A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data.
BACKGROUND
Genome-wide chromatin-immunoprecipitation (ChIP-chip) detects binding of transcriptional regulators to DNA in vivo at low resolution. Motif discovery algorithms can be used to discover sequence patterns in the bound regions that may be recognized by the immunoprecipitated protein. However, the discovered motifs often do not agree with the binding specificity of the protein, when it is known.


RESULTS
We present a powerful approach to analyzing ChIP-chip data, called THEME, that tests hypotheses concerning the sequence specificity of a protein. Hypotheses are refined using constrained local optimization. Cross-validation provides a principled standard for selecting the optimal weighting of the hypothesis and the ChIP-chip data and for choosing the best refined hypothesis. We demonstrate how to derive hypotheses for proteins from 36 domain families. Using THEME together with these hypotheses, we analyze ChIP-chip datasets for 14 human and mouse proteins. In all the cases the identified motifs are consistent with the published data with regard to the binding specificity of the proteins.
Macisaac
Kenzie D
KD
MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.
Gordon
D Benjamin
DB
Nekludova
Lena
L
Odom
Duncan T
DT
Schreiber
Joerg
J
Gifford
David K
DK
Young
Richard A
RA
Fraenkel
Ernest
E
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D006801:Humans; D051379:Mice; D008969:Molecular Sequence Data; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics*
D014157:Transcription Factors