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：现代高通量测序检测不仅能高效捕获基因表达及不同层面的基因调控信息，还能同时检测大量基因组变异。通过评估同源染色体上可变位点中替代等位基因的读数计数等位基因不平衡，可揭示等位基因特异性表达与等位基因特异性调控。本文正式描述了一个用于检测多种组学检测（ChIP-Seq、ATAC-Seq、DNase-Seq、CAGE-Seq等）中单核苷酸变异位点等位基因读数的先进统计框架。MIXALIME模型可解释拷贝数变异、非整倍体及参考序列比对偏差，并针对不同实验噪声引起的过离散数据提供多种评分模型，以平衡敏感性与特异性。