A computational approach to regulatory element discovery in eukaryotes

TL;DR

This paper introduces a new computational method to identify regulatory elements in eukaryotic genomes by analyzing gene sets with shared motifs and their expression levels, successfully recognizing known motifs and proposing new candidates.

Contribution

The paper presents a novel computational approach that links overrepresented upstream motifs with gene expression data to discover regulatory elements in eukaryotes.

Findings

Successfully identified known binding motifs in yeast

Proposed a new candidate regulatory motif for experimental validation

Demonstrated the method's effectiveness with yeast microarray data

Abstract

Gene regulation in Eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of Eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: if this level is significantly higher or lower than the average taken over the whole genome, then the overrepresented motif shared by the genes in the set is likely to play a role in their regulation. We illustrate the method by applying it to the genome of {\it S. cerevisiae}, for which many datasets of microarray experiments are publicly available. Several known binding motifs are correctly recognized by our algorithm, and a new candidate is suggested for experimental verification.