HyARQ: a Snakemake workflow for allele-specific read counting and quality control in F1 hybrids

Abstract

Summary Allele-specific analysis in F1 hybrids allows direct assessment of how cis-regulatory variation contributes to differences in gene expression and other regulatory features. However, allele-specific analyses are sensitive to errors arising from multiple sources, including mapping bias and technical variation, requiring careful processing and quality control. These challenges are compounded by the fact that workflows for different molecular assays are often developed separately, making consistent analysis and quality control across assays difficult. Here, we introduce HyARQ, a Snakemake workflow for allele-specific read counting and quality control across RNA-seq, MOA-seq, ATAC-seq, and ChIP-seq data. HyARQ integrates assay-specific preprocessing, mapping to concatenated parental genomes, filtering, counting at paired parental genes or regulatory regions, and quality control within a common workflow. Using simulated datasets with known parental origin and allelic proportions, we show that HyARQ accurately recovers expected parental proportions with minimal cross-parent assignment across all four assay types. We further applied HyARQ to real RNA-seq, MOA-seq, ATAC-seq, and ChIP-seq datasets and generated paired parental counts and assay-specific quality control outputs for each assay. HyARQ provides a standardized and reproducible framework for allele-specific analysis across multiple molecular layers and facilitates downstream studies of cis-regulatory variation in F1 hybrids.