corresponding reference [24]. For example, the authors comment: "Note the fluffy well formed nests built in the +/+ cages and the huddling of mice in these nests, in contrast to the poorly formed nests in -/- cages with random sleeping patterns." and later: "In addition, +/+ mice built nests from nestlet material that averaged 50 mm in depth, while -/- mice built significantly shallower nests (Figure 4D), with depths that averaged less than 20 mm [t(10) = 3.754, p < 0.004]." The authors also describe the assays used to record these observations: "Nesting Patterns: six cages of wild-type and six cages of mutant mice (N = 4 mice per cage) were used to evaluate nesting patterns. A 5 × 5 cm piece of cotton nesting material (Ancare, Bellmore, NY) was placed in each cage. After 45 min, photographs were taken of each nest and the nest depth was measured. Nest height data were analyzed using the Student's t test." For some users/applications the compound term 'abnormal nest building' might be a sufficient description of this particular phenotypic instance, but this would result in information loss. A human would have to retrieve and read the reference to extract further information. Our schema allows the expression of this information in a machine and human readable manner. In Table 3 we provide the relevant part of our ontology modeled according to the schema. One can easily express these phenotypic instances. In order to describe fluffy, well formed nests or poorly formed nests one would use the following combination: Nest building {has_attribute} attribute:quality {characterized_by} defined_quality_assay (described in Nesting Patterns [24]) {returns_value} well-formed Nest building {has_attribute} attribute:quality {characterized_by} defined_quality_assay (described in Nesting Patterns [24]) {returns_value} poorly-formed Nest building {has_attribute} attribute:quality {characterized_by} defined_quality_assay (described in Nesting Patterns [24]) {returns_value} fluffy We note here that had the value 'fluffy' not been included in the standard values for a quality assay, it could be captured in the free-text field provided by the schema. To express a nest of 50 mm depth or significantly shallower: Nest building {has_attribute} attribute:absolute_depth {characterized_by} undefined_ absolute_depth_assay {returns_value} 50 mm Nest building {has_attribute} attribute: relative_depth {characterized_by} undefined_ relative_depth_assay {returns_value} shallow {has_qualifier} significant With this information one could go back to a higher level and still be able to express a more general characterization of this phenotype as 'abnormal nest building' but obviously the opposite is not possible. An important unresolved issue concerning the use of ontologies to describe phenotypes arises from the fact that all the ontological structures developed so far are designed to describe individual mice. Mutagenesis experiments usually characterize a number of mutant mice to take into account variable penetrance of the mutation and other stochastic effects. A strategy will therefore need to be developed to describe the generalized phenotypic properties of a cohort of mice. This may involve the use of more sophisticated relations such as {usually characterized by} or even quantitative relations such as {80% characterized by}. Discussion Importance of the assay The assay plays a central role in our schema (Figure 1). Assays are the means of making observations and as they determine what can be observed they are a necessary complement to the attribute ontology. Generally, they are recorded as protocols or even as standard operating procedures (SOPs). However, even a visual observation is a form of assay and this needs to be reported when one expresses a phenotypic instance, for example: eye {has_attribute} attribute:color {characterized_by} visual inspection {returned_value} pink On a practical level, assays can add specificity and functionality to the relationship between entities, their attributes and the corresponding values. Most important, an assay vocabulary allows the entire schema to be dynamic by including new assays and capturing explicit differences between assays in different laboratories. The assay will also allow standardization and definition of values for a given phenotypic character, for example, how abnormal is defined in relation to body position. Implementation Our schema can be expressed using a variety of modeling tools and knowledge representation (KR) languages [25]. We chose DAG-Edit [16] (version 1.408) and Protégé-2000 [26] (version 1.9) which is Java-based, well supported and incorporates multiple inheritance, relation hierarchies, meta-classes, constraint axioms and F-Logic [27]. Although the complexity of our current models can be described with existing tools, in the future more complex phenotype domains may require migration to a finer-grained conceptualization. Populating the Mouse Phenotype Ontology The schema was designed to be easily populated using extant core ontologies, such as anatomy, and defining attributes related to each entity. The assay vocabulary can be constructed as required. Permitted values are defined in the range of different assay attributes in part devised in the form of a general scheme and in part built from the output of particular assays. Although we include for demonstration purposes three core ontologies, namely behavior, anatomy, and developmental anatomy (Figures 3 and 4), we have tested the schema only on behavior. We also include a possible structure for PATO attributes and a separate ontology for common values. We note, however, that the structure of PATO has not been finalized. Figure 3 shows the implementation of the schema in DAG-edit. Figure 4 shows a typical implementation of the Schema in Protégé 2000. Options for providing a definition, definition reference, documentation, associated annotations, synonyms, and so on, are offered in our schema. Similar options can be used for attributes using the metaslot options. Since most of the ontologies we are planning to use were generated using the DAG-edit [16] format, we had to convert them to the Protégé-2000 format using the tools and methodology