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Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases.

ABSTRACT: The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate the effect of compounds using electronic health records. For complete details on the use and execution of this protocol, please refer to Gao et al.¹ and Baylis et al.².

SUBMITTER: Gao H

PROVIDER: S-EPMC10876979 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases.

Gao Hua H Zhang Mao M Baylis Richard A RA Wang Fudi F Björkegren Johan L M JLM Kovacic Jason J JJ Ruusalepp Arno A Leeper Nicholas J NJ

STAR protocols 20240212 1

The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate th ...[more]

PMID: 38354084

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Project description:BackgroundOver the last decade, computational sciences have contributed immensely to characterization of phenotypes of airway diseases, but it is difficult to compare derived phenotypes across studies, perhaps as a result of the different decisions that fed into these phenotyping exercises. We aim to perform a systematic review of studies using computational approaches to phenotype obstructive airway diseases in children and adults.Methods and analysisWe will search PubMed, Embase, Scopus, Web of Science, and Google Scholar for papers published between 2010 and 2020. Conferences proceedings, reference list of included papers, and experts will form additional sources of literature. We will include observational epidemiological studies that used a computational approach to derive phenotypes of chronic airway diseases, whether in a general population or in a clinical setting. Two reviewers will independently screen the retrieved studies for eligibility, extract relevant data, and perform quality appraisal of included studies. A third reviewer will arbitrate any disagreements in these processes. Quality appraisal of the studies will be undertaken using the Effective Public Health Practice Project quality assessment tool. We will use summary tables to describe the included studies. We will narratively synthesize the generated evidence, providing critical assessment of the populations, variables, and computational approaches used in deriving the phenotypes across studies CONCLUSION: As progress continues to be made in the area of computational phenotyping of chronic obstructive airway diseases, this systematic review, the first on this topic, will provide the state of the art on the field and highlight important perspectives for future works.Ethics and disseminationNo ethical approval is needed for this work is based only on the published literature and does not involve collection of any primary or human data.Registration and reportingSYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020164898.

Dataset Information

Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases.

Publications

Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases.

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