SysBiolPGWAS: Simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets

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dc.contributor.authorFalola, Oluwadamilare
dc.contributor.authorAdam, Yagoub
dc.contributor.authorAjayi, Olabode
dc.date.accessioned2023-03-16T09:37:35Z
dc.date.available2023-03-16T09:37:35Z
dc.date.issued2023
dc.description.abstractPost-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes.en_US
dc.identifier.citationFalola, O. et al. (2023). SysBiolPGWAS: Simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets. Bioinformatics, 39(1), btac791. https://doi.org/10.1093/bioinformatics/btac791en_US
dc.identifier.issn1367-4811
dc.identifier.urihttps://doi.org/10.1093/bioinformatics/btac791
dc.identifier.urihttp://hdl.handle.net/10566/8604
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.subjectBioinformaticsen_US
dc.subjectBiologyen_US
dc.subjectWeb delopmenten_US
dc.subjectStatistics studiesen_US
dc.subjectData managementen_US
dc.titleSysBiolPGWAS: Simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasetsen_US
dc.typeArticleen_US

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