부산대학교 도서관

Motivation 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. Results 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. Availability and implementation St. Jude Cloud (https://pecan.stjude.cloud/permalink/spikefree) and St. Jude Github (https://github.com/stjude/ChIPseqSpikeInFree). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]