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Regulatory Landscape Enrichment Analysis (RLEA) using gaiaAssociation.
Sosa, Eric A; Rosean, Samuel; O'Shea, Dónal; Raj, Srilakshmi M; Seoighe, Cathal; Greally, John M.
Afiliação
  • Sosa EA; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
  • Rosean S; Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
  • O'Shea D; School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland.
  • Raj SM; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
  • Seoighe C; School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland.
  • Greally JM; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
bioRxiv ; 2023 Oct 16.
Article em En | MEDLINE | ID: mdl-37905111
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article