Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization

• Published in: BMC bioinformatics Vol. 25; no. 1; p. 179
• Main Authors: Rosean, Samuel, Sosa, Eric A, O'Shea, Dónal, Raj, Srilakshmi M, Seoighe, Cathal, Greally, John M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.05.2024; BioMed Central; BMC
• Subjects: Analysis; Binomial distribution; Cell-type prioritization; Chromatin; Chromatin architecture; Computational Biology - methods; Disease; DNA sequencing; Enrichment; Evaluation; Gene expression; Genetic Variation; Genome-wide association studies; Genomes; Genomics; Genomics - methods; Genotype & phenotype; GWAS; Humans; Information management; Linkage Disequilibrium; Loci; Medical research; Medicine, Experimental; Non-coding enrichment; Nucleotide sequencing; Open source software; Phenotype; Phenotypes; Public software; Random variables; Regulatory sequences; Regulatory Sequences, Nucleic Acid - genetics; Software; Source code; United States; Chromatin architecture; Cell-type prioritization; GWAS; Non-coding enrichment
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05794-7

As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.