Anatomy Ontology Research Articles

Utilizing descriptive statements from the biodiversity heritage library to expand the Hymenoptera Anatomy Ontology.

Hymenoptera, the insect order that includes sawflies, bees, wasps, and ants, exhibits an incredible diversity of phenotypes, with over 145,000 species described in a corpus of textual knowledge since Carolus Linnaeus. In the absence of specialized training, often spanning decades, however, these articles can be challenging to decipher. Much of the vocabulary is domain-specific (e.g., Hymenoptera biology), historically without a comprehensive glossary, and contains much homonymous and synonymous terminology. The Hymenoptera Anatomy Ontology was developed to surmount this challenge and to aid future communication related to hymenopteran anatomy, as well as provide support for domain experts so they may actively benefit from the anatomy ontology development. As part of HAO development, an active learning, dictionary-based, natural language recognition tool was implemented to facilitate Hymenoptera anatomy term discovery in literature. We present this tool, referred to as the ‘Proofer’, as part of an iterative approach to growing phenotype-relevant ontologies, regardless of domain. The process of ontology development results in a critical mass of terms that is applied as a filter to the source collection of articles in order to reveal term occurrence and biases in natural language species descriptions. Our results indicate that taxonomists use domain-specific terminology that follows taxonomic specialization, particularly at superfamily and family level groupings and that the developed Proofer tool is effective for term discovery, facilitating ontology construction.

Matching arthropod anatomy ontologies to the Hymenoptera Anatomy Ontology: results from a manual alignment

Matching is an important step for increasing interoperability between heterogeneous ontologies. Here, we present alignments we produced as domain experts, using a manual mapping process, between the Hymenoptera Anatomy Ontology and other existing arthropod anatomy ontologies (representing spiders, ticks, mosquitoes and Drosophila melanogaster). The resulting alignments contain from 43 to 368 mappings (correspondences), all derived from domain-expert input. Despite the many pairwise correspondences, only 11 correspondences were found in common between all ontologies, suggesting either major intrinsic differences between each ontology or gaps in representing each group’s anatomy. Furthermore, we compare our findings with putative correspondences from Bioportal (derived from LOOM software) and summarize the results in a total evidence alignment. We briefly discuss characteristics of the ontologies and issues with the matching process.Database URL: http://purl.obolibrary.org/obo/hao/2012-07-18/arthropod-mappings.obo

Enhanced XAO: the ontology of Xenopus anatomy and development underpins more accurate annotation of gene expression and queries on Xenbase

BackgroundThe African clawed frogs Xenopus laevis and Xenopus tropicalis are prominent animal model organisms. Xenopus research contributes to the understanding of genetic, developmental and molecular mechanisms underlying human disease. The Xenopus Anatomy Ontology (XAO) reflects the anatomy and embryological development of Xenopus. The XAO provides consistent terminology that can be applied to anatomical feature descriptions along with a set of relationships that indicate how each anatomical entity is related to others in the embryo, tadpole, or adult frog. The XAO is integral to the functionality of Xenbase (http://www.xenbase.org), the Xenopus model organism database.ResultsWe significantly expanded the XAO in the last five years by adding 612 anatomical terms, 2934 relationships between them, 640 synonyms, and 547 ontology cross-references. Each term now has a definition, so database users and curators can be certain they are selecting the correct term when specifying an anatomical entity. With developmental timing information now asserted for every anatomical term, the ontology provides internal checks that ensure high-quality gene expression and phenotype data annotation. The XAO, now with 1313 defined anatomical and developmental stage terms, has been integrated with Xenbase expression and anatomy term searches and it enables links between various data types including images, clones, and publications. Improvements to the XAO structure and anatomical definitions have also enhanced cross-references to anatomy ontologies of other model organisms and humans, providing a bridge between Xenopus data and other vertebrates. The ontology is free and open to all users.ConclusionsThe expanded and improved XAO allows enhanced capture of Xenopus research data and aids mechanisms for performing complex retrieval and analysis of gene expression, phenotypes, and antibodies through text-matching and manual curation. Its comprehensive references to ontologies across taxa help integrate these data for human disease modeling.