described by Yeh et al. [27], with minor modifications. This task, however, should no longer be necessary as the latest version of DAG-edit allows the export of ontologies in OWL format. Modeling issues Decisions will inevitably have to be made to combine a core ontology with its attributes and then define facets of that relationship, for example, cardinality, attribute value type and attribute range. In our schema, the class hierarchy of all ontologies employed represents an 'is-a' relation. So, mouse social behavior 'is-a' mouse behavior, or mouse social behavior is a 'kind-of' mouse behavior and so forth. All other relationships, including PATO and 'part-of' relationships, are modeled as attributes. However, we note here that efforts are currently being made by the GO consortium to define and formalize the 'part-of' relationship, which is considered vital and special in bio-ontologies, especially anatomy [28]. Because our phenotype ontology and PATO need to be the result of a collaborative effort within the communities, we feel that it is important to set out the basic modeling concepts that need to be applied upon allocating attributes to the core ontologies. Deciding whether to introduce a new attribute or represent this functionality through an entity is often quite difficult. Several things need to be considered in order to make the best decision, although it should be noted that there are no clear distinction as to what is a right or wrong decision. The first thing to take into account is that subclasses of a class inherit all properties of the parent and could have additional properties and different restrictions from the latter. PATO should remain as general as possible, and, when possible, care should be taken to avoid making PATO domain specific. For example, in the behavior ontology there is a class named 'reflexes' that contains children such as 'blinking reflex', 'Preyer reflex' and 'righting reflex'. It might be worth considering having one 'attribute of reflex' available in PATO rather than creating a separate attribute 'of' for each individual reflex, such as 'attribute of blinking reflex', 'attribute of Preyer reflex', and so on. Then again, if one wishes to assign different functionalities to these properties, creating separate attributes might be useful. As a rule though, one should consider that PATO needs to be consistent, usable and interoperable if it is to be applied to the general domain of phenotypes. Repetition between core ontologies and PATO should be avoided where possible. What is also often not clear is whether one should add a new class to represent functionality or assign attributes to already existent classes. For example, think of the entity 'body position'. There are several ways to model this entity in the mouse behavior phenotype ontology. One could declare 'body position' as a child of a class called 'posture'. An 'attribute of body position' could then be assigned to this class with a range of values that might be specific to an assay, for example SHIRPA [29] allows the value 'lying on its left side' among other values to an assay for body position. Alternatively, a more general 'attribute of position' could be assigned to this class. The choice depends on the functionality of the ontology and the range of phenotypes we wish to express. If the entity requires more specific attribute values to represent specific functionalities important to the domain of knowledge, we assign more specific attributes. If this functionality is not important for the domain, we assign specific attribute values [8]. 'Body position' could also be split into an entity of 'body' and an attribute of 'position'. Again, a new class 'body position' should be assigned, if one considers the objects with different attributes as different kind of object and this distinction important in the domain. As a general rule, before assigning new classes and attributes one should consider the functionality and their role in the domain, creating more distinctions as the depth of knowledge that is required to be expressed in the ontology increases. Classes in the hierarchy should not necessarily have to introduce new properties [8]. Although, in many cases these entities could be represented as attributes, it is not necessary for the functionality of the domain. If the expert thinks that this distinction is significant for the class hierarchy and the logical representation of his knowledge of the domain, then these entities should be represented as classes [8]. An important additional consideration is whether creating new terms in an ontology results in terms that cannot be consistently distinguished experimentally ('resolution'). Conclusions We have presented here an approach to the use of ontologies in describing mouse phenotypes that could provide a platform for the consistent representation of mouse phenotypic data. We have also described in detail a possible methodology to construct applications of this schema across different domains. We have dealt with modeling issues and provide guidelines to deal with semantic and practical problems. We maintain that such modeling efforts in any domain should be done in a collaborative fashion in the community. Repetition between different parts of the mouse phenotype ontologies is unavoidable. However, the use of consistent IDs, synonyms and records for associated annotations could allow seamless integration of ontology products. The nature of the schema proposed, as well as its components, is extremely dynamic; therefore coordination of efforts is vital. The structure allows extensibility and interoperability. Although an ontology should not cover all possible information about a domain, the main idea behind this concept is to allow the phenotype ontology to cope with novel and unpredictable phenotypes and account for new assays, serving scientific autonomy and information validity and integrity. We have built a software system [30] which includes a browser that allows searching and viewing the knowledge captured though the complex relations described here and databases that allow the dynamic update of different parts of the core ontologies, including PATO, without the loss of applied facets.
