| dc.identifier.citation | Falola, O., Adam, Y., Ajayi, O., Kumuthini, J., Adewale, S., Mosaku, A., Samtal, C., Adebayo, G., Emmanuel, J., Tchamga, M.S.S., Erondu, U., Nehemiah, A., Rasaq, S., Ajayi, M., Akanle, B., Oladipo, O., Isewon, I., Adebiyi, M., Oyelade, J., Adebiyi, E., 2023. SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets. Bioinformatics 39, btac791. https://doi.org/10.1093/bioinformatics/btac791 | en_US |
| dc.description.abstract | Motivation: 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. Results: 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. | en_US |
