CoryneRegNet 6.0—Updated database content, new analysis methods and novel features focusing on community demands

Post-genomic analysis techniques such as next-generation sequencing have produced vast amounts of data about micro organisms including genetic sequences, their functional annotations and gene regulatory interactions. The latter are genetic mechanisms that control a cell's characteristics, for instance, pathogenicity as well as survival and reproduction strategies. CoryneRegNet is the reference database and analysis platform for corynebacterial gene regulatory networks. In this article we introduce the updated version 6.0 of CoryneRegNet and describe the updated database content which includes, 6352 corynebacterial regulatory interactions compared with 4928 interactions in release 5.0 and 3235 regulations in release 4.0, respectively. We also demonstrate how we support the community by integrating analysis and visualization features for transiently imported custom data, such as gene regulatory interactions. Furthermore, with release 6.0, we provide easy-to-use functions that allow the user to submit data for persistent storage with the CoryneRegNet database. Thus, it offers important options to its users in terms of community demands. CoryneRegNet is publicly available at http://www.coryneregnet.de

RegPrecise: a database of curated genomic inferences of transcriptional regulatory interactions in prokaryotes

The RegPrecise database (http://regprecise.lbl.gov) was developed for capturing, visualization and analysis of predicted transcription factor regulons in prokaryotes that were reconstructed and manually curated by utilizing the comparative genomic approach. A significant number of high-quality inferences of transcriptional regulatory interactions have been already accumulated for diverse taxonomic groups of bacteria. The reconstructed regulons include transcription factors, their cognate DNA motifs and regulated genes/operons linked to the candidate transcription factor binding sites. The RegPrecise allows for browsing the regulon collections for: (i) conservation of DNA binding sites and regulated genes for a particular regulon across diverse taxonomic lineages; (ii) sets of regulons for a family of transcription factors; (iii) repertoire of regulons in a particular taxonomic group of species; (iv) regulons associated with a metabolic pathway or a biological process in various genomes. The initial release of the database includes ∼11 500 candidate binding sites for ∼400 orthologous groups of transcription factors from over 350 prokaryotic genomes. Majority of these data are represented by genome-wide regulon reconstructions in Shewanella and Streptococcus genera and a large-scale prediction of regulons for the LacI family of transcription factors. Another section in the database represents the results of accurate regulon propagation to the closely related genomes

phiSITE: database of gene regulation in bacteriophages

We have developed phiSITE, database of gene regulation in bacteriophages. To date it contains detailed information about more than 700 experimentally confirmed or predicted regulatory elements (promoters, operators, terminators and attachment sites) from 32 bacteriophages belonging to Siphoviridae, Myoviridae and Podoviridae families. The database is manually curated, the data are collected mainly form scientific papers, cross-referenced with other database resources (EMBL, UniProt, NCBI taxonomy database, NCBI Genome, ICTVdb, PubMed Central) and stored in SQL based database system. The system provides full text search for regulatory elements, graphical visualization of phage genomes and several export options. In addition, visualizations of gene regulatory networks for five phages (Bacillus phage GA-1, Enterobacteria phage lambda, Enterobacteria phage Mu, Enterobacteria phage P2 and Mycoplasma phage P1) have been defined and made available. The phiSITE is accessible at http://www.phisite.org/

On the power and limits of evolutionary conservation—unraveling bacterial gene regulatory networks

The National Center for Biotechnology Information (NCBI) recently announced ‘1000 prokaryotic genomes are now completed and available in the Genome database’. The increasing trend will provide us with thousands of sequenced microbial organisms over the next years. However, this is only the first step in understanding how cells survive, reproduce and adapt their behavior while being exposed to changing environmental conditions. One major control mechanism is transcriptional gene regulation. Here, striking is the direct juxtaposition of the handful of bacterial model organisms to the 1000 prokaryotic genomes. Next-generation sequencing technologies will further widen this gap drastically. However, several computational approaches have proven to be helpful. The main idea is to use the known transcriptional regulatory network of reference organisms as template in order to unravel evolutionarily conserved gene regulations in newly sequenced species. This transfer essentially depends on the reliable identification of several types of conserved DNA sequences. We decompose this problem into three computational processes, review the state of the art and illustrate future perspectives

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc

FITBAR: a web tool for the robust prediction of prokaryotic regulons

<p>Abstract</p> <p>Background</p> <p>The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The <it>in silico </it>prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.</p> <p>Results</p> <p>FITBAR (Fast Investigation Tool for Bacterial and Archaeal Regulons) is a web service designed to identify new protein binding sites on fully sequenced prokaryotic genomes. This tool consists in a workbench where the significance of the predictions can be compared using different statistical methods, a feature not found in existing resources. The Local Markov Model and the Compound Importance Sampling algorithms have been implemented to compute the P-value of newly discovered binding sites. In addition, FITBAR provides two optimized genomic scanning algorithms using either log-odds or entropy-weighted position-specific scoring matrices. Other significant features include the production of a detailed genomic context map for each detected binding site and the export of the search results in spreadsheet and portable document formats. FITBAR discovery of a high affinity <it>Escherichia coli </it>NagC binding site was validated experimentally <it>in vitro </it>as well as <it>in vivo </it>and published.</p> <p>Conclusions</p> <p>FITBAR was developed in order to allow fast, accurate and statistically robust predictions of prokaryotic regulons. This feature constitutes the main advantage of this web tool over other matrix search programs and does not impair its performance. The web service is available at <url>http://archaea.u-psud.fr/fitbar</url>.</p

Using Mutual Information and Answer Set Programming to refine PWM based transcription regulation network

National audienceTranscriptional regulatory network models can be reconstructed ab initio from DNA sequence data by locating the binding sites, defined by position specific score matrices, and identifying transcription factors by homology with known ones in other organisms. In general the resulting network contains spurious elements, because the pattern matching methods for binding site location have low specificity, while homology to known transcription factors does not always identify correctly new ones. In the case of A. ferrooxidans, one of the bacterias involved in industrial bioleaching processes, the sequence based network reconstruction results in 66 transcription factors and 182 binding site motifs represented in 27 435 sites. In this work we use differential expression experimental data, in the form of Mutual Information, as logical constraints to be satisfied by any valid regulatory network subgraph. These rules are expressed as an Answer Set Program, a logical programming paradigm, and used to determine the minimal sets of motif and transcription factors which constitute a genetic regulatory network compatible with the experimental evidence. The resulting network comprises 27 transcription factors and 14 motifs in 2428 instances, satisfying all constraints

Genomic repertoires of DNA-binding transcription factors across the tree of life.

Sequence-specific transcription factors (TFs) are important to genetic regulation in all organisms because they recognize and directly bind to regulatory regions on DNA. Here, we survey and summarize the TF resources available. We outline the organisms for which TF annotation is provided, and discuss the criteria and methods used to annotate TFs by different databases. By using genomic TF repertoires from ∼700 genomes across the tree of life, covering Bacteria, Archaea and Eukaryota, we review TF abundance with respect to the number of genes, as well as their structural complexity in diverse lineages. While typical eukaryotic TFs are longer than the average eukaryotic proteins, the inverse is true for prokaryotes. Only in eukaryotes does the same family of DNA-binding domain (DBD) occur multiple times within one polypeptide chain. This potentially increases the length and diversity of DNA-recognition sequence by reusing DBDs from the same family. We examined the increase in TF abundance with the number of genes in genomes, using the largest set of prokaryotic and eukaryotic genomes to date. As pointed out before, prokaryotic TFs increase faster than linearly. We further observe a similar relationship in eukaryotic genomes with a slower increase in TFs