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). \textbf{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：现代高通量测序分析不仅能高效捕获基因表达和不同水平的基因调控信息，还能同时检测大量基因组变异。通过对人类基因组同源染色体上可变位点的替代等位基因进行靶向分析，可以评估单核苷酸位点的等位基因读段计数不平衡，从而揭示等位基因特异性表达和等位基因特异性调控。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中（ChIP-Seq、ATAC-Seq、DNase-Seq、CAGE-Seq等）单核苷酸变异位点等位基因读段计数的不平衡性。\textbf{MIXALIME} 可校正拷贝数变异和非整倍体、参考序列读段比对偏差，并提供多种评分模型，以在数据实验噪声引起的过度离散程度不同时，平衡敏感性与特异性。