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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.]

Biodiversity image repositories are crucial sources of training data for machine learning approaches to biological research. Metadata, specifically metadata about object quality, is putatively an important prerequisite to selecting sample subsets for these experiments. This study demonstrates the importance ofimage quality metadatato a species classification experiment involving a corpus of 1935 fish specimen images which were annotated with 22 metadata quality properties. A small subset of high quality images produced an F1 accuracy of 0.41 compared to 0.35 for a taxonomically matched subset of low quality images when used by a convolutional neural network approach to species identification. Using the full corpus of images revealed that image quality differed between correctly classified and misclassified images. We found the visibility of all anatomical features was the most important quality feature for classification accuracy. We suggest biodiversity image repositories consider adopting a minimal set of image quality metadata to support future machine learning projects.

Species classification is an important task that is the foundation of industrial, commercial, ecological, and scientific applications involving the study of species distributions, dynamics, and evolution.While conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes.In this work, we propose a new approach for species classification termed Hierarchy-Guided Neural Network (HGNN), which infuses hierarchical taxonomic information into the neural network’s training to guide the structure and relationships among the extracted features. We perform extensive experiments on an illustrative use-case of classifying fish species to demonstrate that HGNN outperforms conventional ConvNet models in terms of classification accuracy, especially under scarce training data conditions.We also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied.

The study of how the observable features of organisms, i.e., their phenotypes, result from the complex interplay between genetics, development, and the environment, is central to much research in biology. The varied language used in the description of phenotypes, however, impedes the large scale and interdisciplinary analysis of phenotypes by computational methods. The Phenoscape project (www.phenoscape.org) has developed semantic annotation tools and a gene–phenotype knowledgebase, the Phenoscape KB, that uses machine reasoning to connect evolutionary phenotypes from the comparative literature to mutant phenotypes from model organisms. The semantically annotated data enables the linking of novel species phenotypes with candidate genes that may underlie them. Semantic annotation of evolutionary phenotypes further enables previously difficult or novel analyses of comparative anatomy and evolution. These include generating large, synthetic character matrices of presence/absence phenotypes based on inference, and searching for taxa and genes with similar variation profiles using semantic similarity. Phenoscape is further extending these tools to enable users to automatically generate synthetic supermatrices for diverse character types, and use the domain knowledge encoded in ontologies for evolutionary trait analysis. Curating the annotated phenotypes necessary for this research requires significant human curator effort, although semi-automated natural language processing tools promise to expedite the curation of free text. As semantic tools and methods are developed for the biodiversity sciences, new insights from the increasingly connected stores of interoperable phenotypic and genetic data are anticipated. We thank all participants in the Phenotype Ontology Research Coordination Network (RCN) (NSF 0956049) for their vision, contributions, and commitment to developing shared and interoperable resources. During the course of this work the Phenoscape project has been supported by NSF awards 1062404, 1062542, 0641025, 1661529, and the National Evolutionary Synthesis Center (NSF 0905606 and 0423641). This manuscript is based in part on work done by P.M.M. while serving at the U.S. National Science Foundation. The views expressed in this paper do not necessarily reflect those of the National Science Foundation or the United States 314 Government.

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 to mutant 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.

© 2016 The Author(s). Background: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. Construction and content: Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. Utility and discussion: The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. Conclusions: The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments. Fil: Thessen, Anne E.. Ronin Institute for Independent Scholarship; Estados Unidos Fil: Bunker, Daniel E.. New Jersey Institute of Technology; Estados Unidos Fil: Buttigieg, Pier Luigi. Helmholtz-Zentrum für Polar-und Meeresforschung; Alemania Fil: Cooper, Laurel D.. State University of Oregon; Estados Unidos Fil: Dahdul, Wasila M.. University of South Dakota; Estados Unidos Fil: Domisch, Sami. University of Yale; Estados Unidos Fil: Franz, Nico M.. Arizona State University; Estados Unidos Fil: Jaiswal, Pankaj. State University of Oregon; Estados Unidos Fil: Lawrence-Dill, Carolyn J.. University of Iowa; Estados Unidos Fil: Midford, Peter E.. No especifica; Fil: Mungall, Christopher J.. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentina Fil: Specht, Chelsea D.. University of California at Berkeley; Estados Unidos Fil: Vogt, Lars. Universitat Bonn; Alemania Fil: Vos, Rutger Aldo. Naturalis Biodiversity Center; Países Bajos Fil: Walls, Ramona L.. Arizona State University; Estados Unidos Fil: White, Jeffrey W.. United States Department of Agriculture; Estados Unidos Fil: Zhang, Guanyang. Arizona State University; Estados Unidos Fil: Deans, Andrew R.. State University of Pennsylvania; Estados Unidos Fil: Huala, Eva. Phoenix Bioinformatics; Estados Unidos Fil: Lewis, Suzanna E.. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Mabee, Paula M.. University of South Dakota; Estados Unidos

The diverse phenotypes of living organisms have been described for centuries, and though they may be digitized, they are not readily available in a computable form. Using over 100 morphological studies, the Phenoscape project has demonstrated that by annotating characters with community ontology terms, links between novel species anatomy and the genes that may underlie them can be made. But given the enormity of the legacy literature, how can this largely unexploited wealth of descriptive data be rendered amenable to large-scale computation? To identify the bottlenecks, we quantified the time involved in the major aspects of phenotype curation as we annotated characters from the vertebrate phylogenetic systematics literature. This involves attaching fully computable logical expressions consisting of ontology terms to the descriptions in character-by-taxon matrices. The workflow consists of: (i) data preparation, (ii) phenotype annotation, (iii) ontology development and (iv) curation team discussions and software development feedback. Our results showed that the completion of this work required two person-years by a team of two post-docs, a lead data curator, and students. Manual data preparation required close to 13% of the effort. This part in particular could be reduced substantially with better community data practices, such as depositing fully populated matrices in public repositories. Phenotype annotation required ∼40% of the effort. We are working to make this more efficient with Natural Language Processing tools. Ontology development (40%), however, remains a highly manual task requiring domain (anatomical) expertise and use of specialized software. The large overhead required for data preparation and ontology development contributed to a low annotation rate of approximately two characters per hour, compared with 14 characters per hour when activity was restricted to character annotation. Unlocking the potential of the vast stores of morphological descriptions requires better tools for efficiently processing natural language, and better community practices towards a born-digital morphology. Database URL: http://kb.phenoscape.org

Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits., Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.