EndoNet: an information resource about regulatory networks of cell-to-cell communication†

EndoNet is an information resource about intercellular regulatory communication. It provides information about hormones, hormone receptors, the sources (i.e. cells, tissues and organs) where the hormones are synthesized and secreted, and where the respective receptors are expressed. The database focuses on the regulatory relations between them. An elementary communication is displayed as a causal link from a cell that secretes a particular hormone to those cells which express the corresponding hormone receptor and respond to the hormone. Whenever expression, synthesis and/or secretion of another hormone are part of this response, it renders the corresponding cell an internal node of the resulting network. This intercellular communication network coordinates the function of different organs. Therefore, the database covers the hierarchy of cellular organization of tissues and organs as it has been modeled in the Cytomer ontology, which has now been directly embedded into EndoNet. The user can query the database; the results can be used to visualize the intercellular information flow. A newly implemented hormone classification enables to browse the database and may be used as alternative entry point. EndoNet is accessible at: http://endonet.bioinf.med.uni-goettingen.de

EndoNet: an information resource about endocrine networks

EndoNet is a new database that provides information about the components of endocrine networks and their relations. It focuses on the endocrine cell-to-cell communication and enables the analysis of intercellular regulatory pathways in humans. In the EndoNet data model, two classes of components span a bipartite directed graph. One class represents the hormones (in the broadest sense) secreted by defined donor cells. The other class consists of the acceptor or target cells expressing the corresponding hormone receptors. The identity and anatomical environment of cell types, tissues and organs is defined through references to the CYTOMER(®) ontology. With the EndoNet user interface, it is possible to query the database for hormones, receptors or tissues and to combine several items from different search rounds in one complex result set, from which a network can be reconstructed and visualized. For each entity, a detailed characteristics page is available. Some well-established endocrine pathways are offered as showcases in the form of predefined result sets. These sets can be used as a starting point for a more complex query or for obtaining a quick overview. The EndoNet database is accessible at

TRANSPATH(®): an information resource for storing and visualizing signaling pathways and their pathological aberrations

TRANSPATH(®) is a database about signal transduction events. It provides information about signaling molecules, their reactions and the pathways these reactions constitute. The representation of signaling molecules is organized in a number of orthogonal hierarchies reflecting the classification of the molecules, their species-specific or generic features, and their post-translational modifications. Reactions are similarly hierarchically organized in a three-layer architecture, differentiating between reactions that are evidenced by individual publications, generalizations of these reactions to construct species-independent ‘reference pathways’ and the ‘semantic projections’ of these pathways. A number of search and browse options allow easy access to the database contents, which can be visualized with the tool PathwayBuilder™. The module PathoSign adds data about pathologically relevant mutations in signaling components, including their genotypes and phenotypes. TRANSPATH(®) and PathoSign can be used as encyclopaedia, in the educational process, for vizualization and modeling of signal transduction networks and for the analysis of gene expression data. TRANSPATH(®) Public 6.0 is freely accessible for users from non-profit organizations under

Graph-based analysis and visualization of experimental results with ONDEX

Motivation: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. Results: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function

T-Reg Comparator: an analysis tool for the comparison of position weight matrices

T-Reg Comparator is a novel software tool designed to support research into transcriptional regulation. Sequence motifs representing transcription factor binding sites are usually encoded as position weight matrices. The user inputs a set of such weight matrices or binding site sequences and our program matches them against the T-Reg database, which is presently built on data from the Transfac [E. Wingender (2004) In Silico Biol., 4, 55–61] and Jaspar [A. Sandelin, W. Alkema, P. Engstrom, W. W. Wasserman and B. Lenhard (2004) Nucleic Acids Res., 32, D91–D94]. Our tool delivers a detailed report on similarities between user-supplied motifs and motifs in the database. Apart from simple one-to-one relationships, T-Reg Comparator is also able to detect similarities between submatrices. In addition, we provide a user interface to a program for sequence scanning with weight matrices. Typical areas of application for T-Reg Comparator are motif and regulatory module finding and annotation of regulatory genomic regions. T-Reg Comparator is available at

cisRED: a database system for genome-scale computational discovery of regulatory elements

We describe cisRED, a database for conserved regulatory elements that are identified and ranked by a genome-scale computational system (). The database and high-throughput predictive pipeline are designed to address diverse target genomes in the context of rapidly evolving data resources and tools. Motifs are predicted in promoter regions using multiple discovery methods applied to sequence sets that include corresponding sequence regions from vertebrates. We estimate motif significance by applying discovery and post-processing methods to randomized sequence sets that are adaptively derived from target sequence sets, retain motifs with p-values below a threshold and identify groups of similar motifs and co-occurring motif patterns. The database offers information on atomic motifs, motif groups and patterns. It is web-accessible, and can be queried directly, downloaded or installed locally

FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting

FOOTER is a newly developed algorithm that analyzes homologous mammalian promoter sequences in order to identify transcriptional DNA regulatory ‘signals’. FOOTER uses prior knowledge about the binding site preferences of the transcription factors (TFs) in the form of position-specific scoring matrices (PSSMs). The PSSM models are generated from known mammalian binding sites from the TRANSFAC database. In a test set of 72 confirmed binding sites (most of them not present in TRANSFAC) of 19 TFs, it exhibited 83% sensitivity and 72% specificity. FOOTER is accessible over the web at

enoLOGOS: a versatile web tool for energy normalized sequence logos

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at

mirConnX: condition-specific mRNA-microRNA network integrator

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. Visualization of the network and subsequent analysis are provided via a very responsive graphic user interface. Two organisms are currently supported: Homo sapiens and Mus musculus. The intuitive user interface and large database make mirConnX a useful tool for clinical scientists for hypothesis generation and explorations. mirConnX is freely available for academic use at http://www.benoslab.pitt.edu/mirconnx