dc.contributor.author | Falola, Oluwadamilare | |
dc.contributor.author | Adam, Yagoub | |
dc.contributor.author | Ajayi, Olabode | |
dc.date.accessioned | 2023-03-16T09:37:35Z | |
dc.date.available | 2023-03-16T09:37:35Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Falola, 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/btac791 | en_US |
dc.identifier.issn | 1367-4811 | |
dc.identifier.uri | https://doi.org/10.1093/bioinformatics/btac791 | |
dc.identifier.uri | http://hdl.handle.net/10566/8604 | |
dc.description.abstract | Post-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.language.iso | en | en_US |
dc.publisher | Oxford University Press | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Biology | en_US |
dc.subject | Web delopment | en_US |
dc.subject | Statistics studies | en_US |
dc.subject | Data management | en_US |
dc.title | SysBiolPGWAS: Simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets | en_US |
dc.type | Article | en_US |