GUILDify v2.0: A Tool to Identify Molecular Networks Underlying Human Diseases,Their Comorbidities and Their Druggable Targets |
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| Affiliation: | 1. Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain;2. Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain;3. Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Vic, Catalonia 08500, Spain;4. Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, SY23 3EB Aberystwyth, United Kingdom;5. Department of Pharmacology and Personalised Medicine, CARIM, FHML, Maastricht University, Universiteitssingel 50, 6229, ER, Maastricht, the Netherlands;1. Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;2. Computational Regulatory Genomics, MRC London Institute of Medical Sciences, London, W12 0NN, UK;3. Structural Bioinformatics Group, Centre for Integrative systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK |
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| Abstract: | The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease–gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein–protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2) |
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