Computational methods for chromatin data

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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.