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

Falola, Oluwadamilare and Adam, Yagoub and Ajayi, Olabode and Kumuthini, Judit and Adewale, Suraju and Mosaku, Abayomi and Samtal, Chaimae and Adebayo, Glory and Emmanuel, Jerry and Tchamga, Milaine S. S. and Erondu, Udochukwu and Adebayo, Nehemiah and Rasaq, Suraj and Ajayi, Mary and Akanle, B. and Oladipo, Olaleye and Isewon, Itunuoluwa and Adebiyi, Marion O. and Oyelade, O. J. and Adebiyi, E. F. (2023) SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets. Bioinformatics,, 39 (1).

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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. 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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA76 Computer software Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Patricia Nwokealisi
Date Deposited:	31 Jul 2024 11:08
Last Modified:	31 Jul 2024 11:08
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18334

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