The advent of affordable high-throughput sequencing has resulted in tremendous advances in our understanding of transcriptional regulation. This talk will focus on the types of questions that we can now ask using ChIP-seq, and on some of the most useful algorithms and approaches for answering them. We will illustrate these using our recently published analysis of the role of transcription factor Klf1 in the development of red blood cells. We will discuss identifying the targeted genes, targeted genomic regions, biological roles, DNA-binding affinity, co-factors and protein-complex partners of the ChIP-ed transcription factor. To aid in accomplishing this last task, we will describe SpaMo, an algorithm we recently developed for identifying transcription factor complexes from ChIP-seq data.

Timothy Bailey is a Senior Research Fellow at the Institute for Molecular Biosciences at the University of Queensland. His group develops computational approaches for studying the mechanisms gene expression. Current projects include the analysis ChIP data, utilization of epigenetic data for predicting regulation, and exploring the role of DNA-RNA triplex formation in regulation. This work funded by the Australian Research Council's Centre of Excellence in Bioinformatics, and by the NIH. Dr Bailey is known for developing the MEME motif discovery algorithm, as well as for his roles in creating a number of widely-used motif-based sequence analysis tools (MAST, Meta-MEME, MCAST, GOMO, and Tomtom), all of which are integrated into the MEME Suite of tools (meme.nbcr.net). Dr Bailey received his Bachelors degree from Stanford University (Mathematical Sciences, 1977) and his PhD from UCSD (Computer Science, 1995). He worked in industry for more than a decade in computer graphics before beginning his career in computational biology.