Comprehensive pathway enrichment analysis workflows: COVID-19 case study

Agapito, Giuseppe; Pastrello, Chiara; Jurišica, Igor

doi:10.1093/bib/bbaa377

Cited by 5 publications

(2 citation statements)

References 71 publications

(87 reference statements)

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“…TEA uses topological information to calculate pathway enrichment. TEA based on interaction networks can be used as a base for identifying pathways connected to a particular group of drugs, toxicity, or disease [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

et al. 2022

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As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can represent valuable methods for visualizing and analysing drug-protein and protein–protein interactions. In this study, a KNIME workflow is presented which connects drugs to causal target proteins and target proteins to their causal protein interactors. With the collected data, networks can be constructed for visualizing and interpreting the connections. The last part of the workflow provides a topological enrichment test for identifying relevant pathways and processes connected to the submitted data. The workflow is based on openly available databases and their web services. As a case study, compounds of DILIRank were analysed. DILIRank is the benchmark dataset for Drug-Induced Liver Injury by the FDA, where compounds are categorized by their likeliness of causing DILI. The study includes the drugs that are most likely to cause DILI (“mostDILI”) and the ones that are not likely to cause DILI (“noDILI”). After selecting the compounds of interest, down- and upregulated proteins connected to the mostDILI group were identified; furthermore, a liver-specific subset of those was created. The downregulated sub-list had considerably more entries, therefore, network and causal interactome were constructed and topological pathway enrichment analysis was performed with this list. The workflow identified proteins such as Prostaglandin G7H synthase 1 and UDP-glucuronosyltransferase 1A9 as key participants in the potential toxic events disclosing the possible mode of action. The topological network analysis resulted in pathways such as recycling of bile acids and salts and glucuronidation, indicating their involvement in DILI. The KNIME pipeline was built to support target and network-based approaches to analyse any sets of drug data and identify their target proteins, mode of actions and processes they are involved in. The fragments of the pipeline can be used separately or can be combined as required.

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Section: Introductionmentioning

confidence: 99%

KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

et al. 2022

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“…The main fields of research include: investigating the molecular basis of the disease, tracing virus mutations, developing SARS-CoV-2 genomes and variants databases, studying host–virus interactions, development of novel vaccines and drugs, adaptation of old drugs to the new disease (drug repurposing), collection and analysis of epidemiological data, analysis of electronic health records (EHRs) on a large scale, testing and tracing of people, infectious disease modeling, impact of COVID-19 on public health, effects of the pandemic at the emotional and behavioral level, etc. Each of the previous challenges has gained benefits from the solutions developed in several branches of computer science, with special focus on bioinformatics pipelines, biological databases, network analysis, and data and text mining [ 4 , 5 , 6 , 7 , 8 ].…”

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confidence: 99%