The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics.

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

JaponicusDB: rapid deployment of a model organism database for an emerging model species.

The fission yeast Schizosaccharomyces japonicus has recently emerged as a powerful system for studying the evolution of essential cellular processes, drawing on similarities as well as key differences between S. japonicus and the related, well-established model Schizosaccharomyces pombe. We have deployed the open-source, modular code and tools originally developed for PomBase, the S. pombe model organism database (MOD), to create JaponicusDB (www.japonicusdb.org), a new MOD dedicated to S. japonicus. By providing a central resource with ready access to a growing body of experimental data, ontology-based curation, seamless browsing and querying, and the ability to integrate new data with existing knowledge, JaponicusDB supports fission yeast biologists to a far greater extent than any other source of S. japonicus data. JaponicusDB thus enables S. japonicus researchers to realize the full potential of studying a newly emerging model species and illustrates the widely applicable power and utility of harnessing reusable PomBase code to build a comprehensive, community-maintainable repository of species-relevant knowledge.

Fission stories: using PomBase to understand Schizosaccharomyces pombe biology.

PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualization, PomBase also drives innovation in the MOD community.

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications.

Maximizing the impact and value of scientific research requires efficient knowledge distribution, which increasingly depends on the integration of standardized published data into online databases. To make data integration more comprehensive and efficient for fission yeast research, PomBase has pioneered a community curation effort that engages publication authors directly in FAIR-sharing of data representing detailed biological knowledge from hypothesis-driven experiments. Canto, an intuitive online curation tool that enables biologists to describe their detailed functional data using shared ontologies, forms the core of PomBase's system. With 8 years' experience, and as the author response rate reaches 50%, we review community curation progress and the insights we have gained from the project. We highlight incentives and nudges we deploy to maximize participation, and summarize project outcomes, which include increased knowledge integration and dissemination as well as the unanticipated added value arising from co-curation by publication authors and professional curators.

Hidden in plain sight: what remains to be discovered in the eukaryotic proteome?

The first decade of genome sequencing stimulated an explosion in the characterization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model organisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes (BP). To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences. We use a simple yet powerful metric based on Gene Ontology BP terms to define characterized and uncharacterized proteins for human, budding yeast and fission yeast. We then identify a set of conserved but unstudied proteins in S. pombe, and classify them based on a combination of orthogonal attributes determined by large-scale experimental and comparative methods. Finally, we explore possible reasons why these proteins remain neglected, and propose courses of action to raise their profile and thereby reap the benefits of completing the catalogue of proteins' biological roles.

Annotation of gene product function from high-throughput studies using the Gene Ontology.

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information.

PomBase (www.pombase.org), the model organism database for the fission yeast Schizosaccharomyces pombe, has undergone a complete redevelopment, resulting in a more fully integrated, better-performing service. The new infrastructure supports daily data updates as well as fast, efficient querying and smoother navigation within and between pages. New pages for publications and genotypes provide routes to all data curated from a single source and to all phenotypes associated with a specific genotype, respectively. For ontology-based annotations, improved displays balance comprehensive data coverage with ease of use. The default view now uses ontology structure to provide a concise, non-redundant summary that can be expanded to reveal underlying details and metadata. The phenotype annotation display also offers filtering options to allow users to focus on specific areas of interest. An instance of the JBrowse genome browser has been integrated, facilitating loading of and intuitive access to, genome-scale datasets. Taken together, the new data and pages, along with improvements in annotation display and querying, allow users to probe connections among different types of data to form a comprehensive view of fission yeast biology. The new PomBase implementation also provides a rich set of modular, reusable tools that can be deployed to create new, or enhance existing, organism-specific databases.

PomBase: The Scientific Resource for Fission Yeast.

The fission yeast Schizosaccharomyces pombe has become well established as a model species for studying conserved cell-level biological processes, especially the mechanics and regulation of cell division. PomBase integrates the S. pombe genome sequence with traditional genetic, molecular, and cell biological experimental data as well as the growing body of large datasets generated by emerging high-throughput methods. This chapter provides insight into the curation philosophy and data organization at PomBase, and provides a guide to using PomBase for infrequent visitors and anyone considering exploring S. pombe in their research.

Model organism databases: essential resources that need the support of both funders and users.

Modern biomedical research depends critically on access to databases that house and disseminate genetic, genomic, molecular, and cell biological knowledge. Even as the explosion of available genome sequences and associated genome-scale data continues apace, the sustainability of professionally maintained biological databases is under threat due to policy changes by major funding agencies. Here, we focus on model organism databases to demonstrate the myriad ways in which biological databases not only act as repositories but actively facilitate advances in research. We present data that show that reducing financial support to model organism databases could prove to be not just scientifically, but also economically, unsound.