PhenoMiner: from text to a database of phenotypes associated with OMIM diseases.
Analysis of scientific and clinical phenotypes reported in the experimental literature has been curated manually to build high-quality databases such as the Online Mendelian Inheritance in Man (OMIM). However, the identification and harmonization of phenotype descriptions struggles with the diversity of human expressivity. We introduce a novel automated extraction approach called PhenoMiner that exploits full parsing and conceptual analysis. Apriori association mining is then used to identify relationships to human diseases. We applied PhenoMiner to the BMC open access collection and identified 13,636 phenotype candidates. We identified 28,155 phenotype-disorder hypotheses covering 4898 phenotypes and 1659 Mendelian disorders. Analysis showed: (i) the semantic distribution of the extracted terms against linked ontologies; (ii) a comparison of term overlap with the Human Phenotype Ontology (HP); (iii) moderate support for phenotype-disorder pairs in both OMIM and the literature; (iv) strong associations of phenotype-disorder pairs to known disease-genes pairs using PhenoDigm. The full list of PhenoMiner phenotypes (S1), phenotype-disorder associations (S2), association-filtered linked data (S3) and user database documentation (S5) is available as supplementary data and can be downloaded at http://github.com/nhcollier/PhenoMiner under a Creative Commons Attribution 4.0 license. Database URL: phenominer.mml.cam.ac.uk.
The University of Cambridge, Cambridge, CB3 9DB, UK, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, nhc30@cam.ac.uk.
Nigel
N
Collier
Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia.
Tudor
T
Groza
Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.
Damian
D
Smedley
Institute for Medical Genetics and Human Genetics, Charité-Universitatsmedizin Berlin, 13353 Berlin, Germany and.
Peter N
PN
Robinson
Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.
Anika
A
Oellrich
The Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland.
Dietrich
D
Rebholz-Schuhmann
D000818:Animals; D020296:Animals, Congenic; D001794:Blood Pressure; D033481:Chromosomes, Mammalian / Q000235:genetics; D057225:Data Mining; D019992:Databases as Topic; D030541:Databases, Genetic; D004032:Diet; D006801:Humans; D006973:Hypertension / Q000150:complications / Q000503:physiopathology / Q000652:urine; D007674:Kidney Diseases / Q000150:complications / Q000503:physiopathology / Q000652:urine; D008954:Models, Biological; D010641:Phenotype; D051381:Rats; D011922:Rats, Mutant Strains; D012964:Sodium / Q000652:urine; D012984:Software; D013599:Systole
D012964:Sodium