Coeliac disease (CD) is a complex, multifactorial pathology caused by different factors, such as nutrition, immunological response and genetic factors. Many autoimmune diseases are comorbidities for CD, and a comprehensive and integrated analysis with bioinformatics approaches can help in evaluating the interconnections among all the selected pathologies. We first performed a detailed survey of gene expression data available in public repositories on CD and less commonly considered comorbidities. Then we developed an innovative pipeline that integrates gene expression, cell-type data and online resources (e.g. a list of comorbidities from the literature), using bioinformatics methods such as gene set enrichment analysis and semantic similarity. Our pipeline is written in R language, available at the following link: http://bioinformatica.isa.cnr.it/COELIAC_DISEASE/SCRIPTS/. We found a list of common differential expressed genes, gene ontology terms and pathways among CD and comorbidities and the closeness among the selected pathologies by means of disease ontology terms. Physicians and other researchers, such as molecular biologists, systems biologists and pharmacologists can use it to analyze pathology in detail, from differential expressed genes to ontologies, performing a comparison with the pathology comorbidities or with other diseases.
Keywords: clinical bioinformatics; coeliac disease; coeliac disease comorbidities; gene set enrichment analysis; ontologies; semantic similarity.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.