Su Dingwen, Peters Moritz, Soltys Volker, Chan Yingguang Frank
Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, 72076, Germany.
University of Groningen, Groningen Institute of Evolutionary Life Sciences (GELIFES), Groningen, 9747 AG, The Netherlands.
BMC Genomics. 2025 Mar 28;26(1):306. doi: 10.1186/s12864-025-11442-y.
A common objective across ATAC-seq and ChIP-seq analyses is to identify differential signals across contrasted conditions. However, in differential analyses, the impact of copy number variation is often overlooked. Here, we demonstrated copy number differences among samples could drive, if not dominate, differential signals. To address this, we propose a pipeline featuring copy number normalization. By comparing the averaged signal per gene copy, it effectively segregates differential signals driven by copy number from other factors. Further applying it to Down syndrome unveiled distinct dosage-dependent and -independent changes on chromosome 21. Thus, we recommend copy number normalization as a general approach.
在全基因组转座酶可接近染色质测序（ATAC-seq）和染色质免疫沉淀测序（ChIP-seq）分析中，一个共同目标是识别不同条件下的差异信号。然而，在差异分析中，拷贝数变异的影响常常被忽视。在此，我们证明样本间的拷贝数差异即便不能主导，也会驱动差异信号。为解决这一问题，我们提出了一个以拷贝数归一化为特色的流程。通过比较每个基因拷贝的平均信号，它能有效区分由拷贝数驱动的差异信号和其他因素导致的差异信号。进一步将其应用于唐氏综合征研究，揭示了21号染色体上不同的剂量依赖性和非依赖性变化。因此，我们推荐将拷贝数归一化作为一种通用方法。
