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.

CyVerse: Cyberinfrastructure for open science.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

Accelerating local extinction associated with very recent climate change.

Climate change has already caused local extinction in many plants and animals, based on surveys spanning many decades. As climate change accelerates, the pace of these extinctions may also accelerate, potentially leading to large-scale, species-level extinctions. We tested this hypothesis in a montane lizard. We resurveyed 18 mountain ranges in 2021-2022 after only ~7 years. We found rates of local extinction among the fastest ever recorded, which have tripled in the past ~7 years relative to the preceding ~42 years. Further, climate change generated local extinction in ~7 years similar to that seen in other organisms over ~70 years. Yet, contrary to expectations, populations at two of the hottest sites survived. We found that genomic data helped predict which populations survived and which went extinct. Overall, we show the increasing risk to biodiversity posed by accelerating climate change and the opportunity to study its effects over surprisingly brief timescales.

The future of rare disease drug development: the rare disease cures accelerator data analytics platform (RDCA-DAP).

Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients. The future of rare disease drug development will hopefully rely on increased data sharing and collaboration among the entire rare disease ecosystem. One path to achieving this outcome has been the development of the rare disease cures accelerator, data analytics platform (RDCA-DAP) funded by the US FDA and operationalized by the Critical Path Institute. FDA intentions were clearly focused on improving the quality of rare disease regulatory applications by sponsors seeking to develop treatment options for various rare disease populations. As this initiative moves into its second year of operations it is envisioned that the increased connectivity to new and diverse data streams and tools will result in solutions that benefit the entire rare disease ecosystem and that the platform becomes a Collaboratory for engagement of this ecosystem that also includes patients and caregivers.

Transforming the study of organisms: Phenomic data models and knowledge bases.

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.

Climate change, extinction, and Sky Island biogeography in a montane lizard.

Around the world, many species are confined to "Sky Islands," with different populations in isolated patches of montane habitat. How does this pattern arise? One scenario is that montane species were widespread in lowlands when climates were cooler, and were isolated by local extinction caused by warming conditions. This scenario implies that many montane species may be highly susceptible to anthropogenic warming. Here, we test this scenario in a montane lizard (Sceloporus jarrovii) from the Madrean Sky Islands of southeastern Arizona. We combined data from field surveys, climate, population genomics, and physiology. Overall, our results support the hypothesis that this species' current distribution is explained by local extinction caused by past climate change. However, our results for this species differ from simple expectations in several ways: (a) their absence at lower elevations is related to warm winter temperatures, not hot summer temperatures; (b) they appear to exclude a low-elevation congener from higher elevations, not the converse; (c) they are apparently absent from many climatically suitable but low mountain ranges, seemingly "pushed off the top" by climates even warmer than those today; (d) despite the potential for dispersal among ranges during recent glacial periods (~18,000 years ago), populations in different ranges diverged ~4.5-0.5 million years ago and remained largely distinct; and (e) body temperatures are inversely related to climatic temperatures among sites. These results may have implications for many other Sky Island systems. More broadly, we suggest that Sky Island species may be relevant for predicting responses to future warming.

Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes.

Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of nonexpert students, or "citizen scientists." Crowdsourcing, or data collection by nonexperts, frequently via the internet, has enabled scientists to tackle some large-scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by nonexpert crowds is, however, often questioned and little data have been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 nonexperts and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared with scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a nonexpert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large-scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and nonexperts. We show that there are important instances in which crowd consensus is not a good proxy for correctness.

A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics.

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

The plant ontology as a tool for comparative plant anatomy and genomic analyses.

The Plant Ontology (PO; http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary ('ontology') of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to >110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.

Aligning Standards Communities for Omics Biodiversity Data: Sustainable Darwin Core-MIxS Interoperability.

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.

Ontologies as integrative tools for plant science.

PREMISE OF THE STUDY: Bio-ontologies are essential tools for accessing and analyzing the rapidly growing pool of plant genomic and phenomic data. Ontologies provide structured vocabularies to support consistent aggregation of data and a semantic framework for automated analyses and reasoning. They are a key component of the semantic web. METHODS: This paper provides background on what bio-ontologies are, why they are relevant to botany, and the principles of ontology development. It includes an overview of ontologies and related resources that are relevant to plant science, with a detailed description of the Plant Ontology (PO). We discuss the challenges of building an ontology that covers all green plants (Viridiplantae). KEY RESULTS: Ontologies can advance plant science in four keys areas: (1) comparative genetics, genomics, phenomics, and development; (2) taxonomy and systematics; (3) semantic applications; and (4) education. CONCLUSIONS: Bio-ontologies offer a flexible framework for comparative plant biology, based on common botanical understanding. As genomic and phenomic data become available for more species, we anticipate that the annotation of data with ontology terms will become less centralized, while at the same time, the need for cross-species queries will become more common, causing more researchers in plant science to turn to ontologies.

Innovations in Therapy Development for Rare Diseases Through the Rare Disease Cures Accelerator-Data and Analytics Platform.

Rare diseases impact the lives of an estimated 350 million people worldwide, and yet about 90% of rare diseases remain without an approved treatment. New technologies have become available, such as gene and oligonucleotide therapies, that offer great promise in treating rare diseases. However, progress toward the development of therapies to treat these diseases is hampered by a limited understanding of the course of each rare disease, how changes in disease progression occur and can be effectively measured over time, and challenges in designing and running clinical trials in diseases where the natural history is poorly characterized. Data that could be used to characterize the natural history of each disease has often been collected in various ways, including in electronic health records, patient-report registries, clinical natural history studies, and in past clinical trials. However, each data source contains a limited number of subjects and different data elements, and data is frequently kept proprietary in the hands of the study sponsor rather than shared widely across the rare disease community. The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) is an FDA-funded effort to overcome these persistent challenges. By aggregating data across all rare diseases and making that data available to the community to support understanding of rare disease natural history and inform drug development, RDCA-DAP aims to accelerate the regulatory approval of new therapies. RDCA-DAP curates, standardizes, and tags data across rare disease datasets to make it findable within the database, and contains a built-in analytics platform to help visualize, interpret, and use it to support drug development. RDCA-DAP will coordinate data and tool resources across non-profit, commercial, and for-profit entities to serve a diverse array of rare disease stakeholders that includes academic researchers, drug developers, FDA reviewers and of course patients and their caregivers. Drug development programs utilizing the RDCA-DAP will be able to leverage existing data to support their efforts and reach definitive decisions on the efficacy of their therapeutics more efficiently and more rapidly than ever.

Establishing a Common Nutritional Vocabulary - From Food Production to Diet.

Informed policy and decision-making for food systems, nutritional security, and global health would benefit from standardization and comparison of food composition data, spanning production to consumption. To address this challenge, we present a formal controlled vocabulary of terms, definitions, and relationships within the Compositional Dietary Nutrition Ontology (CDNO, www.cdno.info) that enables description of nutritional attributes for material entities contributing to the human diet. We demonstrate how ongoing community development of CDNO classes can harmonize trans-disciplinary approaches for describing nutritional components from food production to diet.

MIxS-SA: a MIxS extension defining the minimum information standard for sequence data from symbiont-associated micro-organisms.

The symbiont-associated (SA) environmental package is a new extension to the minimum information about any (x) sequence (MIxS) standards, established by the Parasite Microbiome Project (PMP) consortium, in collaboration with the Genomics Standard Consortium. The SA was built upon the host-associated MIxS standard, but reflects the nestedness of symbiont-associated microbiota within and across host-symbiont-microbe interactions. This package is designed to facilitate the collection and reporting of a broad range of metadata information that apply to symbionts such as life history traits, association with one or multiple host organisms, or the nature of host-symbiont interactions along the mutualism-parasitism continuum. To better reflect the inherent nestedness of all biological systems, we present a novel feature that allows users to co-localize samples, to nest a package within another package, and to identify replicates. Adoption of the MIxS-SA and of the new terms will facilitate reports of complex sampling design from a myriad of environments.

A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data.

Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework. FuTRES already stores millions of trait measurements for paleobiological, zooarchaeological, and modern specimens, with a current focus on mammals. We compare dynamically derived extant mammal species' body size measurements in FuTRES with summary values from other compilations, highlighting potential issues with simply reporting a single mean estimate. We then show that individual-level data improve estimates of body mass-including uncertainty-for zooarchaeological specimens. FuTRES facilitates trait data integration and discoverability, accelerating new research agendas, especially scaling from intra- to interspecific trait variability.

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.

The founding charter of the Omic Biodiversity Observation Network (Omic BON).

Omic BON is a thematic Biodiversity Observation Network under the Group on Earth Observations Biodiversity Observation Network (GEO BON), focused on coordinating the observation of biomolecules in organisms and the environment. Our founding partners include representatives from national, regional, and global observing systems; standards organizations; and data and sample management infrastructures. By coordinating observing strategies, methods, and data flows, Omic BON will facilitate the co-creation of a global omics meta-observatory to generate actionable knowledge. Here, we present key elements of Omic BON's founding charter and first activities.

Angiosperm leaf vein patterns are linked to leaf functions in a global-scale data set.

PREMISE OF THE STUDY: Leaves are plants' primary interface with the atmosphere and affect a range of ecological processes. Vein patterns are one of the most prominent aspects of leaf form, and the functional significance of different vein patterns is gaining increasing attention. METHODS: Phylogenetic and standard ANOVA and regression were used to provide the first global-scale, phylogenetically based test of relations between angiosperm vein patterns and leaf functional traits. Pagel's λ was used to test for phylogenetic signal in all traits. KEY RESULTS: All leaf traits had significant phylogenetic signal. Significant phylogenetically based relations were found between secondary vein pattern and leaf functions, linking leaf form to the well-known trade-off between physiological activity and leaf life span. The relations between primary vein pattern and leaf functions were not found to be significant with phylogenetic tests, suggesting these relations may be the result of changes within a few lineages, followed by phylogenetic conservatism, rather than multiple instances of correlated trait evolution. The relation between minor vein density and maximum photosynthetic rate was found to be marginally nonsignificant with phylogenetic regression, which does not rule out coordinated evolution of hydraulic supply and demand. CONCLUSIONS: Although phylogenetic conservatism may weaken statistical relations between vein patterns and leaf functions, phylogenetic relations can provide a complementary source of information for inferring unmeasured values of leaf traits. Relations among vein patterns, leaf functions, and phylogeny will be valuable for estimating functional attributes of living and fossil plant species and communities.

Alleviating Environmental Health Disparities Through Community Science and Data Integration.

Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these "wicked" dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO).