Abstract not found

Rhodopseudomonas palustris, an�-proteobacterium, carries out three of the chemical reactions that support life on this planet: the conversion of sunlight to chemical-potential energy; the absorption of carbon dioxide, which it converts to cellular material; and the fixation of atmospheric nitrogen into ammonia. Insight into the transcription-regulatory network that coordinates these processes is fundamental to understanding the biology of this versatile bacterium. With this goal in mind, we predicted regulatory signals genomewide, using a two-step phylogenetic-footprinting and clustering process that we had developed previously. In the first step, 4,963 putative transcription factor binding sites, upstream of 2,044 genes and operons, were identified using cross-species Gibbs sampling. Bayesian motif clustering was then employed to group the cross-species motifs into regulons. We have identified 101 putative regulons in R. palustris, including 8 that are of particular interest: a photosynthetic regulon, a flagellar regulon, an organic hydroperoxide resistance regulon, the LexA regulon, and four regulons related to nitrogen metabolism (FixK 2, NnrR, NtrC, and � 54). In some cases, clustering allowed us to assign functions to proteins that previously had been annotated with only putative functions; we have identified RPA0828 as the organic hydroperoxide resistance regulator and RPA1026 as a cell cycle methylase. In addition to predicting regulons, we identified a novel inverted repeat that likely forms a highly conserved stem-loop and that occurs downstream of over 100 genes.

Identifying transcription factor binding sites

www.elsevier.com/locate/ybbrc

identification of transcription factor targets from gene expression data

identification of transcription factor targets from gene expression data

Motivation: An important step in unravelling the transcriptional regulatory network of an organism is to identify, for each transcription factor, all of its DNA binding sites. Several approaches are commonly used in searching for a transcription factor’s binding sites, including consensus sequences and position-specific scoring matrices. In addition, methods that compute the average number of nucleotide matches between a putative site and all known sites can be employed. Such basic approaches can all be naturally extended by incorporating pairwise nucleotide dependencies and per-position information content. In this paper, we evaluate the effectiveness of these basic approaches and their extensions in finding binding sites for a transcription factor of interest without erroneously identifying other genomic sequences. Results: In cross-validation testing on a dataset of Escherichia coli transcription factors and their binding sites, we show that there are statistically significant differences in how well various methods identify transcription factor binding sites. The use of per-position information content improves the performance of all basic approaches. Furthermore, including local pairwise nucleotide dependencies within binding site models results in statistically significant performance improvements for approaches based on nucleotide matches. Based on our analysis, the best results when searching for DNA binding sites of a particular transcription factor are obtained by methods that incorporate both information content and local pairwise correlations. Availability: The software is available at

Full open access to this and thousands of other papers at

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.

In proteobacteria, genes whose expression is modulated in response to the external concentration of inorganic phosphate are often regulated by the PhoB protein which binds to a conserved motif (Pho box) within their promoter regions. Using a position weight matrix algorithm derived from known Pho box sequences, we identified 96 putative Pho regulon members whose promoter regions contained one or more Pho boxs in the Sinorhizobium meliloti genome. Expression of these genes was examined through assays of reporter gene fusions and through comparison with published microarray data. Of 96 genes, 31 were induced and 3 were repressed by Pi starvation in a PhoB dependent manner. Novel Pho regulon members included several genes of unknown function. Comparative analysis across 12 proteobacterial genomes revealed highly conserved Pho regulon members including genes involved in Pi metabolism (pstS, phnC and ppdK). Genes with no obvious association with Pi metabolism were predicted to be Pho regulon members in S.meliloti and multiple organisms. These included smc01605 and smc04317 which are annotated as substrate binding proteins of iron transporters and katA encoding catalase. This data suggests that the Pho regulon overlaps and interacts with several other control circuits, such as the oxidative stress response and iron homeostasis.