Sanchez-Ramirez Santiago, Cutter Asher
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.
MicroPubl Biol. 2025 Jun 2;2025. doi: 10.17912/micropub.biology.001599. eCollection 2025.
Gene regulatory changes acting and to a gene can be inferred with allele-specific expression (ASE) transcriptomes from interspecies and inter-population hybrids and their parents. Problems of mapping bias and excessive information loss, however, can arise unintentionally from cumbersome analysis pipelines. We introduce CompMap, a self-contained method in Python that generates allele-specific expression counts from genotype-specific alignments. CompMap sorts and counts reads, not just SNPs, by comparing read-mapping statistics to parental alignments within homologous regions. Ambiguous alignments resolve proportionally to allele-specific counts or statistically using a binomial distribution. Simulations with CompMap show low error rates in assessing regulatory divergence.
作用于基因的基因调控变化可以通过种间和种群间杂种及其亲本的等位基因特异性表达（ASE）转录组来推断。然而，繁琐的分析流程可能会无意中产生映射偏差和过多信息丢失的问题。我们引入了CompMap，这是一种用Python编写的独立方法，可从基因型特异性比对中生成等位基因特异性表达计数。CompMap通过将读取映射统计信息与同源区域内的亲本比对进行比较，不仅对单核苷酸多态性（SNP）进行排序和计数，还对读取进行排序和计数。模糊比对根据等位基因特异性计数按比例解析，或使用二项分布进行统计解析。使用CompMap进行的模拟显示，在评估调控差异时错误率较低。
