Anatomy Ontology Research Articles

A hymenopterists’ guide to the Hymenoptera Anatomy Ontology: utility, clarification, and future directions

Abstract Hymenoptera exhibit an incredible diversity of phenotypes, the result of ~240 million years of evolution and the primary subject of more than 250 years of research. Here we describe the history, development, and utility of the Hymenoptera Anatomy Ontology (HAO) and its associated applications. These resourc -es are designed to facilitate accessible and extensible research on hymenopteran phenotypes. Outreach with the hymenopterist community is of utmost importance to the HAO project, and this paper is a direct response to questions that arose from project workshops. In a concerted attempt to surmount barriers of understanding, especially regarding the format, utility, and development of the HAO, we discuss the roles of homology, “preferred terms”, and “structural equivalency”. We also outline the use of Universal Resource Identifiers (URIs) and posit that they are a key element necessary for increasing the objectivity and repeatability of science that references hymenopteran anatomy. Pragmatically, we detail a mechanism (the “URI table”) by which authors can use URIs to link their published text to the HAO, and we describe an associated tool (the “Analyzer”) to derive these tables. These tools, and others, are available through the HAO Portal website (http://portal.hymao.org). We conclude by discussing the future of the HAO with respect to digital publication, cross-taxon ontology alignment, the advent of semantic phenotypes, and community-based curation.

500,000 fish phenotypes: The new informatics landscape for evolutionary and developmental biology of the vertebrate skeleton.

The rich phenotypic diversity that characterizes the vertebrate skeleton results from evolutionary changes in regulation of genes that drive development. Although relatively little is known about the genes that underlie the skeletal variation among fish species, significant knowledge of genetics and development is available for zebrafish. Because developmental processes are highly conserved, this knowledge can be leveraged for understanding the evolution of skeletal diversity. We developed the Phenoscape Knowledgebase (KB; http://kb.phenoscape.org) to yield testable hypotheses of candidate genes involved in skeletal evolution. We developed a community anatomy ontology for fishes and ontology-based methods to represent complex free-text character descriptions of species in a computable format. With these tools, we populated the KB with comparative morphological data from the literature on over 2500 teleost fishes (mainly Ostariophysi) resulting in over 500,000 taxon phenotype annotations. The KB integrates these data with similarly structured phenotype data from zebrafish genes (http://zfin.org). Using ontology-based reasoning, candidate genes can be inferred for the phenotypes that vary across taxa, thereby uniting genetic and phenotypic data to formulate evo-devo hypotheses. The morphological data in the KB can be browsed, sorted, and aggregated in ways that provide unprecedented possibilities for data mining and discovery.

Skeletal morphology of Opius dissitus and Biosteres carbonarius (Hymenoptera: Braconidae), with a discussion of terminology.

The Braconidae, a family of parasitic wasps, constitute a major taxonomic challenge with an estimated diversity of 40,000 to 120,000 species worldwide, only 18,000 of which have been described to date. The skeletal morphology of braconids is still not adequately understood and the terminology is partly idiosyncratic, despite the fact that anatomical features form the basis for most taxonomic work on the group. To help address this problem, we describe the external skeletal morphology of Opius dissitus Muesebeck 1963 and Biosteres carbonarius Nees 1834, two diverse representatives of one of the least known and most diverse braconid subfamilies, the Opiinae. We review the terminology used to describe skeletal features in the Ichneumonoidea in general and the Opiinae in particular, and identify a list of recommend terms, which are linked to the online Hymenoptera Anatomy Ontology. The morphology of the studied species is illustrated with SEM-micrographs, photos and line drawings. Based on the examined species, we discuss intraspecific and interspecific morphological variation in the Opiinae and point out character complexes that merit further study.

Ontological labels for automated location of anatomical shape differences

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.

The mouse-human anatomy ontology mapping project

The overall objective of the Mouse–Human Anatomy Project (MHAP) was to facilitate the mapping and harmonization of anatomical terms used for mouse and human models by Mouse Genome Informatics (MGI) and the National Cancer Institute (NCI). The anatomy resources designated for this study were the Adult Mouse Anatomy (MA) ontology and the set of anatomy concepts contained in the NCI Thesaurus (NCIt). Several methods and software tools were identified and evaluated, then used to conduct an in-depth comparative analysis of the anatomy ontologies. Matches between mouse and human anatomy terms were determined and validated, resulting in a highly curated set of mappings between the two ontologies that has been used by other resources. These mappings will enable linking of data from mouse and human. As the anatomy ontologies have been expanded and refined, the mappings have been updated accordingly. Insights are presented into the overall process of comparing and mapping between ontologies, which may prove useful for further comparative analyses and ontology mapping efforts, especially those involving anatomy ontologies. Finally, issues concerning further development of the ontologies, updates to the mapping files, and possible additional applications and significance were considered.Database URL: http://obofoundry.org/cgi-bin/detail.cgi?id=ma2ncit

A strategy for building neuroanatomy ontologies.

Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined. We formally define a set of relations for recording the spatial and connectivity relationships of neuron classes and brain regions in a broad range of species, from vertebrates to arthropods. We illustrate the utility of our approach via its application in the ontology that drives the Virtual Fly Brain web resource. The relations we define are available from http://purl.obolibrary.org/obo/ro.owl. They are used in the Drosophila anatomy ontology (http://purl.obolibrary.org/obo/fbbt/2011-09-06/), which drives the web resource http://www.virtualflybrain.org

VHOG, a multispecies vertebrate ontology of homologous organs groups

Motivation: Most anatomical ontologies are species-specific, whereas a framework for comparative studies is needed. We describe the vertebrate Homologous Organs Groups ontology, vHOG, used to compare expression patterns between species.Results: vHOG is a multispecies anatomical ontology for the vertebrate lineage. It is based on the HOGs used in the Bgee database of gene expression evolution. vHOG version 1.4 includes 1184 terms, follows OBO principles and is based on the Common Anatomy Reference Ontology (CARO). vHOG only describes structures with historical homology relations between model vertebrate species. The mapping to species-specific anatomical ontologies is provided as a separate file, so that no homology hypothesis is stated within the ontology itself. Each mapping has been manually reviewed, and we provide support codes and references when available.Availability and implementation: vHOG is available from the Bgee download site (http://bgee.unil.ch/), as well as from the OBO Foundry and the NCBO Bioportal websites.Contact: bgee@isb-sib.ch; frederic.bastian@unil.ch

An Intelligent System Approach for Integrating Anatomical Ontologies

ABSTRACTRecent years have seen a vast amount of data generated by various biological and biomedical experiments. The storage, management and analysis of this data, is done by means of the modern bioinformatics applications and tools. One of the bioinformatics instruments used for solving these tasks, are ontologies and the apparatus they provide. Ontology as a modeling tool is a specification of a conceptualization meaning that an ontology is a formal description of the concepts and relationships that can exist for a given software system or software agent (8, 10). Anatomical (phenotypic) ontologies of various species nowadays typically contain from few thousands to few tens of thousands of terms and relations (which is a very small number compared to the count of objects and the amount of data produced by biological experiments at the molecular level, for example) but usually the semantics employed in them is enormous in scale. The major problem when using such ontologies is that they lack intelligent tools for cross-species literature searches (text mining) as well as tools aiding the design of new biological and biomedical experiments with other (notyet tested) species/organisms, based on available information about experiments already performed on certain model species/organisms.This is where the process of merging anatomical ontologies comes into use. Using specific models and algorithms for merging of such ontologies is a matter of choice. In this work a novel approach for solving this task, based on two directed acyclic graph (DAG) models and three original algorithmic procedures is presented. Based on them, an intelligent software system for merging two (and possibly more) input/source anatomical ontologies into one output/target super-ontology was designed and implemented. This system was named AnatOM (an abbreviation from “Anatomical Ontologies Merger”).In this work a short overview of ontologies is provided describing what ontologies are and why they are widely used as a tool in bioinformatics. The problem of merging anatomical ontologies of two or more different organisms is introduced and some effort has been put into explaining why it is important. A general outline is presented of the models and the method that have been developed for solving the ontologies merging problem. A high-level overview of the AnatOM program implemented by the authors as part of this work is also provided.To achieve the degree of intelligence that is needed, the AnatOM program utilizes the large amount of high-quality data (knowledge) available in several widely popular and generally recognized knowledge bases such as UMLS, FMA, and WordNet. The last one of these is a general-purpose i.e. non-specialized knowledge source. The first two are biological/biomedical ones. Their choice was based on the fact that they provide a very good foundation for building an intelligent system that performs certain comparative anatomy tasks including mapping and merging of anatomical ontologies (23).

