Computational Analyses of ChIP-chip Experiments on Affymetrix Tiling Arrays

Dr. Xiaole Liu
Harvard University

Room 610, CBI, New Life Science Building, PKU
10:00-11:00 AM, Monday, 4 July,2005

Abstract:
Motivation: Transcription factors (TFs) regulate gene
expression by recognizing and binding to specific regulatory regions on the genome, which in higher eukaryotes can occur far away from the regulated genes. Recently Affymetrix developed the high-density oligonucleotide arrays that tile all the non-repetitive sequences of the human genome at 35-bp resolution. This new array platform allows for the unbiased mapping of in vivo TF binding sequences (TFBSs) using Chromatin ImmunoPrecipitation followed by microarray experiments (ChIP-chip). The massive data generated from these experiments pose great challenges for data analysis.
Results: We developed a fast, scalable and sensitive method to extract TFBSs from ChIP-chip experiments on genome tiling arrays. Our method takes advantage of tiling array data from many experiments to normalize and model the behavior of each individual probe, and identifies TFBSs using a Hidden Markov Model (HMM). When applied to the data of p53 ChIP- chip experiments (Cawley et al., 2004), our method discovered many new high confidence p53 targets including all the regions verified by quantitative PCR. Using a de novo motif finding algorithm MDscan (Liu et al., 2002), we also recovered the p53 motif from our HMM identified p53 target regions. Furthermore, we found substantial p53 motif enrichment in these regions comparing with both genomic background and the TFBSs identified by Cawley et al (2004). Several of the newly identified p53 TFBSs are in known genes' promoter regions or associated with previous characterized p53-responsive genes.

Bios:
Dr. Liu is Assistant Professor in the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute and Harvard School of Public Health. She has expertise in computational sequence analysis, especially transcription factor motif finding. She designed a number of influential motif-finding algorithms such as BioProspector (Liu et al. PSB 2001) for co-expression clusters, MDscan (Liu et al. Nat. Biotech. 2002) for ChIP-chip experiments, Motif Regressor (Conlon et al. PNAS 2003) for association of motif effect with expression changes, and CompareProspector (Liu et al. Genome Res. 2004) for comparative motif analysis in higher eukaryotes. She has extensive collaboration experience, especially on ChIP-chip analysis and has produced high quality results with laboratories of Patrick Brown (Lieb et al. Nat. Gen. 2001), Richard Losick (Ben-Yehuda et al. Mol. Cel. 2005), Myles Brown (Carroll et al, Cell 2005), and Pam Silver (Brodsky et al, Genome Bio. 2005).