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This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.<\/jats:p>","DOI":"10.1093\/bib\/bbaa377","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T20:10:23Z","timestamp":1606507823000},"page":"676-689","source":"Crossref","is-referenced-by-count":8,"title":["Comprehensive pathway enrichment analysis workflows: COVID-19 case study"],"prefix":"10.1093","volume":"22","author":[{"given":"Giuseppe","family":"Agapito","sequence":"first","affiliation":[{"name":"Magna Gr\u00e6cia University, Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Pastrello","sequence":"additional","affiliation":[{"name":"Krembil Research Institute, University Health Network, Toronto, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Jurisica","sequence":"additional","affiliation":[{"name":"Departments of Medical Biophysics and Computer Science, 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