SysBiolPGWAS
SysBiolPGWAS performs post-genome-wide association study (pGWAS) analyses to interpret functional implications of significant single-nucleotide polymorphisms (SNPs) and to integrate multi-omics datasets for downstream biological interpretation.
Key Features:
- Comprehensive pGWAS functionality: Suite of analyses for post-GWAS interpretation of significant single-nucleotide polymorphisms (SNPs).
- Integration of multi-omics datasets: Supports integration and analysis of diverse omics datasets to link genetic associations with additional molecular layers.
- Custom pGWAS pipeline: Implements a custom pipeline that integrates multiple individual pGWAS tools and datasets.
- Visualization tools: Provides visualization capabilities using bioinformatics tools to facilitate interpretation of complex data outputs.
Scientific Applications:
- Human autosomal variant interpretation: Interprets genetic associations on autosomal chromosomes to investigate functional consequences of SNPs.
- High-throughput genomic studies: Handles large-scale datasets for high-throughput post-GWAS analyses.
- Translational genomics and personalized medicine: Supports translation of genetic findings toward disease screening, treatment, and prevention strategies and personalized medicine applications.
Methodology:
Integration of multiple individual pGWAS tools and external datasets into a custom pipeline forming a cohesive platform for post-GWAS analyses.
Topics
Details
- License:
- Not licensed
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Fortran, Assembly language, C
- Added:
- 2/10/2023
- Last Updated:
- 11/24/2024
Operations
Data Inputs & Outputs
Genotyping
Outputs
Publications
Falola O, Adam Y, Ajayi O, Kumuthini J, Adewale S, Mosaku A, Samtal C, Adebayo G, Emmanuel J, Tchamga MSS, Erondu U, Nehemiah A, Rasaq S, Ajayi M, Akanle B, Oladipo O, Isewon I, Adebiyi M, Oyelade J, Adebiyi E. SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets. Bioinformatics. 2022;39(1). doi:10.1093/bioinformatics/btac791. PMID:36477976. PMCID:PMC9825739.