The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

The Zebrafish Information Network: major gene page and home page updates.

The Zebrafish Information Network (ZFIN) (https://zfin.org/) is the database for the model organism, zebrafish (Danio rerio). ZFIN expertly curates, organizes, and provides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines, gene expression, gene function, mutant phenotypes, orthology, human disease models, gene and mutant nomenclature, and reagents. New features at ZFIN include major updates to the home page and the gene page, the two most used pages at ZFIN. Data including disease models, phenotypes, expression, mutants and gene function continue to be contributed to The Alliance of Genome Resources for integration with similar data from other model organisms.

Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology.

Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care.

The Zebrafish Information Network: new support for non-coding genes, richer Gene Ontology annotations and the Alliance of Genome Resources.

The Zebrafish Information Network (ZFIN) (https://zfin.org/) is the database for the model organism, zebrafish (Danio rerio). ZFIN expertly curates, organizes and provides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines, gene expression, gene function, mutant phenotypes, orthology, human disease models, nomenclature and reagents. New features at ZFIN include increased support for genomic regions and for non-coding genes, and support for more expressive Gene Ontology annotations. ZFIN has recently taken over maintenance of the zebrafish reference genome sequence as part of the Genome Reference Consortium. ZFIN is also a founding member of the Alliance of Genome Resources, a collaboration of six model organism databases (MODs) and the Gene Ontology Consortium (GO). The recently launched Alliance portal (https://alliancegenome.org) provides a unified, comparative view of MOD, GO, and human data, and facilitates foundational and translational biomedical research.

The Zebrafish Model Organism Database: new support for human disease models, mutation details, gene expression phenotypes and searching.

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for zebrafish (Danio rerio) genetic, genomic, phenotypic and developmental data. ZFIN curators provide expert manual curation and integration of comprehensive data involving zebrafish genes, mutants, transgenic constructs and lines, phenotypes, genotypes, gene expressions, morpholinos, TALENs, CRISPRs, antibodies, anatomical structures, models of human disease and publications. We integrate curated, directly submitted, and collaboratively generated data, making these available to zebrafish research community. Among the vertebrate model organisms, zebrafish are superbly suited for rapid generation of sequence-targeted mutant lines, characterization of phenotypes including gene expression patterns, and generation of human disease models. The recent rapid adoption of zebrafish as human disease models is making management of these data particularly important to both the research and clinical communities. Here, we describe recent enhancements to ZFIN including use of the zebrafish experimental conditions ontology, 'Fish' records in the ZFIN database, support for gene expression phenotypes, models of human disease, mutation details at the DNA, RNA and protein levels, and updates to the ZFIN single box search.

ZFIN, The zebrafish model organism database: Updates and new directions.

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for genetic and genomic data from zebrafish (Danio rerio) research. ZFIN staff curate detailed information about genes, mutants, genotypes, reporter lines, sequences, constructs, antibodies, knockdown reagents, expression patterns, phenotypes, gene product function, and orthology from publications. Researchers can submit mutant, transgenic, expression, and phenotype data directly to ZFIN and use the ZFIN Community Wiki to share antibody and protocol information. Data can be accessed through topic-specific searches, a new site-wide search, and the data-mining resource ZebrafishMine (http://zebrafishmine.org). Data download and web service options are also available. ZFIN collaborates with major bioinformatics organizations to verify and integrate genomic sequence data, provide nomenclature support, establish reciprocal links, and participate in the development of standardized structured vocabularies (ontologies) used for data annotation and searching. ZFIN-curated gene, function, expression, and phenotype data are available for comparative exploration at several multi-species resources. The use of zebrafish as a model for human disease is increasing. ZFIN is supporting this growing area with three major projects: adding easy access to computed orthology data from gene pages, curating details of the gene expression pattern changes in mutant fish, and curating zebrafish models of human diseases.

The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization.

Background ­: Limited universally adopted data standards in veterinary science hinders data interoperability and therefore integration and comparison; this ultimately impedes application of existing information-based tools to support advancement in veterinary diagnostics, treatments, and precision medicine. Hypothesis/Objectives ­: Creation of a Vertebrate Breed Ontology (VBO) as a single, coherent logic-based standard for documenting breed names in animal health, production and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals ­: No live animals were used in this study. Methods ­: A list of breed names and related information was compiled from relevant sources, organizations, communities, and experts using manual and computational approaches to create VBO. Each breed is represented by a VBO term that includes all provenance and the breed's related information as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results ­: VBO is an open, community-driven ontology representing over 19,000 livestock and companion animal breeds covering 41 species. Breeds are classified based on community and expert conventions (e.g., horse breed, cattle breed). This classification is supported by relations to the breeds' genus and species indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g. relating breeds to their foundation stock, provide additional context to support advanced data analytics. VBO term metadata includes common names and synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance ­: Veterinary data interoperability and computability can be enhanced by the adoption of VBO as a source of standard breed names in databases and veterinary electronic health records.

