Search

Background: Taxonomy has long struggled with analysing vast amounts of phenotypic data due to computational and accessibility challenges. Ontology-based technologies provide a framework for modelling semantic phenotypes that are understandable by computers and compliant with FAIR principles. In this paper, we explore the use of Phenoscript, an emerging language designed for creating semantic phenotypes, to produce computable species descriptions. Our case study centers on the application of this approach to dung beetles (Coleoptera, Scarabaeinae). New information: We illustrate the effectiveness of Phenoscript for creating semantic phenotypes. We also demonstrate the ability of the Phenospy python package to automatically translate Phenoscript descriptions into natural language (NL), which eliminates the need for writing traditional NL descriptions. We introduce a computational pipeline that streamlines the generation of semantic descriptions and their conversion to NL. To demonstrate the power of the semantic approach, we apply simple semantic queries to the generated phenotypic descriptions. This paper addresses the current challenges in crafting semantic species descriptions and outlines the path towards future improvements. Furthermore, we discuss the promising integration of semantic phenotypes and nanopublications, as emerging methods for sharing scientific information. Overall, our study highlights the pivotal role of ontology-based technologies in modernising taxonomy and aligning it with the evolving landscape of big data analysis and FAIR principles. [ABSTRACT FROM AUTHOR]

BackgroundEfforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results. ObjectiveWe aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs. MethodsWe developed a biomedical query language and web application interphase—termed as Translator Query Language (TranQL)—to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph. ResultsWe developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as “gene,” “chemical substance,” and “disease,” that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated. ConclusionsTranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

Organismal anatomy is a hierarchical system of anatomical entities often imposing dependencies among multiple morphological characters. Ontologies provide a formal and computable framework for incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. They also offer new opportunities for working with semantic representations of morphological data.In this work, we present a new R package—rphenoscate—that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction with rphenoscape, it allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the package functionality with data sets from bees and fishes.We demonstrate that ontologies can be employed to automatically set up evolutionary models accounting for trait dependencies in stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information from their original data sets.Ontologies will become important tools for integrating anatomical knowledge into phylogenetic methods and making morphological data FAIR compliant—a critical step of the ongoing 'phenomics' revolution. Our new package offers key advancements towards this goal. [ABSTRACT FROM AUTHOR]

Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent "parts", but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies—structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here, we assess whether evolutionary patterns can explain the proximity of ontology-annotated characters within an ontology. To do so, we measure phylogenetic information across characters and evaluate if it matches the hierarchical structure given by ontological knowledge—in much the same way as across-species diversity structure is given by phylogeny. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to data sets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially explained by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher-level subsets in the ontology. We further applied our approach to address particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that phylogenetic information does match ontology structure for some anatomical entities, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological data sets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may play a role in explaining it: phylogeny, development, or convergence. [Apidae; Bayesian phylogenetic information; Ostariophysi; Phenoscape; phylogenetic dissonance; semantic similarity.] [ABSTRACT FROM AUTHOR]

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL : http://w3id.org/sssom/spec [ABSTRACT FROM AUTHOR]

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL : https://github.com/INCATools/ontology-development-kit [ABSTRACT FROM AUTHOR]

Motivation Gene Ontology Causal Activity Models (GO-CAMs) assemble individual associations of gene products with cellular components, molecular functions and biological processes into causally linked activity flow models. Pathway databases such as the Reactome Knowledgebase create detailed molecular process descriptions of reactions and assemble them, based on sharing of entities between individual reactions into pathway descriptions. Results To convert the rich content of Reactome into GO-CAMs, we have developed a software tool, Pathways2GO, to convert the entire set of normal human Reactome pathways into GO-CAMs. This conversion yields standard GO annotations from Reactome content and supports enhanced quality control for both Reactome and GO, yielding a nearly seamless conversion between these two resources for the bioinformatics community. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology's compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/ [ABSTRACT FROM AUTHOR]

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem. Author summary: Organism traits determine the role of species in economies and ecosystems, and the expression of those traits relies on interactions between an organism's genes and environment. The key to predicting trait expression is having a large pool of data to derive models, but most organism trait observations are recorded in ways that are not computational. In this paper, intended for an interdisciplinary audience, we discuss data models for representing organism traits in a computable format. Increasing acceptance of a data model for traits will greatly increase the pool of available data for studying the dynamic processes that determine trait expression. We hope that explaining these data models in a straightforward way and articulating their potential for accelerating discovery will increase adoption of this promising data standard. [ABSTRACT FROM AUTHOR]

