Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Bioinformatics. 2020 Feb 15;36(4):1270-1272. doi: 10.1093/bioinformatics/btz720.
The traditional reads per million normalization method is inappropriate for the evaluation of ChIP-seq data when treatments or mutations have global effects. Changes in global levels of histone modifications can be detected with exogenous reference spike-in controls. However, most ChIP-seq studies overlook the normalization that must be corrected with spike-in. A method that retrospectively renormalizes datasets without spike-in is lacking.
ChIPseqSpikeInFree is a novel ChIP-seq normalization method to effectively determine scaling factors for samples across various conditions and treatments, which does not rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. Application of ChIPseqSpikeInFree on five datasets demonstrates that this in silico approach reveals a similar magnitude of global changes as the spike-in method does.
St. Jude Cloud (https://pecan.stjude.cloud/permalink/spikefree) and St. Jude Github ( https://github.com/stjude/ChIPseqSpikeInFree).
Supplementary data are available at Bioinformatics online.
当处理或突变具有全局效应时,传统的每百万读数标准化方法不适合评估 ChIP-seq 数据。通过外源性参考掺入对照可以检测组蛋白修饰的全局水平变化。然而,大多数 ChIP-seq 研究忽略了必须用掺入物进行校正的标准化。缺乏一种可以回溯性地对没有掺入物的数据集进行重新归一化的方法。
ChIPseqSpikeInFree 是一种新颖的 ChIP-seq 标准化方法,可有效地确定各种条件和处理下样本的缩放因子,而无需依赖外源性掺入染色质或峰检测来揭示组蛋白修饰占有率的全局变化。在五个数据集上应用 ChIPseqSpikeInFree 表明,这种计算机方法揭示了与掺入方法相似的全局变化幅度。
圣裘德云(https://pecan.stjude.cloud/permalink/spikefree)和圣裘德 Github(https://github.com/stjude/ChIPseqSpikeInFree)。
补充数据可在 Bioinformatics 在线获得。