Deciphering genetic regulatory codes: A challenge for functional genomics

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.

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