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data. [ABSTRACT FROM AUTHOR]

Natural language descriptions of organismal phenotypes, a principal object of study in biology, are abundant in the biological literature. Expressing these phenotypes as logical statements using ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make these phenotype descriptions amenable to machine reasoning. Natural language processing tools have been developed to facilitate this task, and the training and evaluation of these tools depend on the availability of high quality, manually annotated gold standard data sets. We describe the development of an expert-curated gold standard data set of annotated phenotypes for evolutionary biology. The gold standard was developed for the curation of complex comparative phenotypes for the Phenoscape project. It was created by consensus among three curators and consists of entity–quality expressions of varying complexity. We use the gold standard to evaluate annotations created by human curators and those generated by the Semantic CharaParser tool. Using four annotation accuracy metrics that can account for any level of relationship between terms from two phenotype annotations, we found that machine–human consistency, or similarity, was significantly lower than inter-curator (human–human) consistency. Surprisingly, allowing curatorsaccess to external information did not significantly increase the similarity of their annotations to the gold standard or have a significant effect on inter-curator consistency. We found that the similarity of machine annotations to the gold standard increased after new relevant ontology terms had been added. Evaluation by the original authors of the character descriptions indicated that the gold standard annotations came closer to representing their intended meaning than did either the curator or machine annotations. These findings point toward ways to better design software to augment human curators and the use of the gold standard corpus will allow training and assessment of new tools to improve phenotype annotation accuracy at scale. [ABSTRACT FROM AUTHOR]

Background: Bio-ontologies typically require multiple axes of classification to support the needs of their users. Development of such ontologies can only be made scalable and sustainable by the use of inference to automate classification via consistent patterns of axiomatization. Many bio-ontologies originating in OBO or OWL follow this approach. These patterns need to be documented in a form that requires minimal expertise to understand and edit and that can be validated and applied using any of the various programmatic approaches to working with OWL ontologies. Results: Here we describe a system, Dead Simple OWL Design Patterns (DOS-DPs), which fulfills these requirements, illustrating the system with examples from the Gene Ontology. Conclusions: The rapid adoption of DOS-DPs by multiple ontology development projects illustrates both the ease-of use and the pressing need for the simple design pattern system we have developed. [ABSTRACT FROM AUTHOR]

Background: In recent years large bibliographic databases have made much of the published literature of biology available for searches. However, the capabilities of the search engines integrated into these databases for text-based bibliographic searches are limited. To enable searches that deliver the results expected by comparative anatomists, an underlying logical structure known as an ontology is required. Development and Testing of the Ontology: Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, ), a publicly-available, online repository for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). We compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. Results and Significance: Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities. [ABSTRACT FROM AUTHOR]

Phenotypes resulting from mutations in genetic model organisms can help reveal candidate genes for evolutionarily important phenotypic changes in related taxa. Although testing candidate gene hypotheses experimentally in nonmodel organisms is typically difficult, ontology-driven information systems can help generate testable hypotheses about developmental processes in experimentally tractable organisms. Here, we tested candidate gene hypotheses suggested by expert use of the Phenoscape Knowledgebase, specifically looking for genes that are candidates responsible for evolutionarily interesting phenotypes in the ostariophysan fishes that bear resemblance tomutant phenotypes in zebrafish. For this, we searched ZFIN for genetic perturbations that result in either loss of basihyal element or loss of scales phenotypes, because these are the ancestral phenotypes observed in catfishes (Siluriformes).We tested the identified candidate genes by examining their endogenous expression patterns in the channel catfish, Ictalurus punctatus. The experimental results were consistent with the hypotheses that these features evolved through disruption in developmental pathways at, or upstream of, brpf1 and eda/edar for the ancestral losses of basihyal element and scales, respectively. These results demonstrate that ontological annotations of the phenotypic effects of genetic alterations in model organisms, when aggregated within a knowledgebase, can be used effectively to generate testable, and useful, hypotheses about evolutionary changes in morphology. [ABSTRACT FROM AUTHOR]

