ASIH09 workshop

Workshop on "Ontologies for Ichthyology and Herpetology"

Held in conjunction with the 2009 ASIH meetings, Portland Oregon, July 2009

Abstracts

A Gentle Introduction to Ontologies for Biology

Hilmar Lapp (1), Todd J. Vision (1,2)

1. US National Evolutionary Synthesis Center
2. University of North Carolina at Chapel Hill

As biology has become increasingly data-rich, the reliance on databases and computation to integrate and mine the vast body of knowledge traditionally reported in the literature has grown dramatically. This has inspired the development of computational technologies to allow the exploration and linking of diverse types of data across biological databases at unprecedented scales. One of the most important technologies is an “ontology”, a hierarchically structured, controlled vocabulary of well-defined terms and the logical relationships that hold between them. Ontologies have been applied with tremendous success to transform scientific results traditionally reported as free text into a digital representation that is unambiguous, uniform across disciplines, and readily computable. For example, ontologies of biochemical functions and biological processes are used to unambiguously record what is known about a gene's function. Anatomy ontologies are now being used to unequivocally describe the morphological characteristics visible in a specimen image. Efforts (including two in ichthyology and herpetology) are underway to develop ontologies that will ultimately span the breadth of biological knowledge, which will have a profound impact on the way that biologists interact with data collections in the future. In order to be useful community resources, ontologies must accurately capture the state of biological knowledge, which requires that the biological community through their experts play an active role in their development. Here, I will provide a beginner’s guide to the world of ontologies and offer a roadmap for how biologists can best take advantage of, and contribute to, the growing suite of bio-ontology resources.

Linking animal models and human diseases

Monte Westerfield

Director, Zebrafish Information Network and Institute of Neuroscience, University of Oregon

Phenotypes are the result of interactions of the whole genome with the environment. Studies that correlate genotype with phenotype are crucial for unraveling biological pathways and gene product interactions and, hence, are required for reaching the long-term goal of understanding how genes regulate developmental and physiological processes. Together with the NCBO and FlyBase we developed a bipartite “EQ” (Entity + Quality) syntax to describe phenotypes. The Entity is the part of the phenotype being described, the Quality describes how the entity is affected. The entities may be terms from anatomical ontologies or the Gene Ontology (GO; for biological processes, cellular components, and molecular functions). The Quality terms come from the Phenotype and Trait Ontology (PATO) that provides a hierarchy of qualitative or quantitative qualities that may be applied to an observable structure or process. We used EQ syntax and ontologies to annotate human disease genes (OMIM), and their Drosophila and Zebrafish homologs. We show that these data can be comparatively queried by phenotype alone, using an information content-based similarity search algorithm. To test whether usage of EQ syntax and the PATO ontology is sufficiently reproducible for annotating phenotypes, three curators independently annotated the same records. Differences in the annotations recorded by the three curators may arise from deficiencies in PATO, the anatomy or Gene ontologies, or the syntax itself. A comparison of these annotations allows testing and development of curatorial standards for phenotype annotation.

Phenoscape: Using Ontologies to Link Comparative Morphology to Genes

Paula Mabee

University of South Dakota

[final version?] Decades of comparative anatomical studies in ichthyology and herpetology have resulted in a rich body of ‘free-text’ data. As these data grow, they are increasingly hard to align and synthesize across taxonomic groups, and synthetic questions concerning the developmental and genetic basis of evolutionary changes in morphology cannot be easily or efficiently addressed. In order for this volume of comparative anatomical data to be analyzed in a developmental genetic context, it must first be rendered computable. One way to achieve this is to use ontologies. Using ostariophysan fishes as a prototype, the Phenoscape project has developed a system that includes ontologies representing expert knowledge of anatomy and taxonomy (the Teleost Anatomy Ontology and the Teleost Taxonomy Ontology), software for data curation (Phenex), and a knowledgebase that supports ontology-based reasoning about evolutionary phenotype data (PhenoscapeKB, http://phenoscape.org/kb). To date, over 5,000 characters from the phylogenetic literature have been annotated for 8,300 species, resulting in over eight million annotated phenotypes. PhenoscapeKB combines these evolutionary phenotypes with information about genetically characterized phenotype from ZFIN, the zebrafish community database. Through ontology-based reasoning over expert knowledge in taxonomy, comparative anatomy and developmental genetics, PhenoscapeKB can be used to address a host of questions spanning the domains of genetics, development and evolutionary biology, such as the nature of the genetic changes underlying phenotypic variation among taxa in nature.

Development of an Anatomical Ontology for Amphibians

Anne Maglia (1), Jennifer Leopold (1), Susan Gauch (2), Analia Pugener (1)

1. Missouri University of Science and Technology, Rolla, MO, United States
2. University of Arkansas, Fayetteville, AR, United States

Herein, we describe our ongoing efforts to develop a robust ontology for amphibian anatomy (www.amphibanat.org) that accommodates the diversity of anatomical structures present in the group. We discuss the design and implementation of the project, current resolutions to issues we have encountered, and future enhancements to the ontology. We also comment on efforts to integrate other data sets with the amphibian anatomical ontology.

Illustrating Ontologies with Morphbank and Phenoscape

Greg Riccardi (1) and Austin Mast (2)

1. College of Information, Florida State University
2. Department of Biological Science, Florida State University, amast@bio.fsu.edu

This talk will discuss some ways that image management and annotation, ontology development, and studies of physical features of organisms are integrated to facilitate research in evolution and development. The management of metadata and annotations of images is a primary capability of the Morphbank system, an on-line image repository system that has over 250,000 images of a variety of organisms. The metadata in Morphbank for an image includes information about the content of the image—that is, characteristics of the objects shown in the image. This includes information about the specimen with Darwin Core fields and information about the anatomy and views that are presented. Morphbank and Phenoscape have combined to integrate anatomical and phenotype annotations on images. An image of a portion of a fish skeleton in Morphbank includes references to the Teleost ontology terms that describe that bone (c. f., http://www.morphbank.net?id=459110). 1500 skeletal images from CToL are currently in Morphbank. A Search of Morphbank for the term will produce a collection of relevant images. An annotation of an image in Morphbank attaches one or more ontology terms to a specific part of the image. The integration of Morphbank with the Morphster ontology browser [ref] provides an illustration of ontology terms. A user may select a term in Morphster and see annotated Morphbank images that are relevant to that term. The inference capabilities of Morphster allow a user to find all images of “bone” or just those of “ceratobranchial” or those that exhibit a specific phenotype.

Bgee: Integrating ontology and homology for the study of gene expression evolution

Marc Robinson-Rechavi

Department of Ecology and Evolution, Biophore, Lausanne University and Swiss Institute of Bioinformatics

The study of the evolution of developmental processes (evo-devo) has shown that the primary source of change in the evolution of phenotypes is changes in gene expression. Comparing gene expression patterns between animals thus is a major step to understand their evolution. This approach requires dedicated bioinformatics tools to perform high throughput analyses. Thus we have developed Bgee, a database designed to compare expression patterns between animals, by implementing ontologies describing anatomies and developmental stages of species, and then designing homology relationships between anatomies and comparison criteria between developmental stages. To define homology relationships between anatomical features we have developed the software Homolonto, which uses a modified ontology alignment approach to propose homology relationships between ontologies. Bgee then uses these aligned ontologies, onto which heterogeneous expression data types are mapped. These include microarrays, in situ hybridization, and ESTs, from human, mouse, xenopus and zebrafish. Bgee is available at http://bgee.unil.ch/

Vize title TBA

Peter Vize

University of Calgary