Information content based model for the topological properties of the gene regulatory network of Escherichia coli

Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae. (Balcan et al., 2007) Random sequences that represent promoter regions and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in agreement with the data. The clustering coefficient, degree-degree correlation, rich club coefficient and k-core visualization all agree qualitatively with the empirical network to an extent not yet achieved by any other computational model, to our knowledge. The significant statistical differences can point the way to further research into non-adaptive and adaptive processes in the evolution of the E. coli GRN.Comment: 58 pages, 3 tables, 22 figures. In press, Journal of Theoretical Biology (2009)

SwissRegulon: a database of genome-wide annotations of regulatory sites

SwissRegulon () is a database containing genome-wide annotations of regulatory sites in the intergenic regions of genomes. The regulatory site annotations are produced using a number of recently developed algorithms that operate on multiple alignments of orthologous intergenic regions from related genomes in combination with, whenever available, known sites from the literature, and ChIP-on-chip binding data. Currently SwissRegulon contains annotations for yeast and 17 prokaryotic genomes. The database provides information about the sequence, location, orientation, posterior probability and, whenever available, binding factor of each annotated site. To enable easy viewing of the regulatory site annotations in the context of other features annotated on the genomes, the sites are displayed using the GBrowse genome browser interface and can be queried based on any annotated genomic feature. The database can also be queried for regulons, i.e. sites bound by a common factor

Transcriptome Analysis of Targeted Mouse Mutations Reveals the Topography of Local Changes in Gene Expression.

The unintended consequences of gene targeting in mouse models have not been thoroughly studied and a more systematic analysis is needed to understand the frequency and characteristics of off-target effects. Using RNA-seq, we evaluated targeted and neighboring gene expression in tissues from 44 homozygous mutants compared with C57BL/6N control mice. Two allele types were evaluated: 15 targeted trap mutations (TRAP); and 29 deletion alleles (DEL), usually a deletion between the translational start and the 3' UTR. Both targeting strategies insert a bacterial beta-galactosidase reporter (LacZ) and a neomycin resistance selection cassette. Evaluating transcription of genes in +/- 500 kb of flanking DNA around the targeted gene, we found up-regulated genes more frequently around DEL compared with TRAP alleles, however the frequency of alleles with local down-regulated genes flanking DEL and TRAP targets was similar. Down-regulated genes around both DEL and TRAP targets were found at a higher frequency than expected from a genome-wide survey. However, only around DEL targets were up-regulated genes found with a significantly higher frequency compared with genome-wide sampling. Transcriptome analysis confirms targeting in 97% of DEL alleles, but in only 47% of TRAP alleles probably due to non-functional splice variants, and some splicing around the gene trap. Local effects on gene expression are likely due to a number of factors including compensatory regulation, loss or disruption of intragenic regulatory elements, the exogenous promoter in the neo selection cassette, removal of insulating DNA in the DEL mutants, and local silencing due to disruption of normal chromatin organization or presence of exogenous DNA. An understanding of local position effects is important for understanding and interpreting any phenotype attributed to targeted gene mutations, or to spontaneous indels

SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates

Identification of genomic regulatory elements is essential for understanding the dynamics of cellular processes. This task has been substantially facilitated by the availability of genome sequences for many species and high-throughput data of transcripts and transcription factor (TF) binding. However, rigorous computational methods are necessary to derive accurate genome-wide annotations of regulatory sites from such data. SwissRegulon (http://swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory motifs, promoters and TF binding sites (TFBSs) in promoter regions across model organisms. Its binding site predictions were obtained with rigorous Bayesian probabilistic methods that operate on orthologous regions from related genomes, and use explicit evolutionary models to assess the evidence of purifying selection on each site. New in the current version of SwissRegulon is a curated collection of 190 mammalian regulatory motifs associated with ∼340 TFs, and TFBS annotations across a curated set of ∼35 000 promoters in both human and mouse. Predictions of TFBSs for Saccharomyces cerevisiae have also been significantly extended and now cover 158 of yeast's ∼180 TFs. All data are accessible through both an easily navigable genome browser with search functions, and as flat files that can be downloaded for further analysi

JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles

JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner. Its fourth major release is the largest expansion of the core database to date: the database now holds 457 non-redundant, curated profiles. The new entries include the first batch of profiles derived from ChIP-seq and ChIP-chip whole-genome binding experiments, and 177 yeast TF binding profiles. The introduction of a yeast division brings the convenience of JASPAR to an active research community. As binding models are refined by newer data, the JASPAR database now uses versioning of matrices: in this release, 12% of the older models were updated to improved versions. Classification of TF families has been improved by adopting a new DNA-binding domain nomenclature. A curated catalog of mammalian TFs is provided, extending the use of the JASPAR profiles to additional TFs belonging to the same structural family. The changes in the database set the system ready for more rapid acquisition of new high-throughput data sources. Additionally, three new special collections provide matrix profile data produced by recent alternative high-throughput approaches

YPA: an integrated repository of promoter features in Saccharomyces cerevisiae

This study presents the Yeast Promoter Atlas (YPA, http://ypa.ee.ncku.edu.tw/ or http://ypa.csbb.ntu.edu.tw/) database, which aims to collect comprehensive promoter features in Saccharomyces cerevisiae. YPA integrates nine kinds of promoter features including promoter sequences, genes’ transcription boundaries—transcription start sites (TSSs), five prime untranslated regions (5′-UTRs) and three prime untranslated regions (3′UTRs), TATA boxes, transcription factor binding sites (TFBSs), nucleosome occupancy, DNA bendability, transcription factor (TF) binding, TF knockout expression and TF–TF physical interaction. YPA is designed to present data in a unified manner as many important observations are revealed only when these promoter features are considered altogether. For example, DNA rigidity can prevent nucleosome packaging, thereby making TFBSs in the rigid DNA regions more accessible to TFs. Integrating nucleosome occupancy, DNA bendability, TF binding, TF knockout expression and TFBS data helps to identify which TFBS is actually functional. In YPA, various promoter features can be accessed in a centralized and organized platform. Researchers can easily view if the TFBSs in an interested promoter are occupied by nucleosomes or located in a rigid DNA segment and know if the expression of the downstream gene responds to the knockout of the corresponding TFs. Compared to other established yeast promoter databases, YPA collects not only TFBSs but also many other promoter features to help biologists study transcriptional regulation

Reconstruction of novel transcription factor regulons through inference of their binding sites

Background In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present, identification of TF regulons is mostly done through comparative genomics approaches. Such methods could miss organism-specific regulatory interactions and often require expensive and time-consuming experimental techniques to generate the underlying data. Results In this work, we present an efficient algorithm that aims to identify a given transcription factor’s regulon through inference of its unknown binding sites, based on the discovery of its binding motif. The proposed approach relies on computational methods that utilize gene expression data sets and knockout fitness data sets which are available or may be straightforwardly obtained for many organisms. We computationally constructed the profiles of putative regulons for the TFs LexA, PurR and Fur in E. coli K12 and identified their binding motifs. Comparisons with an experimentally-verified database showed high recovery rates of the known regulon members, and indicated good predictions for the newly found genes with high biological significance. The proposed approach is also applicable to novel organisms for predicting unknown regulons of the transcriptional regulators. Results for the hypothetical protein D d e0289 in D. alaskensis include the discovery of a Fis-type TF binding motif. Conclusions The proposed motif-based regulon inference approach can discover the organism-specific regulatory interactions on a single genome, which may be missed by current comparative genomics techniques due to their limitations

Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years

FABIAN-variant: predicting the effects of DNA variants on transcription factor binding.

While great advances in predicting the effects of coding variants have been made, the assessment of non-coding variants remains challenging. This is especially problematic for variants within promoter regions which can lead to over-expression of a gene or reduce or even abolish its expression. The binding of transcription factors to the DNA can be predicted using position weight matrices (PWMs). More recently, transcription factor flexible models (TFFMs) have been introduced and shown to be more accurate than PWMs. TFFMs are based on hidden Markov models and can account for complex positional dependencies. Our new web-based application FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants affect the binding of 1387 different human transcription factors. For each variant and transcription factor, the software combines the results of different models for a final prediction of the resulting binding-affinity change. The software is written in C++ for speed but variants can be entered through a web interface. Alternatively, a VCF file can be uploaded to assess variants identified by high-throughput sequencing. The search can be restricted to variants in the vicinity of candidate genes. FABIAN-variant is available freely at https://www.genecascade.org/fabian/