The recent availability of the human and mouse genomes have shown that the vast majority of the genes are almost identical between human and mouse. The differences between human and mouse most likely stem from variations in the gene regulatory networks. However, much of this regulatory network is unknown. In fact, with the tremendous success of genome sequencing efforts and the numerous complete genome sequences available, much is unknown about the regulatory networks of any of these sequenced genomes. One of the main challenges facing researchers today is deciphering the regulatory mechanisms of genes. This challenge includes detecting the transcription factor binding sites involved in regulation as well as discovering the regulatory networks.
Automatic methods for helping decipher the regulatory mechanism are crucial for understanding the regulatory network. The core of these methods are the motif-finding algorithms that can help identify regulatory elements. However, many new challenges are presented when analyzing complete genomes. These challenges include scaling to very large samples, incorporating multiple genomes, detection of weak signals and incorporating different types of genomic data such as protein localization data and gene expression
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