Computational methods for chromatin data

染色质数据的计算方法

• 批准号: RGPIN-2022-05134
• 负责人: Hoffman, Michael
• 金额: $ 3.5万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760720
• 关键词: Computational; methods; chromatin; data

Chromatin structure and the presence of associated proteins like transcription factors and histones control the transcription of each eukaryotic gene, directly or indirectly. Fully understanding gene regulation requires knowledge of detailed properties of chromatin, including interacting proteins, in a wide range of experimental conditions. Data on chromatin properties come from an ever-increasing variety of genomic assays. Computational methods for working with these data have not kept up-neither with the diversity of data types available nor the capabilities of software in other areas of genomics. There exists an unmet need for better computational methods for chromatin data. Using specific genomic sequencing assays, we can interrogate individual chromatin properties. For example, the chromatin immunoprecipitation-sequencing (ChIP-seq) and cleavage under targets and release using nuclease (CUT&RUN) assays determine the location of DNA-associated proteins. The assay for transposase-accessible chromatin-sequencing (ATAC-seq) determines the location of open chromatin. These methods have two major aspects in common. First, these assays produce raw data in the form of DNA sequence reads. Second, one can determine chromatin properties for any region of a reference genome by counting how often these reads map to that region. This process transforms the raw sequencing reads into a signal map of some property across the whole genome. The purpose of this research program is to transform raw data from chromatin assays into information that illuminates the workings of eukaryotic gene regulation and to enable others to do the same. Objectives. We will continue this research program through the following objectives: 1. Develop computational methods for CUT&RUN quantification and peak calling using spike-in controls. 2. Identify key genomic elements for gene regulation through integrative analysis of many datasets with Segway. 3. Predict difficult-to-measure chromatin properties from readily available data through Virtual ChIP-seq. Applicant. Dr. Michael Hoffman is a leader in computational methods for epigenomic analysis. He has formal training in computational genomics, machine learning, and biochemistry. He has created several influential methods which led to multiple highly cited papers and sustainable epigenomics software used daily by researchers around the world. Impact. This research program will lead to new methods for analysis of chromatin data and a better understanding of gene regulation. It will produce easy-to-use, freely available software and annotations, accelerating reproducible analysis of chromatin data by the genomics community.

染色质结构和相关蛋白质如转录因子和组蛋白的存在直接或间接地控制着每个真核基因的转录。充分了解基因调控需要了解染色质的详细性质，包括在广泛的实验条件下相互作用的蛋白质。有关染色质特性的数据来自越来越多的基因组分析。处理这些数据的计算方法没有跟上--既没有可用的数据类型的多样性，也没有基因组学其他领域的软件的能力。对染色质数据的更好的计算方法存在着尚未得到满足的需求。使用特定的基因组测序分析，我们可以询问单个染色质的特性。例如，染色质免疫沉淀测序(CHIP-SEQ)和靶标下裂解和核酸酶释放(CUT&RUN)分析确定了DNA相关蛋白质的位置。转座酶可及染色质测序分析(ATAC-SEQ)确定开放染色质的位置。这些方法有两个主要方面的共同点。首先，这些分析以DNA序列读取的形式产生原始数据。其次，人们可以通过计算这些读数映射到该区域的频率来确定参考基因组的任何区域的染色质属性。这一过程将原始的测序读数转化为整个基因组中某些属性的信号图谱。这项研究计划的目的是将染色质分析的原始数据转化为阐明真核基因调控工作的信息，并使其他人能够做同样的事情。目标。我们将通过以下目标继续这项研究计划：1.开发削减和运行量化的计算方法，以及使用尖峰控制的高峰呼叫。2.通过使用Segway对多个数据集进行综合分析，确定基因调控的关键基因组元件。3.通过虚拟芯片序列从现成的数据中预测难以测量的染色质属性。申请人。迈克尔·霍夫曼博士是表观基因组分析计算方法的领导者。他接受过计算基因组学、机器学习和生物化学方面的正式培训。他创造了几种有影响力的方法，这些方法导致了多篇高被引用的论文和世界各地研究人员每天使用的可持续表观基因组学软件。冲击力。这项研究计划将带来分析染色质数据的新方法，并更好地理解基因调控。它将产生易于使用、免费提供的软件和注释，加快基因组学社区对染色质数据的可重复分析。