A unified approach for debugging is-a structure and mappings in networked taxonomies

BackgroundWith the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologies and ontology mappings has become increasingly important. These defects can lead to wrong or incomplete results for the applications.ResultsWe propose a unified framework for debugging the is-a structure of and mappings between taxonomies, the most used kind of ontologies. We present theory and algorithms as well as an implemented system RepOSE, that supports a domain expert in detecting and repairing missing and wrong is-a relations and mappings. We also discuss two experiments performed by domain experts: an experiment on the Anatomy ontologies from the Ontology Alignment Evaluation Initiative, and a debugging session for the Swedish National Food Agency.ConclusionsSemantically-enabled applications need high quality ontologies and ontology mappings. One key aspect is the detection and removal of defects in the ontologies and ontology mappings. Our system RepOSE provides an environment that supports domain experts to deal with this issue. We have shown the usefulness of the approach in two experiments by detecting and repairing circa 200 and 30 defects, respectively.

Matching arthropod anatomy ontologies to the Hymenoptera Anatomy Ontology: results from a manual alignment

Matching arthropod anatomy ontologies to the Hymenoptera Anatomy Ontology: results from a manual alignment

EMAP/EMAPA ontology of mouse developmental anatomy: 2013 update

BackgroundThe Edinburgh Mouse Atlas Project (EMAP) ontology of mouse developmental anatomy provides a standard nomenclature for describing normal and mutant mouse embryo anatomy. The ontology forms the core of the EMAP atlas and is used for annotating gene expression data by the mouse Gene Expression Database (GXD), Edinburgh Mouse Atlas of Gene Expression (EMAGE) and other database resources.FindingsThe original EMAP ontology listed anatomical entities for each developmental stage separately, presented as uniparental graphs organized as a strict partonomy. An "abstract" (i.e. non-stage-specific) representation of mouse developmental anatomy has since been developed. In this version (EMAPA) all instances for a given anatomical entity are presented as a single term, together with the first and last stage at which it is considered to be present. Timed-component anatomies are now derived using staging information in the "primary" non-timed version. Anatomical entities are presented as a directed acyclic graph enabling multiple parental relationships. Subsumption classification as well as partonomic and other types of relationships can now be represented. Most concept names are unique, with compound names constructed using standardized nomenclature conventions, and alternative names associated as synonyms.ConclusionsThe ontology has been extended and refined in a collaborative effort between EMAP and GXD, with additional input from others. Efforts are also underway to improve the revision process with regards to updating and editorial control. The revised EMAPA ontology is freely available from the OBO Foundry resource, with descriptive information and other documentation presented in associated Wiki pages (http://www.obofoundry.org/wiki/index.php/EMAPA:Main_Page).

Male terminalia of Ceraphronoidea: morphological diversity in an otherwise monotonous taxon

