High-resolution computational models of genome binding events.
Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.
Qi
Yuan
Y
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA.
Rolfe
Alex
A
MacIsaac
Kenzie D
KD
Gerber
Georg K
GK
Pokholok
Dmitry
D
Zeitlinger
Julia
J
Danford
Timothy
T
Dowell
Robin D
RD
Fraenkel
Ernest
E
Jaakkola
Tommi S
TS
Young
Richard A
RA
Gifford
David K
DK
D001483:Base Sequence; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry*; D004268:DNA-Binding Proteins / Q000737:chemistry*; D008956:Models, Chemical; D008957:Models, Genetic; D008958:Models, Molecular; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000737:chemistry*
D004268:DNA-Binding Proteins; D014157:Transcription Factors; D004247:DNA