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 HM 和 TF 都可以调节类型特异性基因表达程序和染色质结构。 发病机制中的异常调节是癌细胞依赖性的主要类别。 HM 和 TF 在全基因组范围内的结合对于更好地理解转录调控至关重要。 目标下切割和标记 (CUT&Tag) 是一种简单且低成本的表观基因组分析方法， 可以在少量细胞甚至数千个 CUT&Tag 数据集上执行。 自该技术出现以来，已经生成了用于分析 TF 结合位点和 HM 的 功能基因组学和疾病研究的宝贵资源依赖于 CUT&Tag 实验。 用于标签化的高活性转座酶 Tn5 受到内在序列插入偏差的影响，并且 Tn5 捕获的读数向染色质可及性区域的富集也混淆了 CUT&Tag 读取，特别是对于与染色质可及性关联较弱的因子，这两个功能都带来了。 CUT&Tag 数据中的巨大偏差会混淆数据分析，例如强 CUT&Tag 信号。 在转录活跃的基因处可以观察到抑制性组蛋白修饰 H3K27me3 的富集 启动子区域的染色质可公开访问，但 ChIP-seq 没有​​ H3K27me3 信号，表明 观察到的 CUT&Tag 信号可能是假阳性单细胞数据的高稀疏特性。 与批量数据相比，固有偏差更加严重，从而带来了额外的挑战 例如，计算模型和数据分析的平均 Tn5 内在裂解偏差水平各不相同。 跨单个细胞并混淆单细胞 ATAC-seq 数据的细胞聚类结果，该数据携带 与 CUT&Tag 类似的 Tn5 内在偏差 基于这些初步观察和我们小组的现有工作， 我们建议开发计算模型来准确量化开放染色质偏差和 Tn5 使用新模型，可以从批量和单细胞水平上的 CUT&Tag 数据中获得内在的裂解偏差。 开发后，我们将描述开放染色质和内在切割偏差如何影响 HM 的检测 批量和单细胞水平 CUT&Tag 数据中的 TF 结合位点和 TF 结合位点 偏差校正模型可以进一步进行。 纳入现有或新的生物信息学方法来检测 HM/TF 信号，无论是批量信号还是单信号信号 该项目专注于开发一种用于偏差校正的计算方法。 改进 CUT&Tag 数据分析。所提出的计算方法补充了现有的生物信息学。 工具并将在功能基因组学和表观基因组学研究中具有广泛的应用。 拟议的工作将填补染色质动力学和转录单细胞研究的知识空白 监管，并可以为基础科学和转化研究提供机制见解。