TY  - JOUR
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
Y1  - 2016/10/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1430 
EP  - 1440 
DO  - 10.1101/gr.199778.115 
VL  - 26 
IS  - 10 
UR  - http://genome.cshlp.org/content/26/10/1430.abstract 
N2  - 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. 
ER  -