Dataset Information

Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.

ABSTRACT: Genome-wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene-by-gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight matrices (WMs), we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, "PROCSE" (probabilistic clustering of sequences), uses Monte Carlo sampling of this distribution to partition and align thousands of short DNA sequences into clusters. The algorithm internally determines the number of clusters from the data and assigns significance to the resulting clusters. We place theoretical limits on the ability of any algorithm to correctly cluster sequences drawn from WMs when these WMs are unknown. Our analysis suggests that the set of all putative sites for a single genome (e.g., Escherichia coli) is largely inadequate for clustering. When sites from different genomes are combined and all the homologous sites from the various species are used as a block, clustering becomes feasible. We predict 50-100 new regulons as well as many new members of existing regulons, potentially doubling the number of known regulatory sites in E. coli.

SUBMITTER: van Nimwegen E

PROVIDER: S-EPMC124229 | biostudies-literature | 2002 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.

van Nimwegen Erik E Zavolan Mihaela M Rajewsky Nikolaus N Siggia Eric D ED

Proceedings of the National Academy of Sciences of the United States of America 20020501 11

Genome-wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene-by-gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight matrices (WMs), we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, "PROCSE" (probabilistic clustering of sequences), uses Monte Carlo sampling of this distribution to partition and align thousands ...[more]

PMID: 12032281

Similar Datasets

Project description:BACKGROUND: Clostridium perfringens is a Gram-positive anaerobic bacterium causing severe diseases such as gas gangrene and pseudomembranosus colitis, that are generally due to the secretion of powerful extracellular toxins. The expression of toxin genes is mainly regulated by VirR, the response regulator of a two-component system. Up to now few targets only are known for this regulator and mainly in one strain (Strain 13). Due to the high genomic and phenotypic variability in toxin production by different strains, the development of effective strategies to counteract C. perfringens infections requires methodologies to reconstruct the VirR regulon from genome sequences. RESULTS: We implemented a two step computational strategy allowing to consider available information concerning VirR binding sites in a few species to scan all genomes of the same species, assuming the VirR targets are at least partially conserved across these strains. Results obtained are in agreement with previous works where experimental validation of the promoters have been performed and showed the presence of a core and an accessory regulon of VirR in C. perfringens strains with three target genes also located on plasmids. Moreover, the type E strain JGS1987 has the largest predicted regulon with as many as 10 VirR targets not found in the other genomes. CONCLUSIONS: In this work we exploited available experimental information concerning the targets of the VirR toxin regulator in one C. perfringens strain to obtain plausible predictions concerning target genes in genomes and plasmids of nearby strains. Our predictions are available for wet-lab researchers working on less characterized C. perfringens strains that can thus design focused experiments reducing the search space of their experiments and increasing the probability of characterizing positive targets with less efforts. Main result was that the VirR regulon is variable in different C. perfringens strains with 4 genes controlled in all but one strains and most genes controlled in one or two strains only.

Project description:DNA-binding transcription factors (TFs) are essential components of transcriptional regulatory networks in bacteria. LacI-family TFs (LacI-TFs) are broadly distributed among certain lineages of bacteria. The majority of characterized LacI-TFs sense sugar effectors and regulate carbohydrate utilization genes. The comparative genomics approaches enable in silico identification of TF-binding sites and regulon reconstruction. To study the function and evolution of LacI-TFs, we performed genomics-based reconstruction and comparative analysis of their regulons. For over 1300 LacI-TFs from over 270 bacterial genomes, we predicted their cognate DNA-binding motifs and identified target genes. Using the genome context and metabolic subsystem analyses of reconstructed regulons, we tentatively assigned functional roles and predicted candidate effectors for 78 and 67% of the analyzed LacI-TFs, respectively. Nearly 90% of the studied LacI-TFs are local regulators of sugar utilization pathways, whereas the remaining 125 global regulators control large and diverse sets of metabolic genes. The global LacI-TFs include the previously known regulators CcpA in Firmicutes, FruR in Enterobacteria, and PurR in Gammaproteobacteria, as well as the three novel regulators-GluR, GapR, and PckR-that are predicted to control the central carbohydrate metabolism in three lineages of Alphaproteobacteria. Phylogenetic analysis of regulators combined with the reconstructed regulons provides a model of evolutionary diversification of the LacI protein family. The obtained genomic collection of in silico reconstructed LacI-TF regulons in bacteria is available in the RegPrecise database (http://regprecise.lbl.gov). It provides a framework for future structural and functional classification of the LacI protein family and identification of molecular determinants of the DNA and ligand specificity. The inferred regulons can be also used for functional gene annotation and reconstruction of sugar catabolic networks in diverse bacterial lineages.

Project description:In silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels.An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis.A comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations.The reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/).

Project description:BackgroundThe number of completely sequenced plastid genomes available is growing rapidly. This array of sequences presents new opportunities to perform comparative analyses. In comparative studies, it is often useful to compare across wide phylogenetic spans and, within angiosperms, to include representatives from basally diverging lineages such as the genomes reported here: Nuphar advena (from a basal-most lineage) and Ranunculus macranthus (a basal eudicot). We report these two new plastid genome sequences and make comparisons (within angiosperms, seed plants, or all photosynthetic lineages) to evaluate features such as the status of ycf15 and ycf68 as protein coding genes, the distribution of simple sequence repeats (SSRs) and longer dispersed repeats (SDR), and patterns of nucleotide composition.ResultsThe Nuphar [GenBank:NC_008788] and Ranunculus [GenBank:NC_008796] plastid genomes share characteristics of gene content and organization with many other chloroplast genomes. Like other plastid genomes, these genomes are A+T-rich, except for rRNA and tRNA genes. Detailed comparisons of Nuphar with Nymphaea, another Nymphaeaceae, show that more than two-thirds of these genomes exhibit at least 95% sequence identity and that most SSRs are shared. In broader comparisons, SSRs vary among genomes in terms of abundance and length and most contain repeat motifs based on A and T nucleotides.ConclusionSSR and SDR abundance varies by genome and, for SSRs, is proportional to genome size. Long SDRs are rare in the genomes assessed. SSRs occur less frequently than predicted and, although the majority of the repeat motifs do include A and T nucleotides, the A+T bias in SSRs is less than that predicted from the underlying genomic nucleotide composition. In codon usage third positions show an A+T bias, however variation in codon usage does not correlate with differences in A+T-richness. Thus, although plastome nucleotide composition shows "A+T richness", an A+T bias is not apparent upon more in-depth analysis, at least in these aspects. The pattern of evolution in the sequences identified as ycf15 and ycf68 is not consistent with them being protein-coding genes. In fact, these regions show no evidence of sequence conservation beyond what is normal for non-coding regions of the IR.

Dataset Information

Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.

Publications

Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets