Histone modifications (HM) and transcription factors (TF) are aberrantly regulated in the pathogenesis of humans and are a major class of cancer cell dependencies. Precise detection of HM and TF binding genome-wide is essential for a better understanding of cancer epigenetics. Cleavage Under Targets & Tagmentation (CUT&Tag) is an easy and low-cost epigenomic profiling method that can be performed on a low number of cells and on the single-cell level. A large number of CUT&Tag datasets have been generated in cancer samples, providing a valuable resource. Unbiased analysis of CUT&Tag data is important for cancer epigenetics research. CUT&Tag experiments rely on the hyperactive transposase Tn5 for tagmentation. We performed integrative analysis of CUT&Tag and other high-throughput data, and found that Tn5 is subject to intrinsic sequence insertion biases (intrinsic biases). Preference of Tn5 captured reads toward chromatin accessibility regions (open chromatin biases) also confound the distribution of CUT&Tag reads. Both sources of biases confound the analysis of CUT&Tag data, especially for factors that are not expected to associate with accessible chromatin. High sparsity of single-cell data makes the effect of both biases more substantial compared to bulk data. We present a computational model for accurate characterization and quantitative estimation of biases in CUT&Tag data. This model paved the way for further development of computational tools for improving both bulk and single-cell CUT&Tag data analysis.

Citation Format: Sheng'en Hu, Qingying Chen, Chongzhi Zang. Accurate estimation of open chromatin and intrinsic biases in bulk and single-cell CUT&Tag data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 871.