Data Research OverviewNameDescriptionPublicationAberOWLOntology repository and reasoning-as-a-service Hoehndorf, R., Slater, L., Schofield, P. N., & Gkoutos, G. V. (2015). Aber-OWL: a framework for ontology-based data access in biology. BMC Bioinformatics, 16(1). https://doi.org/10.1186/s12859-015-0456-9PhenomeNETCross-species phenotype ontology and similarity computation Hoehndorf, R., Schofield, P. N., & Gkoutos, G. V. (2011). PhenomeNET: a whole-phenome approach to disease gene discovery. Nucleic Acids Research, 39(18), e119–e119. https://doi.org/10.1093/nar/gkr538PathoPhenoDBDatabase of pathogen-to-phenotype associations Kafkas, Ş., Abdelhakim, M., Hashish, Y., Kulmanov, M., Abdellatif, M., Schofield, P. N., & Hoehndorf, R. (2019). PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0090-xICDPhenoPhenotypes associated with diseases in the ICD Kafkas, Ş., Althubaiti, S., Gkoutos, G. V., Hoehndorf, R., & Schofield, P. N. (2021). Linking common human diseases to their phenotypes; development of a resource for human phenomics. Journal of Biomedical Semantics, 12(1). https://doi.org/10.1186/s13326-021-00249-xDDIEMDrug Database for Inborn Error of Metabolism Abdelhakim, M., McMurray, E., Syed, A. R., Kafkas, S., Kamau, A. A., Schofield, P. N., & Hoehndorf, R. (2020). DDIEM: drug database for inborn errors of metabolism. Orphanet Journal of Rare Diseases, 15(1). https://doi.org/10.1186/s13023-020-01428-2Phenotype Reactor Database and website of phenotype associations EDAM-annotated ontologiesA manually curated list of all ontologies in AberOWL annotated by topic, species and NCBI taxonomyRodríguez-García MÁ, Slater L, Boudellioua I, Schofield P, Gkoutos G, Hoehndorf R (2016)Updates to the AberOWL ontology repository (ICBO BioCreative 2016) PhenomeNet Similarity Matrix (Human)A matrix of PhenomeNet similarity scores between genes and OMIM IDs using human phenotypes onlyBoudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500Hoehndorf R, Schofield PN, Gkoutos GV (2011) PhenomeNET: a whole-phenome approach to disease gene discovery. Nuclein Acids Research, https://doi.org/10.1093/nar/gkr538PhenomeNet Similarity Matrix (Mouse)A matrix of PhenomeNet similarity scores between genes and OMIM IDs using mouse phenotypes onlyBoudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500Hoehndorf R, Schofield PN, Gkoutos GV (2011) PhenomeNET: a whole-phenome approach to disease gene discovery. Nuclein Acids Research, https://doi.org/10.1093/nar/gkr538Raw ExomesA set of synthetic exomes (VCF File Format) generated using ClinVar variantsBoudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500Raw Exomes (MAF)A set of synthetic exomes (VCF File Format) generated using ClinVar variants, filtered by MAF < 1%Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500Raw GenomesA set of synthetic genomes (VCF File Format) generated using ClinVar variantsBoudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500Raw Genomes (MAF)A set of synthetic genomes (VCF File Format) generated using ClinVar variants, filtered by MAF < 1%Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500RDF Knowledge Graph of proteins, drugs, diseases, functions and phenotypesAn RDF dataset containing data on the following semantic types, gene, drug, protein function, diseaseAlshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, Hoehndorf R (2017) Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics btx275. doi:10.1093/bioinformatics/btx275Knowledge Graph embeddingsA knowledge graph embeddings containing of the RDF dataset mentioned aboveAlshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, Hoehndorf R (2017) Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics btx275. doi:10.1093/bioinformatics/btx275Protein-protein interaction network embeddings for uniprot proteinsNetwork embeddings generated with knowledge graph representation learning methodKulmanov M, Khan MA, Hoehndorf R (2017) DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics, btx624, https://doi.org/10.1093/bioinformatics/btx624Functions of CAFA3 targetsProtein function predictions for CAFA3 targets by using DeepGOKulmanov M, Khan MA, Hoehndorf R (2017) DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics, btx624, https://doi.org/10.1093/bioinformatics/btx624SIDER2DOMapping between indications in SIDER and Disease OntologyHoehndorf R, Schofield PN, Gkoutos GV (2015) Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Scientific Reports, doi: 10.1038/srep10888Disease Similarity MatrixA disease-disease similarity matrix based on phenotype similarity, using Human Disease Ontology to represent diseasesHoehndorf R, Schofield PN, Gkoutos GV (2015) Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Scientific Reports, doi: 10.1038/srep10888Disease-Disease drug similarityA file containing pairs of diseases (using Human Disease Ontology) treated with the same drugsHoehndorf R, Schofield PN, Gkoutos GV (2015) Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Scientific Reports, doi: 10.1038/srep10888