Background Elucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent 'natural' phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes. Results Previously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued. Conclusions The newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies. The ontology release files associated with the ontology merge described in this manuscript are available at: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/ Current ontology release files are available always available at: http://purl.obolibrary.org/obo/uberon/releases/ [ABSTRACT FROM AUTHOR]

We revise two relatively rare ensign wasp genera, whose species are restricted to Sub-Saharan Africa: Afrevania and Trissevania. Afrevania longipetiolata sp. nov., Trissevania heatherae sp. nov., T. hugoi sp. nov., T. mrimaensis sp. nov. and T. slideri sp. nov. are described, males and females of T. anemotis and Afrevania leroyi are redescribed, and an identification key for Trissevaniini is provided. We argue that Trissevania mrimaensis sp. nov. and T. heatherae sp. nov. populations are vulnerable, given their limited distributions and threats from mining activities in Kenya. We hypothesize that these taxa together comprise a monophyletic lineage, Trissevaniini, tr. nov., the members of which share the ability to fold their fore wings along two intersecting fold lines. Although wing folding of this type has been described for the hind wing of some insects four-plane wing folding of the fore wing has never been documented. The wing folding mechanism and the pattern of wing folds of Trissevaniini is shared only with some cockroach species (Blattodea). It is an interesting coincidence that all evaniids are predators of cockroach eggs. The major wing fold lines of Trissevaniini likely are not homologous to any known longitudinal anatomical structures on the wings of other Evaniidae. Members of the new tribe share the presence of a coupling mechanism between the fore wing and the mesosoma that is composed of a setal patch on the mesosoma and the retinaculum of the fore wing. While the setal patch is an evolutionary novelty, the retinaculum, which originally evolved to facilitate fore and hind wing coupling in Hymenoptera, exemplifies morphological exaptation. We also refine and clarify the Semantic Phenotype approach used in previous taxonomic revisions and explore the consequences of merging new with existing data. The way that semantic statements are formulated can evolve in parallel, alongside improvements to the ontologies themselves. [ABSTRACT FROM AUTHOR]

Background A hierarchical taxonomy of organisms is a prerequisite for semantic integration of biodiversity data. Ideally, there would be a single, expansive, authoritative taxonomy that includes extinct and extant taxa, information on synonyms and common names, and monophyletic supraspecific taxa that reflect our current understanding of phylogenetic relationships. Description As a step towards development of such a resource, and to enable large-scale integration of phenotypic data across vertebrates, we created the Vertebrate Taxonomy Ontology (VTO), a semantically defined taxonomic resource derived from the integration of existing taxonomic compilations, and freely distributed under a Creative Commons Zero (CC0) public domain waiver. The VTO includes both extant and extinct vertebrates and currently contains 106,947 taxonomic terms, 22 taxonomic ranks, 104,736 synonyms, and 162,400 cross-references to other taxonomic resources. Key challenges in constructing the VTO included (1) extracting and merging names, synonyms, and identifiers from heterogeneous sources; (2) structuring hierarchies of terms based on evolutionary relationships and the principle of monophyly; and (3) automating this process as much as possible to accommodate updates in source taxonomies. Conclusions The VTO is the primary source of taxonomic information used by the Phenoscape Knowledgebase (http://phenoscape.org/), which integrates genetic and evolutionary phenotype data across both model and non-model vertebrates. The VTO is useful for inferring phenotypic changes on the vertebrate tree of life, which enables queries for candidate genes for various episodes in vertebrate evolution. [ABSTRACT FROM AUTHOR]