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare Disease.

Structured representations of clinical data can support computational analysis of individuals and cohorts, and ontologies representing disease entities and phenotypic abnormalities are now commonly used for translational research. The Medical Action Ontology (MAxO) provides a computational representation of treatments and other actions taken for the clinical management of patients. Currently, manual biocuration is used to assign MAxO terms to rare diseases, enabling clinical management of rare diseases to be described computationally for use in clinical decision support and mechanism discovery. However, it is challenging to scale manual curation to comprehensively capture information about medical actions for the more than 10,000 rare diseases. We present AutoMAxO, a semi-automated workflow that leverages Large Language Models (LLMs) to streamline MAxO biocuration for rare diseases. AutoMAxO first uses LLMs to retrieve candidate curations from abstracts of relevant publications. Next, the candidate curations are matched to ontology terms from MAxO, Human Phenotype Ontology (HPO), and MONDO disease ontology via a combination of LLMs and post-processing techniques. Finally, the matched terms are presented in a structured form to a human curator for approval. We used this approach to process 4,918 unique medical abstracts and identified annotations for 21 rare genetic diseases, we extracted 18,631 candidate disease-treatment curations, 538 of which were confirmed and transferred to the MAxO annotation dataset. The results of this project underscore the potential of generative AI to accelerate precision medicine by enabling a robust and comprehensive curation of the primary literature to represent information about diseases and procedures in a structured fashion. Although we focused on MAxO in this project, similar approaches could be taken for other biomedical curation tasks.

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI).

BACKGROUND: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources. RESULTS: We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues. CONCLUSIONS: These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

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.

Pineal-specific agouti protein regulates teleost background adaptation.

Background adaptation is used by teleosts as one of a variety of camouflage mechanisms for avoidance of predation. Background adaptation is known to involve light sensing by the retina and subsequent regulation of melanophore dispersion or contraction in melanocytes, mediated by α-melanocyte-stimulating hormone and melanin-concentrating hormone, respectively. Here, we demonstrate that an agouti gene unique to teleosts, agrp2, is specifically expressed in the pineal and is required for up-regulation of hypothalamic pmch and pmchl mRNA and melanosome contraction in dermal melanocytes in response to a white background. floating head, a mutant with defective pineal development, exhibits defective up-regulation of mch mRNAs by white background, whereas nrc, a blind mutant, exhibits a normal response. These studies identify a role for the pineal in background adaptation in teleosts, a unique physiological function for the agouti family of proteins, and define a neuroendocrine axis by which environmental background regulates pigmentation.

The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human Disease.

Navigating the vast landscape of clinical literature to find optimal treatments and management strategies can be a challenging task, especially for rare diseases. To address this task, we introduce the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes. MAxO was developed with logical structures that make it compatible with several other ontologies within the Open Biological and Biomedical Ontologies (OBO) Foundry. These cover a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. We have created a database of over 16000 annotations that describe diagnostic modalities for specific phenotypic abnormalities as defined by the Human Phenotype Ontology (HPO). Additionally, 413 annotations are provided for medical actions for 189 rare diseases. We have developed a web application called POET (https://poet.jax.org/) for the community to use to contribute MAxO annotations. MAxO provides a computational representation of treatments and other actions taken for the clinical management of patients. The development of MAxO is closely coupled to the Mondo Disease Ontology (Mondo) and the Human Phenotype Ontology (HPO) and expands the scope of our computational modeling of diseases and phenotypic features to include diagnostics and therapeutic actions. MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease.

BACKGROUND: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions. METHODS: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology. FINDINGS: MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases. CONCLUSIONS: MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO). FUNDING: NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.

Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies.

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.

Identification of differentially expressed genes in the zebrafish hypothalamic-pituitary axis.

The vertebrate hypothalamic-pituitary axis (HP) is the main link between the central nervous system and endocrine system. Although several signal pathways and regulatory genes have been implicated in adenohypophysis ontogenesis, little is known about hypothalamic-neurohypophysial development or when the HP matures and becomes functional. To identify markers of the HP, we constructed subtractive cDNA libraries between adult zebrafish hypothalamus and pituitary. We identified previously published genes, ESTs and novel zebrafish genes, some of which were predicted by genomic database analysis. We also analyzed expression patterns of these genes and found that several are expressed in the embryonic and larval hypothalamus, neurohypophysis, and/or adenohypophysis. Expression at these stages makes these genes useful markers to study HP maturation and function.

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.

Zebrafish Models of Human Disease: Gaining Insight into Human Disease at ZFIN.

The Zebrafish Model Organism Database (ZFIN; https://zfin.org) is the central resource for genetic, genomic, and phenotypic data for zebrafish (Danio rerio) research. ZFIN continuously assesses trends in zebrafish research, adding new data types and providing data repositories and tools that members of the research community can use to navigate data. The many research advantages and flexibility of manipulation of zebrafish have made them an increasingly attractive animal to model and study human disease.To facilitate disease-related research, ZFIN developed support to provide human disease information as well as annotation of zebrafish models of human disease. Human disease term pages at ZFIN provide information about disease names, synonyms, and references to other databases as well as a list of publications reporting studies of human diseases in which zebrafish were used. Zebrafish orthologs of human genes that are implicated in human disease etiology are routinely studied to provide an understanding of the molecular basis of disease. Therefore, a list of human genes involved in the disease with their corresponding zebrafish ortholog is displayed on the disease page, with links to additional information regarding the genes and existing mutations. Studying human disease often requires the use of models that recapitulate some or all of the pathologies observed in human diseases. Access to information regarding existing and published models can be critical, because they provide a tractable way to gain insight into the phenotypic outcomes of the disease. ZFIN annotates zebrafish models of human disease and supports retrieval of these published models by listing zebrafish models on the disease term page as well as by providing search interfaces and data download files to access the data. The improvements ZFIN has made to annotate, display, and search data related to human disease, especially zebrafish models for disease and disease-associated gene information, should be helpful to researchers and clinicians considering the use of zebrafish to study human disease.

Expression of the zinc finger Egr1 gene during zebrafish embryonic development.

Egr1 is a highly conserved zinc finger protein which plays important roles in many aspects of vertebrate development and in the adult. The cDNA coding for zebrafish Egr1 was obtained and its expression pattern was examined during zebrafish embryogenesis using whole-mount in situ hybridization. Egr1 mRNA is first detected in adaxial cells in the presomitic mesoderm between 11 and 20 h post-fertilization (hpf), spanning the 4-24 somite stages. Later, Egr1 expression is observed only in specific brain areas, starting at 21 hpf and subsequently increasing in distinct domains of the central nervous system, e.g. in the telencephalon, diencephalon and hypothalamus. Between 24 and 48 hpf, Egr1 is expressed in specific domains of the hypothalamus, mesencephalon, tegmentum, pharynx, retina, otic vesicle and heart.

Equivalent progenitor cells in the zebrafish anterior preplacodal field give rise to adenohypophysis, lens, and olfactory placodes.

Embryonic organizing centers secrete signaling molecules that instruct target cells about their position and future identity. Information about cell position in relation to sources of instructive signals and about precursor cell lineages is key to our understanding of developmental processes that restrict cell potency and determine cell fate. We review adenohypophysis, lens, and olfactory placode formation and how gene expression patterns, cell positions, and cell fates in the anterior neural plate and anterior placodal field correlate in zebrafish and other vertebrates. Single cell lineage analysis in zebrafish suggests that the majority of preplacodal cells might be specified for pituitary, lens, or olfactory placode by the end of gastrulation.

belladonna/(Ihx2) is required for neural patterning and midline axon guidance in the zebrafish forebrain.

Some of the earliest axon pathways to form in the vertebrate forebrain are established as commissural and retinal axons cross the midline of the diencephalon and telencephalon. To better understand axon guidance in the forebrain, we characterized the zebrafish belladonna (bel) mutation, which disrupts commissural and retinal axon guidance in the forebrain. Using a positional cloning strategy, we determined that the bel locus encodes zebrafish Lhx2, a lim-homeodomain transcription factor expressed in the brain, eye and fin buds. We show that bel(Ihx2) function is required for patterning in the ventral forebrain and eye, and that loss of bel function leads to alterations in regulatory gene expression, perturbations in axon guidance factors, and the absence of an optic chiasm and forebrain commissures. Our analysis reveals new roles for Ihx2 in midline axon guidance, forebrain patterning and eye morphogenesis.