The skeletomuscular system of male terminalia in Evaniomorpha (Hymenoptera) is described and the functional morphology of male genitalia is discussed. Confocal laser scanning microscopy is the primary method used for illustrating anatomical phenotypes, and a domain-specific anatomy ontology is employed to more explicitly describe anatomical structures. A comprehensive data set of ceraphronoid male genitalia is analyzed, yielding the first phylogeny of the superfamily. One hundred and one taxa, including three outgroups, are scored for 48 characters. Ceraphronoidea are recovered as sister to the remaining Evaniomorpha in the implied weighting analyses. Numerous character states suggest that Ceraphronoidea is a relatively basal apocritan lineage. Ceraphronoidea, Ceraphronidae, and Megaspilinae are each retrieved as monophyletic in all analyses. Megaspilidae is not recovered as monophyletic. Lagynodinae is monophyletic in the implied weighting analyses with strong support and is a polytomy in the equal weighting analysis. Lagynodinae shares numerous plesiomorphies with both Megaspilinae and Ceraphronidae. Relationships among genera are weakly corroborated.Masneris sister of Ceraphronidae.Trassediais nested within Ceraphronidae based on the present analysis. Because of this and numerous features shared between it andCeraphronwe transferTrassediafrom Megaspilidae to Ceraphronidae.Dendrocerusforms a single monophyletic clade, with modest support, together with someConostigmusspecies. This result challenges the utility of such traditional diagnostic characters as ocellar arrangement and shape of the male flagellomeres.Aphanogmusis monophyletic in the implied weighting, but remains a polytomy withCeraphronin the equal weighting analysis.Gnathoceraphronis always nested within a well-supportedAphanogmusclade.CyoceraphronandElysoceraphronare nested withinCeraphronandAphanogmus, respectively. The male genitalia prove to be a substantial source of phylogenetically relevant information. Our results indicate that a reclassification of Ceraphronoidea both at the family and generic level is necessary but that more data are required.

The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments

BackgroundThe Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience.DescriptionRecently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases.ConclusionsIn this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community.

OntoCATI: Towards an ontology of neuroimaging measures in the CATI Platform

Event Abstract Back to Event OntoCATI: Towards an ontology of neuroimaging measures in the CATI Platform Lydie EDWARD1, 2*, Grégory Operto2, 3, Sylvain Poret1, 2, Yann Cointepas2, 3, Nader Cheaib2, 3, Leila Makkaoui2, 3 and Bénédicte M. Batrancourt1, 2 1 CRICM UPMC/INSERM UMR_S975, France 2 CATI, France 3 CEA, UNATI/NeuroSpin/I2BM, France CATI (Acquisition Centre and Image Processing, a French national platform supported by the Alzheimer’s Foundation [1]) provides software solutions for automatic measurement of different biomarkers related to specific modalities (Figure 1). CATI provides assistance for acquiring, analyzing, organizing and sharing neuroimaging data in the context of multi-centric clinical research studies. Our goal is to support the scientific study of the different working groups (neurologist, MRI PET physician, quality check operations engineer, engineer developing brain imaging tools) and help them to model the knowledge linked to their domain of interest. We proposed a structured metadata hierarchy for storing information relevant to various aspects of a project (e.g. study, subject, dataset, etc.) along with derived data and process in order to develop improved semantic request and reasoning tools for knowledge extraction on the derived data (for example we would like to compare the hippocampal volumes obtained on different time points). We developed a database schema CATISchema and an ontology OntoCATI [2]. At this stage, the first module of OntoCATI supports the information concerning acquisition, raw data (clinical and neuroimaging), and image processing. This module uses DOLCE [3] as its upper ontology. The image processing generates a multitude of data and derived measures. These measures are potential biomarkers for the Alzheimer’s disease (AD). In order to build the second module of OntoCATI dedicated to these measurements we first focus on the data issued from the modality T1 MRI and on three types of measures: rate of hippocampal atrophy with the longitudinal version of SACHA [4], cortical thickness reduction with Freesurfer (http://ftp.nmr.mgh.harvard.edu/) and opening of primary cortical folds using the longitudinal version of Morphologist-2012 pipeline [5]. An important point in our modeling consists in distinguishing the concept of measure and biomarker. For example, hippocampal volume is considered as the most widely used and admitted biomarkers in the AD to monitor its progression [6]. Therefore in OntoCATI this measure belongs to the sub-class of biomarker. Concerning the other measures the ontology will allow us to determine what measures become biomarkers using queries. To model the different measurements that are the assignment of numbers to anatomical entities, we need to describe the brain anatomy (e.g., hippocampus), the measure (magnitude, dimensions (units) and uncertainty) and the disease concepts and their codification. This module of OntoCATI uses the Basic Formal Ontology (BFO) as its upper level ontology [7]. For the anatomy domain we refer to the Foundational Model of Anatomy ontology (FMA) [8], which adopts and extends into BFO [9], a domain-independent, spatio-temporal theory that provides a rigorous ontological framework. Concerning the pathology domain we rely on ICD-10 [10] and UMLS standard [11]. For example to describe the hippocampus, according to FMA, we define hippocampus as a ‘gyrus of limbic lobe' that is a ‘segment of cerebral hemisphere’ that is a subClass of ‘Anatomical Entity’ that is a subclass of the BFO concept 'independent continuant'. To represent the volume of the hippocampus we create the relation has for volume that is linking the concept of ‘hippocampus’ to the BFO concept ‘three dimensional region’ (Figure 2). This is the first step in our work. In the future, we will integrate other concepts to represent the other measures (cortical thickness, opening of primary cortical folds, etc.). We will also work on the other modalities presented in Figure 1 and we will develop the queries to cross the frontier between measures and biomarkers. Figure 1 Figure 2 Acknowledgements This work is funded by the French Foundation Plan Alzheimer and was realized in the CATI study group. The authors gratefully acknowledge all contributors.

