Modern high-throughput sequencing assays efficiently capture not only gene expression and different levels of gene regulation but also a multitude of genome variants. Focused analysis of alternative alleles of variable sites at homologous chromosomes of the human genome reveals allele-specific gene expression and allele-specific gene regulation by assessing allelic imbalance of read counts at individual sites. Here we formally describe an advanced statistical framework for detecting the allelic imbalance in allelic read counts at single-nucleotide variants detected in diverse omics studies (ChIP-Seq, ATAC-Seq, DNase-Seq, CAGE-Seq, and others). MIXALIME accounts for copy-number variants and aneuploidy, reference read mapping bias, and provides several scoring models to balance between sensitivity and specificity when scoring data with varying levels of experimental noise-caused overdispersion.

翻译：现代高通量测序实验不仅能高效捕获基因表达及不同层次的基因调控信息，还能揭示大量基因组变异。通过评估同源染色体上变异位点等位基因读段计数的不平衡性，对人类基因组可变异位点的替代等位基因进行聚焦分析，可揭示等位基因特异性表达和调控。本文正式描述了一种高级统计框架，用于检测多种组学研究（ChIP-Seq、ATAC-Seq、DNase-Seq、CAGE-Seq等）中单核苷酸变异的等位基因读段计数不平衡。MIXALIME可处理拷贝数变异和非整倍体、参考读段映射偏差，并提供多种评分模型，以在实验噪声导致的过度离散程度可变的数据评分中平衡灵敏度与特异性。