The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs

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/.

Spatial-symbolic Query Engine in Anatomy

Currently, the primary means for answering anatomical questions such as 'what vital organs would potentially be impacted by a bullet wound to the abdomen?' is to look them up in textbooks or to browse online sources. In this work we describe a semantic web service and spatial query processor that permits a user to graphically pose such questions as joined queries over separately defined spatial and symbolic knowledge sources. Spatial relations (e.g. anterior) were defined by two anatomy experts, and based on a 3-D volume of labeled images of the thorax, all the labeled anatomical structures were queried to retrieve the target structures for every query structure and every spatial relation. A web user interface and a web service were designed to relate existing symbolic information from the Foundational Model of Anatomy ontology (FMA) with spatial information provided by the spatial query processor, and to permit users to select anatomical structures and define queries. We evaluated the accuracy of results returned by the queries, and since there is no independent gold standard, we used two anatomy experts' opinions as the gold standard for comparison. We asked the same experts to define the gold standard and to define the spatial relations. The F-measure for the overall evaluation is 0.90 for rater 1 and 0.56 for rater 2. The percentage of observed agreement is 99% and Cohen's kappa coefficient reaches 0.51. The main source of disagreement relates to issues with the labels used in the dataset, and not with the tool itself. In its current state the system can be used as an end-user application but it is likely to be of most use as a framework for building end-user applications such as displaying the results as a 3-D anatomical scene. The system promises potential practical utility for obtaining and navigating spatial and symbolic data.

A Unified Anatomy Ontology of the Vertebrate Skeletal System

A Unified Anatomy Ontology of the Vertebrate Skeletal System

A hymenopterists’ guide to the Hymenoptera Anatomy Ontology: utility, clarification, and future directions

A hymenopterists’ guide to the Hymenoptera Anatomy Ontology: utility, clarification, and future directions

Uberon, an integrative multi-species anatomy ontology

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org

BOAT: Automatic alignment of biomedical ontologies using term informativeness and candidate selection

The biomedical sciences is one of the few domains where ontologies are widely being developed to facilitate information retrieval and knowledge sharing, but there still remains the problem that applications using different ontologies cannot share knowledge without explicit references between overlapping concepts. Ontology alignment is the task of identifying such equivalence relations between concepts across ontologies. Its application to the biomedical domain should address two open issues: (1) determining the equivalence of concept-pairs which have overlapping terms in their names, and (2) the high run-time required to align large ontologies which are typical in the biomedical domain. To address them, we present a novel approach, named the Biomedical Ontologies Alignment Technique (BOAT), which is state-of-the-art in terms of F-measure, precision and speed. A key feature of BOAT is that it considers the informativeness of each component word in the concept labels, which has significant impact on biomedical ontologies, resulting in a 12.2% increase in F-measure. Another important feature of BOAT is that it selects for comparison only concept pairs that show high likelihoods of equivalence, based on the similarity of their annotations. BOAT’s F-measure of 0.88 for the alignment of the mouse and human anatomy ontologies is on par with that of another state-of-the-art matcher, AgreementMaker, while taking a shorter time.

Pictorial key for females of Decevania Huben (Hymenoptera, Evaniidae) and description of a new species

Decevania Huben currently comprises 13 species, the females of which are known for only four. Herein an additional Neotropical Decevania is newly described: Decevania feitosai Kawada, sp. n. from Colombia. The description and identification key were made using the DELTA program. A pictorial key to females of Decevania is provided. Anatomical terminology follows the Hymenoptera Anatomy Ontology project with an atlas for terminologies used for recognition of Decevania species. The distribution maps can be accessed in Google Maps or through of Dryad (repository of data).

PhenomeNET: a whole-phenome approach to disease gene discovery

Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the PhenomeNET cross-species phenotype network. We demonstrate that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene–disease associations through the comparison of mutant phenotypes. We provide evidence that the Adam19 and Fgf15 genes in mice are involved in the tetralogy of Fallot, and, using zebrafish phenotypes, propose the hypothesis that the mammalian homologs of Cx36.7 and Nkx2.5 lie in a pathway controlling cardiac morphogenesis and electrical conductivity which, when defective, cause the tetralogy of Fallot phenotype. Our method implements a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.

The GUDMAP database – an online resource for genitourinary research

The GenitoUrinary Development Molecular Anatomy Project (GUDMAP) is an international consortium working to generate gene expression data and transgenic mice. GUDMAP includes data from large-scale in situ hybridisation screens (wholemount and section) and microarray gene expression data of microdissected, laser-captured and FACS-sorted components of the developing mouse genitourinary (GU) system. These expression data are annotated using a high-resolution anatomy ontology specific to the developing murine GU system. GUDMAP data are freely accessible at www.gudmap.org via easy-to-use interfaces. This curated, high-resolution dataset serves as a powerful resource for biologists, clinicians and bioinformaticians interested in the developing urogenital system. This paper gives examples of how the data have been used to address problems in developmental biology and provides a primer for those wishing to use the database in their own research.

Gill arch and hyoid arch diversity and cypriniform phylogeny: Distributed integration of morphology and web-based tools

Using the multiple tools available to support an online collaborative environment, we surveyed 62 morphological features from the hyoid arch and gill arches of 53 species of cypriniform fishes that matched those sampled in recent molecularanalyses and two sets of outgroup species (‘Saitoh outgroups’ and ‘Basal outgroups’). This is a skeletal region whose variation is considered historically significant within fishes and Cypriniformes in particular, and we review previous work in light of our own. The clarity of description of characters was enhanced by the use of a community reference ontology, the Teleost Anatomy Ontology. Terms, synonyms, and definitions for skeletal features from this region were contributed to this ontology, and links to these terms and relationships are included in our character descriptions. One thousand two hundred sixty-three images of features from this region were linked to ontology terms and deposited in a community image repository, Morphbank; these are linked to the characters described herein. Character data were analyzed using parsimony and Bayesian methods for two sets of outgroups, one of which matched that used in previous molecular analyses. The parsimony results, using either outgroup set, indicated similar higher-level relationships, including a sister group relationship between cyprinids and loaches. A basal trichotomy among Gyrinocheilus, catostomids and cyprinids + loaches was the result of Saitoh outgroup analysis in contrast to a sister group relationship between Gyrinocheilus and catostomids discovered in the Basal outgroup analysis. Interestingly, analyses including basal outgroups recovered a monophyletic Cyprinidae, consistent with all previous studies. Character evolution that supports higher-level nodes of interest in the consensus tree is described. In some respects, it might be a surprise that 62 morphological characters from a small skeletal region for only 53 cypriniform species (out of over 3,200 total species) could provide as much resolution as it does. We expect, however, further phylogenetic resolution as morphological data from across skeleton regions are combined, species sampling is increased, and molecular data are added.

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning

In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.

Automatically identifying and annotating mouse embryo gene expression patterns

Motivation: Deciphering the regulatory and developmental mechanisms for multicellular organisms requires detailed knowledge of gene interactions and gene expressions. The availability of large datasets with both spatial and ontological annotation of the spatio-temporal patterns of gene expression in mouse embryo provides a powerful resource to discover the biological function of embryo organization. Ontological annotation of gene expressions consists of labelling images with terms from the anatomy ontology for mouse development. If the spatial genes of an anatomical component are expressed in an image, the image is then tagged with a term of that anatomical component. The current annotation is done manually by domain experts, which is both time consuming and costly. In addition, the level of detail is variable, and inevitably errors arise from the tedious nature of the task. In this article, we present a new method to automatically identify and annotate gene expression patterns in the mouse embryo with anatomical terms.Results: The method takes images from in situ hybridization studies and the ontology for the developing mouse embryo, it then combines machine learning and image processing techniques to produce classifiers that automatically identify and annotate gene expression patterns in these images. We evaluate our method on image data from the EURExpress study, where we use it to automatically classify nine anatomical terms: humerus, handplate, fibula, tibia, femur, ribs, petrous part, scapula and head mesenchyme. The accuracy of our method lies between 70% and 80% with few exceptions. We show that other known methods have lower classification performance than ours. We have investigated the images misclassified by our method and found several cases where the original annotation was not correct. This shows our method is robust against this kind of noise.Availability: The annotation result and the experimental dataset in the article can be freely accessed at http://www2.docm.mmu.ac.uk/STAFF/L.Han/geneannotation/.Contact: l.han@mmu.ac.ukSupplementary Information: Supplementary data are available at Bioinformatics online.