Predicting and Scoring Links in Anatomical Ontology Mapping

The paper presents a work performed in the area of automatic and semi-automatic ontology mapping. A method for inferring additional cross-ontology links while mapping anatomical ontologies is described and the results of some experiments performed with various external knowledge sources and scoring schemes are discussed as well.

A Unified Anatomy Ontology of the Vertebrate Skeletal System

The skeleton is of fundamental importance in research in comparative vertebrate morphology, paleontology, biomechanics, developmental biology, and systematics. Motivated by research questions that require computational access to and comparative reasoning across the diverse skeletal phenotypes of vertebrates, we developed a module of anatomical concepts for the skeletal system, the Vertebrate Skeletal Anatomy Ontology (VSAO), to accommodate and unify the existing skeletal terminologies for the species-specific (mouse, the frog Xenopus, zebrafish) and multispecies (teleost, amphibian) vertebrate anatomy ontologies. Previous differences between these terminologies prevented even simple queries across databases pertaining to vertebrate morphology. This module of upper-level and specific skeletal terms currently includes 223 defined terms and 179 synonyms that integrate skeletal cells, tissues, biological processes, organs (skeletal elements such as bones and cartilages), and subdivisions of the skeletal system. The VSAO is designed to integrate with other ontologies, including the Common Anatomy Reference Ontology (CARO), Gene Ontology (GO), Uberon, and Cell Ontology (CL), and it is freely available to the community to be updated with additional terms required for research. Its structure accommodates anatomical variation among vertebrate species in development, structure, and composition. Annotation of diverse vertebrate phenotypes with this ontology will enable novel inquiries across the full spectrum of phenotypic diversity.

The Plant Ontology as a Tool for Comparative Plant Anatomy and Genomic Analyses

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.

An Algorithmic Approach to Inferring Cross-Ontology Links while Mapping Anatomical Ontologies

Automated and semi-automated mapping and the subsequently merging of two (or more) anatomical ontologies can be achieved by (at least) two direct procedures. The first concerns syntactic matching between the terms of the two ontologies; in this paper, we call this direct matching (DM). It relies on identities between the terms of the two input ontologies in order to establish cross-ontology links between them. The second involves consulting one or more external knowledge sources and utilizing the information available in them, thus providing additional information as to how terms (concepts) from the two input ontologies are related/linked to each other. Each of the two ontologies is aligned to an external knowledge source and links representing synonymy, is-a parent-child, and part-of parent-child relations, are drawn between the ontology and the knowledge source. These links are then run through a set of simple logical rules in order to come up with cross-ontology links between the two input ontologies. This method is known as semantic matching. It proves useful and reasonably accurate; in this paper, we call it the source matching predictions (SMP) procedure.Not all cross-ontology links that semantically (i.e., from a biological/anatomical standpoint) exist between the two input ontologies will be discovered by either DM or SMP. To improve the discovery of cross-ontology links we propose a novel algorithmic procedure which involves a probability-like scoring scheme. This procedure is called the child matching predictions (CMP) procedure. Describing the DM, SMP, CMP procedures, and particularly the CMP procedure in formal terms is the main goal of this paper.

DcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more

We present ‘dcGO’ (http://supfam.org/SUPERFAMILY/dcGO), a comprehensive ontology database for protein domains. Domains are often the functional units of proteins, thus instead of associating ontological terms only with full-length proteins, it sometimes makes more sense to associate terms with individual domains. Domain-centric GO, ‘dcGO’, provides associations between ontological terms and protein domains at the superfamily and family levels. Some functional units consist of more than one domain acting together or acting at an interface between domains; therefore, ontological terms associated with pairs of domains, triplets and longer supra-domains are also provided. At the time of writing the ontologies in dcGO include the Gene Ontology (GO); Enzyme Commission (EC) numbers; pathways from UniPathway; human phenotype ontology and phenotype ontologies from five model organisms, including plants; anatomy ontologies from three organisms; human disease ontology and drugs from DrugBank. All ontological terms have probabilistic scores for their associations. In addition to associations to domains and supra-domains, the ontological terms have been transferred to proteins, through homology, providing annotations of >80 million sequences covering 2414 complete genomes, hundreds of meta-genomes, thousands of viruses and so forth. The dcGO database is updated fortnightly, and its website provides downloads, search, browse, phylogenetic context and other data-mining facilities.

New neotropical species of Opiinae (Hymenoptera, Braconidae) reared from fruit-infesting and leaf-mining Tephritidae (Diptera) with comments on the Diachasmimorpha mexicana species group and the genera Lorenzopius and Tubiformopius

Four new species of opiine Braconidae are described from Mexico. These are Diachasmimorpha martinalujai Wharton reared from Rhagoletis infesting fruits of Crataegus spp., Diachasmimorpha norrbomi Wharton reared from Euphranta mexicana infesting fruits of Ribes pringlei, Eurytenes (Stigmatopoea) norrbomi Wharton reared from Trypeta concolor mining leaves of Barkleyanthus salicifolia and Eurytenes (Stigmatopoea) maya Wharton reared from Rhagoletis pomonella infesting apples and fruits of Crataegus spp. Morphological features of the first metasomal segment and occipital carina, useful for placement of these species, are discussed relative to the genera Diachasmimorpha, Eurytenes, Lorenzopius, Tubiformopius, and Opius s.l. Descriptions and diagnoses are referenced to the Hymenoptera Anatomy Ontology. The following represent new combinations: Diachasmimorpha hildagensis, Lorenzopius euryteniformis, and Tubiformopius tubibasis. Revised diagnoses are provided for Diachasmimorpha hildagensis, Diachasmimorpha mexicana, Diachasmimorpha sanguinea, Eurytenes (Stigmatopoea), Lorenzopius, Lorenzopius euryteniformis, Tubiformopius, Tubiformopius tubigaster, Tubiformopius tubibasis, Opius incoligma, and Opius rugicoxis. Two species groups are delineated within Lorenzopius and a key to species of Diachasmimorpha occurring in the New World is provided.

Development of a text search engine for medicinal chemistry patents

Division of Medical Information Sciences (SIMED), University Hospitals of Geneva and University of Geneva, Geneva, Switzerland Bibliomics and Text-Mining Group (BiTeM), Information Science Department, University of Applied Sciences, Geneva, Switzerland Novartis Institute for BioMedical Research – Text Mining Services (NIBR-IT/TMS), Novartis Pharma AG, Basel, Switzerland e-mail: emilie.pasche@hcuge.ch (corresponding), julien.gobeill@hesge.ch, fatma.oezdemirzaech@novartis.com, therese.vachon@novartis.com, christian.lovis@hcuge.ch, patrick.ruch@hesge.ch

A UML profile for the OBO relation ontology

BackgroundOntologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain.ResultsWe have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO).ConclusionsThe use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology.

