Understanding Transcriptional Regulation Using De-novo Sequence Motif Discovery, Network Inference and Interactome Data

Gene regulation is a complex process involving the role of several genomic elements which work in concert to drive spatio-temporal expression. The experimental characterization of gene regulatory elements is a very complex and resource-intensive process. One of the major goals in computational biology is the in-silico annotation of previously uncharacterized elements using results from the subset of known, previously annotated, regulatory elements. The recent results of the ENCODE project (http://encode.nih.gov) presented in-depth analysis of such functional (regulatory) non-coding elements for 1% of the human genome. It is hoped that the results obtained on this subset can be scaled to the rest of the genome. This is an extremely important effort which will enable faster dissection of other functional elements in key biological processes such as disease progression and organ development ([20],[25]. The computational annotation of these hitherto uncharacterized regions would require an identification of features that have good predictive value. Gene regulation in higher eukaryotes involves a complex interplay between the gene proximal promoter and distal elements (such as enhancers). Though the exact mechanism of gene regulation is not completely known, several data-driven models have been hypothesized to understand transcription, pointing to sequence, expression, transcription factor (TF) and their interactome level attributes, at the Arvind Rao and Alfred O. Hero, III are with the Departments of Electrical Engineering and Computer Science, and Bioinformatics at the University of Michigan, Ann Arbor, MI-48109, email: [ ukarvind, hero] @umich.edu James Douglas Engel is with the Department of Cell and Developmental Biology at the University of Michigan, Ann Arbor, MI-48109. David J. States is with the Departments of Bioinformatics and Human Genetics at the University of Michigan, Ann Arbor, MI-48109. A part of this work has been presented at the Computational Systems Bioinformatics (CSB) 2007 conference, as well as at the Statistical Signal Processing Workshop (SSP) 2007.