RT Journal
A1 Hashimoto, Tatsunori
A1 Sherwood, Richard I.
A1 Kang, Daniel D.
A1 Rajagopal, Nisha
A1 Barkal, Amira A.
A1 Zeng, Haoyang
A1 Emons, Bart J.M.
A1 Srinivasan, Sharanya
A1 Jaakkola, Tommi
A1 Gifford, David K.
T1 A synergistic DNA logic predicts genome-wide chromatin accessibility
JF Genome Research 
JO Genome Research 
YR 2016 
FD October 01 
VO 26 
IS 10 
SP 1430 
OP 1440 
DO 10.1101/gr.199778.115 
UL http://genome.cshlp.org/content/26/10/1430.abstract 
AB 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.