Computational methods for chromatin data

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每个真核基因的转录。充分了解基因调控需要染色质的详细性质的知识，包括相互作用的蛋白质，在广泛的实验条件。关于染色质性质的数据来自不断增加的各种基因组测定。处理这些数据的计算方法没有跟上，既没有可用数据类型的多样性，也没有基因组学其他领域软件的能力。对于染色质数据的更好的计算方法存在未满足的需求。使用特定的基因组测序分析，我们可以询问个别染色质的性质。例如，染色质免疫沉淀测序（ChIP-seq）和靶下切割以及使用核酸酶释放（CUT&RUN）测定确定DNA相关蛋白的位置。转座酶可及染色质测序（ATAC-seq）测定确定开放染色质的位置。这些方法有两个主要的共同点。首先，这些测定以DNA序列读数的形式产生原始数据。其次，可以通过计数这些读数映射到参考基因组的任何区域的频率来确定该区域的染色质性质。这个过程将原始测序读数转换为整个基因组中某些特性的信号图。这项研究计划的目的是将来自染色质测定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CUT&RUN定量和峰调用的计算方法，使用加标对照。 2.通过使用Segway对许多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Virtual ChIP-seq从现成的数据预测难以测量的染色质特性。 申请人。Michael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规培训。他创造了几种有影响力的方法，这些方法导致了多篇高引用论文和可持续的表观基因组学软件被世界各地的研究人员每天使用。 冲击这项研究计划将导致新的方法来分析染色质数据和更好地了解基因调控。它将产生易于使用，免费提供的软件和注释，加速基因组学社区对染色质数据的可重复分析。

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.