Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance.

ABSTRACT: Methods that analyze single-cell paired RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) multiome data have shown promise in linking regulatory elements to genes. However, existing methods exhibit low concordance and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on expression quantitative trait locus (eQTL) data to assign a probabilistic score to each candidate single-nucleotide polymorphism-gene link. pgBoost attained higher enrichment than existing methods for evaluation sets derived from eQTL, activity-by-contact, CRISPR and genome-wide association study (GWAS) data. We further determin

SUBMITTER: Dorans E 

PROVIDER: S-EPMC12704523 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature