Mondo: Unifying diseases for the world, by the world

Vasilevsky, Nicole; Matentzoglu, Nicolas; Toro, Sabrina; Flack, Joe E; Unni, Deepak; Alyea, Gioconda; Amberger, Joanna S.; Babb, Larry; Balhoff, James P.; Bingaman, Taylor I.; Burns, Gully A. P. C.; Callahan, Tiffany J.; Carmody, Leigh C.; Chan, Lauren; Chang, George S; Dumontier, Michel; Failla, Laura; Flowers, May J; Garrett, H A; Gration, Dylan; Groza, Tudor; Hanauer, Marc; Harris, Nomi L.; Helbig, Ingo; Hilton, Jason; Himmelstein, Daniel S.; Hoyt, Charles Tapley; Kane, Megan; Köhler, Sebastian; Lagorce, David; Larralde, Martin; Lock, Antonia; Santiago, Irene Lopez; Maglott, Donna; Malheiro, Adriana J; Meldal, Birgit; McMurry, Julie A.; Muñoz-Torres, Monica; Nelson, Tristan; Ochoa, David; Oprea, Tudor I.; Osumi-Sutherland, David; Parkinson, Helen; Pendlington, Zoë May; Rath, Ana; Rehm, Heidi L.; Remennik, Lyubov; Riggs, Erin Rooney; Roncaglia, Paola; Ross, Justyne; Shadbolt, Marion; Shefchek, Kent; Similuk, Morgan; Sioutos, Nicholas; Sparks, Rachel; Stefancsik, Raymund; Stephan, Ralf; Stupp, Doron; Sundaramurthi, Jagadish Chandrabose; Tammen, Imke; Thaxton, Courtney; Valasek, Eloise; Wagner, Alex H.; Welter, Danielle; Whetzel, Patricia L.; Whiteman, Lori; Wood, Valerie; Xu, Colleen H; Zankl, Andreas; Zhang, Xingmin A; Chute, Christopher G; Robinson, Peter N.; Mungall, Christopher J.; Hamosh, Ada; Haendel, Melissa

doi:10.1101/2022.04.13.22273750

Cited by 43 publications

(29 citation statements)

References 21 publications

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“…While the Bioregistry provides a solution for standardizing the syntax of references to and between various biomedical resources, it is not itself sufficient for standardizing equivalent entities occurring in different semantic spaces (e.g., Monarch Disease Ontology (MONDO) 54 , Medical Subject Headings (MeSH) 50 , Human Phenotype Ontology (HPO) 55 , and Human Disease Ontology (DO) 56 for phenotypes and diseases; Cell Ontology (CL) 57 , Cell Line Ontology (CL) 58 , Brenda Tissue Ontology (BTO) 59 for cell types; GO, Reactome 60 , WikiPathways 61 for pathways) that often lead to redundancy when integrating disparate resources, such as when constructing knowledge graphs. However, the Bioregistry enables the standardization of the underlying CURIEs and URIs in these resources that makes existing solutions such as lexical mapping, graph-based entity alignment, and ontology merging more actionable.…”

Section: Discussionmentioning

confidence: 99%

Unifying the Identification of Biomedical Entities with the Bioregistry

Hoyt

Balk

Callahan

et al. 2022

Preprint

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The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 20 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry.

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Section: Discussionmentioning

confidence: 99%

Unifying the Identification of Biomedical Entities with the Bioregistry

Hoyt

Balk

Callahan

et al. 2022

Preprint

Self Cite

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“…The newly added data includes disease definitions, hierarchy, and mappings provided by the Mondo Disease Ontology (Mondo) ( 21 , 22 ). This includes synonyms for equivalent terms from 24 ontologies, such as Disease Ontology (DO) ( 23 ), Online Mendelian Inheritance in Man (OMIM) ( 24 ), Orphanet ( 25 ), Unified Medical Language System (UMLS) ( 26 ), Genetic and Rare Diseases Information Center (GARD) ( 21 ), etc.…”

Section: Methodsmentioning

confidence: 99%

Pharos 2023: an integrated resource for the understudied human proteome

Kelleher

Sheils

Mathias

et al. 2022

Nucleic Acids Research

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The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.

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“…The ClinVar v20220403 database ( 38 ) was used. To select HCM-associated variants, we filtered the dataset using a combination of three disease ontologies by keeping variants with any of the following identifiers: MedGen ( 39 ) (C3495498, C0949658); OMIM ( 40 ) (192600); Mondo Disease Ontology ( 41 ) (0005045, 0024573). Additionally, we excluded all variants with zero-star review status, classified as “Uncertain_significance” or with conflicting interpretations of pathogenicity.…”

Section: Methodsmentioning

confidence: 99%

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Barbosa

Ribeiro

Carmo‐Fonseca

et al. 2022

Front. Cardiovasc. Med.

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Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.

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Mondo: Unifying diseases for the world, by the world

Cited by 43 publications

References 21 publications

Unifying the Identification of Biomedical Entities with the Bioregistry

Unifying the Identification of Biomedical Entities with the Bioregistry

Pharos 2023: an integrated resource for the understudied human proteome

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

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