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In the past two decades, considerable effort has been devoted to elucidating the mechanisms of transcriptional regulation in metazoans. A number of fundamental principles have been established concerning the functions of many transcription factors (TFs) and the cis-acting sequences to which they bind (1). One hypothesis that has emerged from these studies is that genes with similar temporal and spatial expression patterns are subject to a common regulatory logic. That is, unique “transcriptional codes” govern the activation and repression of genes in particular developmental contexts (2, 3). However, because of the laborious nature of cis-regulatory sequence dissection, few comprehensive examples exist to support this concept. A more efficient approach to the identification of coexpressed genes and their associated regulatory elements would accelerate this field greatly. The availability of complete human and model organism genome sequences represents a tremendous windfall for those interested in this problem. Two papers appearing in this issue of PNAS (4, 5) exemplify how a marriage between computational and experimental biology can yield a powerful approach for exploiting genomic information to predict and validate new genes, the expression of which are subject to similar transcriptional codes. With a purely computational approach, uncertainty remains as to whether a predicted CRM actually possesses the expected function. The complex spatial and temporal patterns of gene expression occurring in development are orchestrated by cis-acting regulatory modules (CRMs) or enhancers (2, 3). CRMs comprise sets of short oligonucleotide motifs, each of which has an affinity for one or more sequence-specific DNA-binding proteins or TFs. TFs in turn interact with each other and the basal transcriptional machinery to either activate or repress the expression of coding regions associated with the particular CRM. A recurring theme in the organization of CRMs is their ability to integrate multiple convergent inputs through the …

Deciphering genetic regulatory codes: A challenge for functional genomics