Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model

• Published in: Genome research 2002-07, Vol.12 (7), p.1019-1028
• Main Authors: Halfon, Marc S, Grad, Yonatan, Church, George M, Michelson, Alan M
• Format: Article
• Language: English
• Subjects: 3' Flanking Region - genetics; Animals; Bacterial Proteins; Base Composition - genetics; Base Sequence; Chromosome Mapping; Combinatorial Chemistry Techniques - methods; Computational Biology - methods; Conserved Sequence - genetics; DNA-Binding Proteins - genetics; Drosophila - genetics; Drosophila melanogaster - genetics; Drosophila Proteins - genetics; Enhancer Elements, Genetic - genetics; Genes, Insect - genetics; Homeodomain Proteins - genetics; MEF2 Transcription Factors; Mesoderm - chemistry; Mesoderm - metabolism; Models, Genetic; Molecular Sequence Data; Myogenic Regulatory Factors; Regulatory Sequences, Nucleic Acid - genetics; Repressor Proteins - genetics; Transcription Factors - genetics
• ISSN: 1088-9051; 1054-9803; 1549-5469
• DOI: 10.1101/gr.228902

Gene expression is regulated by transcription factors that interact with cis-regulatory elements. Predicting these elements from sequence data has proven difficult. We describe here a successful computational search for elements that direct expression in a particular temporal-spatial pattern in the Drosophila embryo, based on a single well characterized enhancer model. The fly genome was searched to identify sequence elements containing the same combination of transcription factors as those found in the model. Experimental evaluation of the search results demonstrates that our method can correctly predict regulatory elements and highlights the importance of functional testing as a means of identifying false-positive results. We also show that the search results enable the identification of additional relevant sequence motifs whose functions can be empirically validated. This approach, combined with gene expression and phylogenetic sequence data, allows for genome-wide identification of related regulatory elements, an important step toward understanding the genetic regulatory networks involved in development.