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

TL;DR

This paper explores computational methods to identify gene regulatory elements by integrating sequence, expression, and interactome data, aiming to improve annotation of uncharacterized genomic regions.

Contribution

It introduces a multi-modal data integration approach using statistical learning to predict regulatory enhancers, exemplified by the Gata2 gene case study.

Findings

High-throughput assays provide valuable features for enhancer prediction.

Integrated data modalities improve the accuracy of regulatory element identification.

Specific features from genomic and expression data are most discriminatory.

Abstract

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 \textit{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 (\emph{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 (\cite{Kleinjan2005},\cite{Lieb2006}. The computational annotation of these hitherto uncharacterized regions would require an identification of features that have good predictive value. In this work, we study transcriptional regulation as a problem in heterogeneous data integration, across sequence, expression and interactome level attributes. Using the example of the \textit{Gata2} gene and its recently discovered urogenital enhancers \cite{Khandekar2004} as a case study, we examine the predictive value of various high throughput functional genomic assays (from projects like ENCODE and SymAtlas) in characterizing these enhancers and their regulatory role. Observing results from the application of modern statistical learning methodologies for each of these data modalities, we propose a set of features that are most discriminatory to find these enhancers.