Background: Scientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great "Tree of Life" (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the user's needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces. Results: With the aim of building such a "phylotastic" system, the NESCent Hackathons, Interoperability, Phylogenies (HIP) working group recruited 2 dozen scientist-programmers to a weeklong programming hackathon in June 2012. During the hackathon (and a three-month follow-up period), 5 teams produced designs, implementations, documentation, presentations, and tests including: (1) a generalized scheme for integrating components; (2) proofof- concept pruners and controllers; (3) a meta-API for taxonomic name resolution services; (4) a system for storing, finding, and retrieving phylogenies using semantic web technologies for data exchange, storage, and querying; (5) an innovative new service, DateLife.org, which synthesizes pre-computed, time-calibrated phylogenies to assign ages to nodes; and (6) demonstration projects. These outcomes are accessible via a public code repository (GitHub.com), a website (www.phylotastic.org), and a server image. Conclusions: Approximately 9 person-months of effort (centered on a software development hackathon) resulted in the design and implementation of proof-of-concept software for 4 core phylotastic components, 3 controllers, and 3 end-user demonstration tools. While these products have substantial limitations, they suggest considerable potential for a distributed system that makes phylogenetic knowledge readily accessible in computable form. Widespread use of phylotastic systems will create an electronic marketplace for sharing phylogenetic knowledge that will spur innovation in other areas of the ToL enterprise, such as annotation of sources and methods and third-party methods of quality assessment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 E. rafaeli Kawada, sp. n. from Brazil. Evaniscus sulcigenis Roman, syn. n., is synonymized under E. 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 E. tibialis and E. 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. [ABSTRACT FROM AUTHOR]

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 resources 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. [ABSTRACT FROM AUTHOR]

Background: The wealth of phenotypic descriptions documented in the published articles, monographs, and dissertations of phylogenetic systematics is traditionally reported in a free-text format, and it is therefore largely inaccessible for linkage to biological databases for genetics, development, and phenotypes, and difficult to manage for large-scale integrative work. The Phenoscape project aims to represent these complex and detailed descriptions with rich and formal semantics that are amenable to computation and integration with phenotype data from other fields of biology. This entails reconceptualizing the traditional free-text characters into the computable Entity-Quality (EQ) formalism using ontologies. Methodology/Principal Findings: We used ontologies and the EQ formalism to curate a collection of 47 phylogenetic studies on ostariophysan fishes (including catfishes, characins, minnows, knifefishes) and their relatives with the goal of integrating these complex phenotype descriptions with information from an existing model organism database (zebrafish, http://zfin.org). We developed a curation workflow for the collection of character, taxonomic and specimen data from these publications. A total of 4,617 phenotypic characters (10,512 states) for 3,449 taxa, primarily species, were curated into EQ formalism (for a total of 12,861 EQ statements) using anatomical and taxonomic terms from teleost-specific ontologies (Teleost Anatomy Ontology and Teleost Taxonomy Ontology) in combination with terms from a quality ontology (Phenotype and Trait Ontology). Standards and guidelines for consistently and accurately representing phenotypes were developed in response to the challenges that were evident from two annotation experiments and from feedback from curators. Conclusions/Significance: The challenges we encountered and many of the curation standards and methods for improving consistency that we developed are generally applicable to any effort to represent phenotypes using ontologies. This is because an ontological representation of the detailed variations in phenotype, whether between mutant or wildtype, among individual humans, or across the diversity of species, requires a process by which a precise combination of terms from domain ontologies are selected and organized according to logical relations. The efficiencies that we have developed in this process will be useful for any attempt to annotate complex phenotypic descriptions using ontologies. We also discuss some ramifications of EQ representation for the domain of systematics. [ABSTRACT FROM AUTHOR]

Background: Phenotypic differences among species have long been systematically itemized and described by biologists in the process of investigating phylogenetic relationships and trait evolution. Traditionally, these descriptions have been expressed in natural language within the context of individual journal publications or monographs. As such, this rich store of phenotype data has been largely unavailable for statistical and computational comparisons across studies or integration with other biological knowledge. Methodology/Principal Findings: Here we describe Phenex, a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic similarities and differences using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Phenex can be configured to load only those ontologies pertinent to a taxonomic group of interest. The graphical user interface was optimized for evolutionary biologists accustomed to working with lists of taxa, characters, character states, and characterby- taxon matrices. Conclusions/Significance: Annotation of phenotypic data using ontologies and globally unique taxonomic identifiers will allow biologists to integrate phenotypic data from different organisms and studies, leveraging decades of work in systematics and comparative morphology. [ABSTRACT FROM AUTHOR]

