Research Pages

BORG Current Research List

KAUST CEMSE BORG Research Complex Variant prioritization CompleX: Variant prioritization in complex disease Phenotype-based methods have repeatedly shown to be highly effective in identifying causative variants in whole genome or whole exome sequences. The main limitation of phenotype-based methods, however, is the limited availability of characterised genotype-phenotype associations. Model organism phenotypes have in the past been used to supplement genotype–phenotype associations observed in humans and were demonstrated to predict disease genes. Nevertheless, in almost all cases, genotypes

Data

​Name Description​ Publication​ AberOWL Ontology 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/s12859-015-0456-9 PhenomeNET Cross-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/gkr538 PathoPhenoDB Database of pathogen-to

Neuro-symbolic systems

Details Technological breakthroughs in biological and biomedical research have led to the generation of a large amount of data in many areas. Utilizing this data for improving human life is nevertheless a challenge due to the inherent heterogeneity in the data, the complexity, and the large data size. The early promises of the success of next-generation sequencing in biomedicine have fallen short because it proved challenging to interpret the information obtained from individuals and translate it into new discoveries that benefit human health. A key challenge in biomedicine is to understand

Ontologies

​Name ​Description Publication​ ​ FLOPO ​Flora Phenotype Ontology for traits observed in flowering plants ​ https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0107-8 ​ DermO ​Dermatological Disease Ontology ​https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0085-x ​ NBO ​Neuro-Behavior Ontology for behavioral processes and phenotypes ​ https://linkinghub.elsevier.com/retrieve/pii/B978-0-12-388408-4.00004-6 ​ PhenomeNET ​Cross-species phenotype ontology for human, mouse, and fish ​https://academic.oup.com/nar/article/39/18/e119/1092588 ​ PATO ​Ontology of qualities

Pathogen informatics

Details Infectious diseases are caused by a wide range of organisms (viruses, bacteria, fungi, worms, protozoa) that are generally considered as pathogens. Antimicrobial drugs are often the first-line therapy for infectious diseases. However, drug resistance accumulates over time due to the selection of genetic changes in pathogen populations when they are exposed to antimicrobial drugs (such as antibiotics, antifungals, antivirals, antimalarials, and antihelmintics). It now becomes crucial to develop strategies that can identify a pathogen rapidly and determine successful treatment options

Phenotypes

CompleX: Variant prioritization in complex disease Details We are now at a stage when the discovery of new disease genes is slowing. The number of patients remaining undiagnosed following whole exome sequencing argues either that many more disease genes and variants still await discovery, or that the heterogeneity and novelty of disease phenotypes that we see are due to a combination of alleles of multiple, known, disease genes in the same individual. While the particular combinations of alleles in the same person may be rare, they likely involve medium-rare or common alleles as well as rare

Prediction of functions and phenotypes

Details The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high throughput sequencing technologies in the life sciences. It is a key question in the life sciences to identify the functions of proteins, and furthermore to identify the phenotypes that may be associated with a loss (or gain) of function in these proteins. Protein functions are generally determined experimentally, and it is clear that experimental determination of protein functions will not scale to the current – and rapidly increasing – amount of

Software

​Name ​Description Publication​ DTI-Voodoo Accurate prediction of drug--target interactions through graph neural networks and side-effects Hinnerichs, T., & Hoehndorf, R. (2021). DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions. Bioinformatics, 37(24), 4835–4843. https://doi.org/10.1093/bioinformatics/btab548 MESH2OWL Converts the MESH metathesaurus into OWL Onto2Graph Generating graphs from ontology axioms Rodríguez-García, M. Á., & Hoehndorf, R. (2018). Inferring ontology graph structures using OWL reasoning. BMC