title: A synergistic DNA logic predicts genome-wide chromatin accessibility.
abstract: Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that log-additive cis-acting DNA sequence features can predict chromatin accessibility at high spatial resolution. We develop a new type of high-dimensional machine learning model, the Synergistic Chromatin Model (SCM), which when trained with DNase-seq data for a cell type is capable of predicting expected read counts of genome-wide chromatin accessibility at every base from DNA sequence alone, with the highest accuracy at hypersensitive sites shared across cell types. We confirm that a SCM accurately predicts chromatin accessibility for thousands of synthetic DNA sequences using a novel CRISPR-based method of highly efficient site-specific DNA library integration. SCMs are directly interpretable and reveal that a logic based on local, nonspecific synergistic effects, largely among pioneer TFs, is sufficient to predict a large fraction of cellular chromatin accessibility in a wide variety of cell types.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: Tatsunori
authors.initials: T
authors.lastname: Hashimoto
authors.affiliation: Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Richard I
authors.initials: RI
authors.lastname: Sherwood
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: Daniel D
authors.initials: DD
authors.lastname: Kang
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: Nisha
authors.initials: N
authors.lastname: Rajagopal
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Amira A
authors.initials: AA
authors.lastname: Barkal
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: Haoyang
authors.initials: H
authors.lastname: Zeng
authors.affiliation: Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Bart J M
authors.initials: BJ
authors.lastname: Emons
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Sharanya
authors.initials: S
authors.lastname: Srinivasan
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: Tommi
authors.initials: T
authors.lastname: Jaakkola
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
authors.forename: David K
authors.initials: DK
authors.lastname: Gifford
mesh_terms: D000818:Animals; D002843:Chromatin / Q000235:genetics* / Q000378:metabolism; D042002:Chromatin Assembly and Disassembly; D015894:Genome, Human; D006801:Humans; D000069550:Machine Learning; D008957:Models, Genetic
chemical_list: D002843:Chromatin