A multi-level bias correction model for bulk and single-cell CUT&Tag data

用于批量和单细胞切割的多级偏差校正模型

• 批准号: 10645980
• 负责人: Chongzhi Zang
• 金额: $ 44.41万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10645980
• 关键词: ATAC-seq; Affect; Basic Science; Benchmarking; Binding; Binding Sites; Bioinformatics; Biological; Biological Assay; Cells; ChIP-seq; Characteristics; Chromatin; Chromatin Structure; Complement; Computer Models; Computer software; Computing Methodologies; DNA Insertion Elements; DNA-Protein Interaction; Data; Data Analyses; Data Set; Dependence; Detection; Disease; Gene Expression; Gene Expression Regulation; Genetic Transcription; Genome; Goals; Human Cell Line; Human Genome; Hyperactivity; Individual; Knowledge; Measures; Methods; Modeling; Names; Pathogenesis; Promoter Regions; Research; Resources; Signal Transduction; Statistical Models; Techniques; Tn5 transposase; Transcriptional Regulation; Work; bioinformatics tool; cancer cell; computer framework; cost; data resource; epigenomic profiling; epigenomics; experimental study; functional genomics; genome-wide; genome-wide analysis; histone modification; human data; improved; innovation; insight; method development; open source; programs; transcription factor; transcriptome sequencing; translational study

Histone modifications (HM) and transcription factors (TF) are key factors in maintaining the cell identity by regulating the type-specific gene expression program and chromatin structure. Both HMs and TFs can be aberrantly regulated in the pathogenesis 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 transcriptional regulation. 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 or even on the single-cell level. Thousands of CUT&Tag datasets have been generated for profiling TF binding sites and HMs since the advent of this technique, providing a valuable resource for functional genomics and disease research. CUT&Tag experiments rely on the hyperactive transposase Tn5 for tagmentation. Tn5 is subject to intrinsic sequence insertion biases, and enrichment of Tn5 captured reads toward chromatin accessibility regions also confound the distribution of CUT&Tag reads, especially for factors with weak association with chromatin accessibility. Both features bring great biases in the CUT&Tag data that confound the data analysis. For example, Strong CUT&Tag signal enrichment of repressive histone modification H3K27me3 can be observed at actively transcribed gene promoter regions where chromatin is openly accessible but no H3K27me3 signal from ChIP-seq, indicating that the observed CUT&Tag signal is likely false positive. The high-sparsity characteristics of single-cell data makes the intrinsic biases more substantial compared to bulk data, creating additional challenges in computational modeling and data analysis. For example, the average Tn5 intrinsic cleavage bias level varies across individual cells and confound the cell clustering result from single-cell ATAC-seq data, which carries similar Tn5 intrinsic bias as CUT&Tag. Based on these preliminary observations and our group’s existing work, we propose to develop computational models to accurately quantify both the open chromatin bias and the Tn5 intrinsic cleavage bias from CUT&Tag data on both bulk and single-cell levels. Using the new model to be developed, we will characterize how open chromatin and intrinsic cleavage biases affect the detection of HM and TF binding sites in both bulk and single-cell level CUT&Tag data. The bias correction model can be further incorporated in existing or new bioinformatics methods to detect the HM/TF signals, for both bulk and single- cell CUT&Tag data. this project focuses on developing a computational method for bias correction for improving CUT&Tag data analysis. The proposed computational method complements existing bioinformatics tools and will have broad applications in functional genomics and epigenomics research. The results from the proposed work will fill the knowledge gap in single-cell studies of chromatin dynamics and transcriptional regulation and could provide mechanistic insights for both basic science and translational studies.

组蛋白修饰（HM）和转录因子（TF）是维持细胞身份的关键因素 调节类型特异性基因表达程序和染色质结构。 HMS和TF都可以 在发病机理中受到异常调节，是一类主要的癌细胞依赖性。精确检测 HM和TF结合基因组的范围对于更好地理解转录调节至关重要。 目标和标签下的裂解（剪切和标签）是一种简单且低成本的表观基因组分析方法 可以在较少的单元格甚至单细胞水平上执行。成千上万的剪切数据集 自从这项技术冒险以来，已经生成用于分析TF绑定位点和HMS的生成 功能基因组学和疾病研究的宝贵资源。剪切和标记实验依赖 用于标记的多动活跃转座酶TN5。 TN5受到内在序列插入偏见的约束，并且 捕获的TN5的富集对染色质可及性区域的读取也混淆了分布 剪切和标签读取，尤其是与染色质可及性相关性较弱的因素。这两个功能带来 混淆数据分析的剪切和标签数据中的巨大偏见。例如，强切和标记信号 在积极转录的基因上，可以观察到反射性组蛋白修饰H3K27me3的富集 启动子区域公开访问染色质，但没有chip-seq的H3K27me3信号，表明 观察到的切割和标签信号可能是假阳性。单细胞数据的高标准特性 与批量数据相比，使内在的偏见更加强大，从而在 计算建模和数据分析。例如，平均TN5固有切割偏置水平变化 跨单个单元格，并混淆单细胞ATAC-SEQ数据产生的细胞聚类，该数据是汽车的 类似的TN5固有偏置与剪切和标签。基于这些初步观察以及我们小组的现有工作， 我们建议开发计算模型，以准确量化开放的染色质偏差和TN5 从散装和单细胞水平上的切割和标记数据中的内在切割偏置。使用新模型为 开发，我们将表征开放的染色质和内在裂解偏见如何影响HM的检测 和TF绑定位点，包括批量和单细胞级别剪切和标签数据。偏置校正模型可以进一步 纳入现有或新的生物信息学方法，用于检测HM/TF信号 细胞切割和标签数据。该项目着重于开发用于偏见校正的计算方法 改进剪切和标签数据分析。提出的计算方法完成现有的生物信息学 工具将在功能基因组学和表观基因组学研究中广泛应用。来自 拟议的工作将填补染色质动力学和转录的单细胞研究中的知识差距 调节，可以为基础科学和翻译研究提供机械见解。