A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation

The Neotropical evaniid genus Evaniscus Szépligeti currently includes six species. Two new species are described, Evaniscus lansdownei Mullins, sp. n. from Colombia and Brazil and Evaniscus rafaeli Kawada, sp. n. from Brazil. Evaniscus sulcigenis Roman, syn. n., is synonymized under Evaniscus rufithorax Enderlein. An identification key to species of Evaniscus is provided. Thirty-five parsimony informative morphological characters are analyzed for six ingroup and four outgroup taxa. A topology resulting in a monophyletic Evaniscus is presented with Evaniscus tibialis and Evaniscus rafaeli as sister to the remaining Evaniscus species. The Hymenoptera Anatomy Ontology and other relevant biomedical ontologies are employed to create semantic phenotype statements in Entity-Quality (EQ) format for species descriptions. This approach is an early effort to formalize species descriptions and to make descriptive data available to other domains.

A new ontology (structured hierarchy) of human developmental anatomy for the first 7 weeks (Carnegie stages 1–20)

This paper describes a new ontology of human developmental anatomy covering the first 49 days [Carnegie stages (CS)1-20], primarily structured around the parts of organ systems and their development. The ontology includes more than 2000 anatomical entities (AEs) that range from the whole embryo, through organ systems and organ parts down to simple or leaf tissues (groups of cells with the same morphological phenotype), as well as features such as cavities. Each AE has assigned to it a set of facts of the form <AE><relationship><parent>, with the relationships including starts_at and ends_at (CSs), part_of (there can be several parents) and is_a (this gives the type of tissue, from an organ system down to one of ~ 80 simple tissues predominantly composed of a single cell kind, which is also specified). Leaf tissues also have a develops_from link to its parent tissue. The ontology includes ~14 000 such facts, which are mainly from the literature and an earlier ontology of human developmental anatomy (EHDAA, now withdrawn). The relationships enable these facts to be integrated into a single, complex hierarchy (or mathematical graph) that was made and can be viewed in the OBO-Edit browser (oboedit.org). Each AE has an EHDAA2 ID that may be useful in an informatics context, while the ontology as a whole can be used for organizing databases of human development. It is also a knowledge resource: a user can trace the lineage of any tissue back to the egg, study the changes in cell phenotype that occur as a tissue develops, and use the structure to add further (e.g. molecular) information. The ontology may be downloaded from www.obofoundry.org. Queries and corrections should be sent to j.bard@ed.ac.uk.

Joint stage recognition and anatomical annotation of drosophila gene expression patterns

Motivation: Staining the mRNA of a gene via in situ hybridization (ISH) during the development of a Drosophila melanogaster embryo delivers the detailed spatio-temporal patterns of the gene expression. Many related biological problems such as the detection of co-expressed genes, co-regulated genes and transcription factor binding motifs rely heavily on the analysis of these image patterns. To provide the text-based pattern searching for facilitating related biological studies, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with developmental stage term and anatomical ontology terms manually by domain experts. Due to the rapid increase in the number of such images and the inevitable bias annotations by human curators, it is necessary to develop an automatic method to recognize the developmental stage and annotate anatomical terms.Results: In this article, we propose a novel computational model for jointly stage classification and anatomical terms annotation of Drosophila gene expression patterns. We propose a novel Tri-Relational Graph (TG) model that comprises the data graph, anatomical term graph, developmental stage term graph, and connect them by two additional graphs induced from stage or annotation label assignments. Upon the TG model, we introduce a Preferential Random Walk (PRW) method to jointly recognize developmental stage and annotate anatomical terms by utilizing the interrelations between two tasks. The experimental results on two refined BDGP datasets demonstrate that our joint learning method can achieve superior prediction results on both tasks than the state-of-the-art methods.Availability: http://ranger.uta.edu/%7eheng/Drosophila/Contact: heng@uta.edu