Year: 2023
Type: preprint
Abstract: Motivation: To understand whether sets of genomic loci are enriched at the regulatory loci of one or more cell types, we developed the gaiaAssociation package to perform Regulatory Landscape Enrichment Analysis (RLEA). RLEA is a novel analytical process that tests for enrichment of sets of loci in cell type-specific open chromatin regions (OCRs) in the genome. Results: We demonstrate that the application of RLEA to genome-wide association study (GWAS) data reveals cell types likely to be mediating the phenotype studied, and clusters OCRs based on their shared regulatory profiles. GaiaAssociation is Python code that is freely available for use in functional genomics studies. Availability and Implementation: Gaia Association is available on PyPi (https://pypi.org/project/gaiaAssociation/0.6.0/#description) for pip download and use on the command line or as an inline Python package. Gaia Association can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Inference of somatic mutation rates from high-throughput sequencing data: models and applications, UNDERSTANDING CELLULAR AND TRANSCRIPTIONAL REGULATORY CHANGES IN HUMAN AGING, SFI Centre for Research Training in Genomics Data Science, All of Us Consortium of CTSA Community Engagement Programs, Defining the gene regulatory roles of non-coding variants in the pathogenesis of autism
OpenAlex ID: W4387665927