Abstract 7435: PATTY: A bias estimation and correction model for bulk and single-cell CUT&Tag data

Abstract The accurate detection of transcription factor (TF) binding sites and histone modifications (HM) on the genome-wide scale is essential for studying functional epigenetics and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is a low-cost and easy-to-implement epigenomic profiling method that can be performed on a low number of cells and on the single-cell level. CUT&Tag experiments use the hyperactive transposase Tn5 for tagmentation. We find that Tn5 is subject to intrinsic sequence insertion bias (intrinsic bias). Additionally, the preference of Tn5 insertion toward accessible chromatin also affects the distribution of CUT&Tag reads (open chromatin bias). Both types of biases can significantly confound bulk and single-cell CUT&Tag data analysis, which requires careful assessment and new analytical methods. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a computational method for systematic characterization and correction of biases in CUT&Tag data. We demonstrate that histone modification signals (H3K27me3 and H3K27ac) detected from CUT&Tag data after bias correction using PATTY are better associated with orthogonal biological features such as gene expression. PATTY-corrected single-cell CUT&Tag signals for histone modification can better cluster cells into their true cell types. This new computational method can improve both bulk and single-cell CUT&Tag data analysis. Citation Format: Sheng'en Shawn Hu, Qingying Chen, Megan Grieco, Chongzhi Zang. PATTY: A bias estimation and correction model for bulk and single-cell CUT&Tag data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7435.