The evolutionary mechanisms that operate on genetic variation within transcriptional regulatory sequences are not well under- stood. We present here an evolutionary analysis of an exceptionally well characterized cis-regulatory region, the endo 16 promoter of the purple sea urchin. Segregating variation reveals striking differences in the intensity of negative selection among regulatory modules, reflecting their distinct functional roles. Surprisingly, transcription-factor-binding sites are as polymorphic and as likely to contain fixed differences as flanking nucleotides. Whereas nucleotides in protein-binding sites in the most proximal regulatory module exhibit reduced variation, those in other modules tend to be more polymorphic than putatively nonfunctional nucleotides. Two unrelated large insertions at the same position within the promoter are segregating at low frequencies; one is a strong ectodermal repressor that contains 16 verified transcription-factor- binding sites. These results demonstrate that a simple relationship between conservation and function does not exist within this cis-regulatory region and highlight significant population heterogeneity in the fine structure of a well understood promoter. [ABSTRACT FROM AUTHOR]

We revise two relatively rare ensign wasp genera, whose species are restricted to Sub-Saharan Africa: Afrevania and Trissevania. Afrevania longipetiolata sp. nov., Trissevania heatherae sp. nov., T. hugoi sp. nov., T. mrimaensis sp. nov. and T. slideri sp. nov. are described, males and females of T. anemotis and Afrevania leroyi are redescribed, and an identification key for Trissevaniini is provided. We argue that Trissevania mrimaensis sp. nov. and T. heatherae sp. nov. populations are vulnerable, given their limited distributions and threats from mining activities in Kenya. We hypothesize that these taxa together comprise a monophyletic lineage, Trissevaniini, tr. nov., the members of which share the ability to fold their fore wings along two intersecting fold lines. Although wing folding of this type has been described for the hind wing of some insects four-plane wing folding of the fore wing has never been documented. The wing folding mechanism and the pattern of wing folds of Trissevaniini is shared only with some cockroach species (Blattodea). It is an interesting coincidence that all evaniids are predators of cockroach eggs. The major wing fold lines of Trissevaniini likely are not homologous to any known longitudinal anatomical structures on the wings of other Evaniidae. Members of the new tribe share the presence of a coupling mechanism between the fore wing and the mesosoma that is composed of a setal patch on the mesosoma and the retinaculum of the fore wing. While the setal patch is an evolutionary novelty, the retinaculum, which originally evolved to facilitate fore and hind wing coupling in Hymenoptera, exemplifies morphological exaptation. We also refine and clarify the Semantic Phenotype approach used in previous taxonomic revisions and explore the consequences of merging new with existing data. The way that semantic statements are formulated can evolve in parallel, alongside improvements to the ontologies themselves. [ABSTRACT FROM AUTHOR]

The principles of genetics apply across the entire tree of life. At the cellular level we share biological mechanisms with species from which we diverged millions, even billions of years ago. We can exploit this common ancestry to learn about health and disease, by analyzing DNA and protein sequences, but also through the observable outcomes of genetic differences, i.e. phenotypes. To solve challenging disease problems we need to unify the heterogeneous data that relates genomics to disease traits. Without a big-picture view of phenotypic data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) provides tools for genotype-phenotype analysis, genomic diagnostics, and precision medicine across broad areas of disease. [ABSTRACT FROM AUTHOR]

Lemurs and the other strepsirrhine primates are of great interest to the primate genomics community due to their phylogenetic placement as the sister lineage to all other primates. Previous attempts to resolve the phylogeny of lemurs employed limited mitochondrial or small nuclear data sets, with many relationships poorly supported or entirely unresolved. We used genomic resources to develop 11 novel markers from nine chromosomes, representing ~9 kb of nuclear sequence data. In combination with previously published nuclear and mitochondrial loci, this yields a data set of more than 16 kb and adds ~275 kb of DNA sequence to current databases. Our phylogenetic analyses confirm hypotheses of lemuriform monophyly and provide robust resolution of the phylogenetic relationships among the five lemuriform families. We verify that the genus Daubentonia is the sister lineage to all other lemurs. The Cheirogaleidae and Lepilemuridae are sister taxa and together form the sister lineage to the Indriidae; this clade is the sister lineage to the Lemuridae. Divergence time estimates indicate that lemurs are an ancient group, with their initial diversification occurring around the Cretaceous-Tertiary boundary. Given the power of this data set to resolve branches in a notoriously problematic area of primate phylogeny, we anticipate that our phylogenomic toolkit will be of value to other studies of primate phylogeny and diversification. Moreover, the methods applied will be broadly applicable to other taxonomic groups where phylogenetic relationships have been notoriously difficult to resolve. [ABSTRACT FROM AUTHOR]