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The Plant Ontology as a Tool for Comparative Plant Anatomy and Genomic Analyses
Laurel Cooper, Ramona L. Walls, Justin Elser … (2012)
The Plant Ontology (PO; http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary (‘ontology’) of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to >110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.
Author and article information
Contributors
Lorena Endara:
ORCID: https://orcid.org/0000-0003-2834-7412
Hong Cui
Journal
Journal ID (nlm-ta): Biodivers Data J
Journal ID (iso-abbrev): Biodivers Data J
Journal ID (pmc): Biodiversity Data Journal
Journal ID (publisher-id): Biodiversity Data Journal
Title:
Biodiversity Data Journal
Publisher:
Pensoft Publishers
ISSN
(Electronic):
1314-2828
Publication date Collection: 2018
Publication date
(Electronic):
28
November
2018
Issue: 6
Electronic Location Identifier: e29232
Affiliations
[1
]
University of Florida, Gainesville, United States of America
University of Florida
Gainesville
United States of America
[2
]
The Ronin Institute for Independent Scholarship, Monclair, NJ, United States of America
The Ronin Institute for Independent Scholarship
Monclair, NJ
United States of America
[3
]
Science and Technology Branch, Agriculture and Agri-Food Canada, Government of Canada,
Ottawa, Canada
Science and Technology Branch, Agriculture and Agri-Food Canada, Government of Canada
Ottawa
Canada
[5
]
College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences,
Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom
College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences,
Centre for Computational Biology, University of Birmingham
Birmingham
United Kingdom
[6
]
Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation
Trust, B15 2TT, Birmingham, United Kingdom
Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation
Trust, B15 2TT
Birmingham
United Kingdom
[7
]
Center for Studies of Information Resources, Wuhan Universtity, Wuhan, China
Center for Studies of Information Resources, Wuhan Universtity
Wuhan
China
Author notes
Corresponding authors: Lorena Endara (
clendara@
123456gmail.com
), Hong Cui (
hongcui@
123456email.arizona.edu
).
Author information
Lorena Endara
https://orcid.org/0000-0003-2834-7412
Anne E Thessen
https://orcid.org/0000-0002-2908-3327
Ramona Walls
https://orcid.org/0000-0001-8815-0078
Steven S. Chong
https://orcid.org/0000-0003-1264-1166
Article
Publisher ID: Biodiversity Data Journal Other ID: 9817DOI: 10.3897/BDJ.6.e29232
PMC ID: 6281706
SO-VID: cc0badea-2e8a-4cc9-8c63-08fb792c635e
Copyright © Lorena Endara, Anne Thessen, Heather Cole, Ramona Walls, Georgios Gkoutos, Yujie Cao,
Steven Chong, Hong Cui
License:
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 21
August
2018
Date
accepted
: 20
November
2018
Page count
Figures: 4,
Tables: 4,
References: 34
Categories
Subject:
Research Article
Subject:
Animalia
Subject:
Archaea
Subject:
Bacteria
Subject:
Chromista
Subject:
Fungi
Subject:
Plantae
Subject:
Protozoa
Subject:
Bioinformatics
Subject:
Cenozoic
Subject:
Mesozoic
Subject:
Paleozoic
Subject:
Precambrian
Subject:
World
Data availability: