Reader in Biomedical Informatics
Integration and exploitation of Big Data for human health
Comparing phenotypes between species potentially provides invaluable insights into the pathobiology and etiology of human disease. Phenotypic characterisation of, for example, mouse and zebrafish mutants can provide information that can be used to prioritise gene lists derived from human genome-wide association studies, allow the dissection of loci involved in copy number-variation lesions, and provide functional validation of disease gene candidates, as well as insights into basic biological processes. The ability to cross the species divide has long been a thorny problem, as human and model organism phenotypes are described using different formal ontologies and conceptual approaches. To address this, we are working to develop a series of ontologies and tools that use those ontologies, allowing the seamless integration of phenotypic data between species. We are now applying semantic approaches to the integration of large public datasets including patient electronic health records, drug effect data and the phenotypes of mutant model organisms. This work is concentrating on the use of this data to develop new therapeutic approaches to human disease, for example through the repositioning of existing drugs.
Data sharing initiatives
Data access and integration have become central to modern biology and using our experience we are developing with the German Federal Radiation Protection agency (BfS) and the MELODI (melodi-online.eu) initiative, a public database for primary experimental and epidemiological data from radiation biology: STORE. This database will provide a platform for international data sharing and uses state of the art informatics to maximise data discovery and recovery.
Prof Robert Hoehndorf, Computational Biology, King Abdullah University of Science and Technology, Saudi Arabia
Prof George Gkoutos, Centre for Computational Biology, College of Medical and Dental Sciences, University of Birmingham, UK
Prof Peter Robinson, Institut für Medizinische Genetik und Humangenetik Charité - Universitätsmedizin Berlin
Prof John Sundberg, The Jackson Laboratory, Bar Harbor, Maine, USA
Dr Bernd Grosche, Bundesamt fuer Strahlenschutz, Neuherberg, Germany
Boudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R,( 2017), Semantic prioritization of novel causative genomic variants,, 13:e1005500.
Gkoutos GV, Schofield PN, Hoehndorf R, (2017), The anatomy of phenotype ontologies: principles, properties and applications, Briefings in Bioinformatics, Brief Bioinform bbx035
Schofield, P.N and Bard, J.B. (2015) Human anatomy informatics. Commentary 2.1. in Gray’s Anatomy, 41st Edition. ed Standring. S. Elsevier.
Hoehndorf R, Schofield PN, Gkoutos GV, (2015), Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases, Scientific reports, 5, 10888
Groza T, Kohler S, Moldenhauer D et al., (2015), The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease, American Journal of Human Genetics, 97, 111-124
Hoehndorf R, Schofield PN, Gkoutos GV, (2015), The role of ontologies in biological and biomedical research: a functional perspective, Briefings in Bioinformatics, 10.1093/bib/bbv011
Hoehndorf R, Gruenberger M, Gkoutos GV, Schofield PN, (2015), Similarity-based search of model organism, disease and drug effect phenotypes, Journal of Biomedical Semantics, 6, 6
Hoehndorf R, Slater L, Schofield PN, Gkoutos GV, (2015), Aber-OWL: a framework for ontology-based data access in biology, BMC bioinformatics, 16, 26
Moeller M, Hirose M, Mueller S, Roolf C, Baltrusch S, Ibrahim S, Junghanss C, Wolkenhauer O, Jaster R, Kohling R, Kunz M, Tiedge M, Schofield PN, Fuellen G, (2014), Inbred mouse strains reveal biomarkers that are pro-longevity, antilongevity or role switching, Aging Cell, 13, 729-738
Ibn-Salem J, Kohler S, Love MI et al., (2014), Deletions of chromosomal regulatory boundaries are associated with congenital disease, Genome Biology, 15, 423
Kohler S, Doelken SC, Mungall CJ, et al., (2014), The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data, Nucleic Acids Research, 42, D966-974
Hoehndorf R, Hancock JM, Hardy NW, Mallon AM, Schofield PN, Gkoutos GV, (2014), Analyzing gene expression data in mice with the Neuro Behavior Ontology, Mammalian Genome, 25, 32-40
Hoehndorf R, Hiebert T, Hardy NW, Schofield PN, Gkoutos GV, Dumontier M, (2014), Mouse model phenotypes provide information about human drug targets, Bioinformatics, 30, 719-725
Sundberg JP, Roopenian DC, Liu ET, Schofield PN, (2013), The Cinderella effect: searching for the best fit between mouse models and human diseases, The Journal of Investigative Dermatology, 133, 2509-2513
Hoehndorf R, Schofield PN, Gkoutos GV, (2011), PhenomeNET: a whole-phenome approach to disease gene discovery, Nucleic Acids Research, 39, e119
Boudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R: Semantic prioritization of novel causative genomic variants. PLoS computational biology 2017, 13:e1005500.