  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.
