Feb. 1st webinar

The Monarch Initiative: harmonizing cross-species data for disease diagnostics and discovery.

Addressing complex scientific challenges requires weaving together data from diverse sources, organisms, contexts, formats, and granularities, and building a coherent holistic view of this data landscape to address any given problem is non-trivial – much of the relevant information is scattered and not readily accessible for searching or analysis. The Monarch Initiative is a consortium and a set of resources aiming to overcome these limitations by integrating the fragmented data landscape into the most comprehensive open collection of genotype-phenotype data available. Monarch seeks to bridge the space between basic and applied and clinical research, developing tools that facilitate connecting data across a variety of scientific approaches and disciplines including genomics, proteomics, molecular modeling, diagnosis of disease and syndromes, and the organization of patient record data. The Monarch Knowledge Graph (KG) links together clinical, biomedical, and basic science research data spanning multiple species, and it supports reasoning across a wide range of organisms, body systems, and diseases. We founded the Human Phenotype Ontology (HPO), one of the most widely used biomedical ontologies and the gold standard for describing human phenotypes, and are also creators of the Mondo unified disease ontology, the Unified Phenotype Ontology (uPheno), the cross-species anatomy ontology (Uberon), the Environmental Conditions and Treatments Ontology (ECTO), and most recently, the Vertebrate Breed Ontology (VBO), a single source for data standardization and integration of all breed names. We also created the Simple Standard for Sharing Ontology Mappings (SSSOM) to harmonize the ontologies that are used by the sources, and the only ISO-approved standard for exchanging detailed, case-level phenotype data, Phenopackets. Monarch tools and resources are publically available and are designed for both informatics users, as well as clinical and basic research use cases. By making data more interoperable, our widely-used standards for data annotation and exchange help support a wide range of data sharing and reuse by projects and organizations around the world, and reduce the effort they need to devote to data harmonization. During this presentation, we will introduce you to a few of these resources and offer you the information to find and implement the ones that best serve your scientific needs.

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