We conducted a comprehensive genomic analysis of the temporal response of yeast to anaerobiosis (six generations) and subsequent aerobic recovery (�2 generations) to reveal metabolic-state (galactose versus glucose)-dependent differences in gene network activity and function. Analysis of variance showed that far fewer genes responded (raw P value of <10 �8) to the O 2 shifts in glucose (1,603 genes) than in galactose (2,388 genes). Gene network analysis reveals that this difference is due largely to the failure of “stress”-activated networks controlled by Msn2/4, Fhl1, MCB, SCB, PAC, and RRPE to transiently respond to the shift to anaerobiosis in glucose as they did in galactose. After �1 generation of anaerobiosis, the response was similar in both media, beginning with the deactivation of Hap1 and Hap2/3/4/5 networks involved in mitochondrial functions and the concomitant derepression of Rox1-regulated networks for carbohydrate catabolism and redox regulation and ending (>2 generations) with the activation of Upc2- and Mot3-regulated networks involved in sterol and cell wall homeostasis. The response to reoxygenation was rapid (<5 min) and similar in both media, dominated by Yap1 networks involved in oxidative stress/redox regulation and the concomitant activation of heme-regulated ones. Our analyses revealed extensive networks of genes subject to combinatorial regulation by both heme-dependent (e.g., Hap1, Hap2/3/4/5, Rox1, Mot3, and Upc2) and heme-independent (e.g., Yap1, Skn7, and Puf3) factors under these conditions. We also uncover novel functions for several

Classifying transcription factor targets and discovering relevant biological features

Identification of novel regulatory modules in dicotyledonous plants using expression data and comparative genomics

Genome-wide identification of the regulatory targets of a transcription factor using biochemical characterization and computational genomic analysis

identification of functional motifs in temporal gene expression analysis

Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. The availability of complete genome sequence from multiple organisms, as well as mRNA profiling and high-throughput experimental methods for mapping protein-binding sites in DNA, have contributed to the development of methods that utilize these auxiliary data to inform the detection of transcriptional regulatory elements. Progress is also being made in the identification of cis-regulatory modules and higher order structures of the regulatory sequences, which is essential to the understanding of transcription regulation in the metazoan genomes. This article reviews the computational approaches for modeling and identification of genomic regulatory elements, with an emphasis on the recent developments, and current challenges.

context after gene duplication

Research article WeederH: an algorithm for finding conserved regulatory motifs and regions in homologous sequences