STITCH: interaction networks of chemicals and proteins

The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de

Induced biochemical interactions in immature and biodegraded heavy crude oils

Studies in which selective chemical markers have been used to explore the mechanisms by which biocatalysts interact with heavy crude oils have shown that the biochemical reactions follow distinct trends. The term biocatalyst refers to a group of extremophilic microorganisms which, under the experimental conditions used, interact with heavy crude oils to (1) cause a redistribution of hydrocarbons, (2) cause chemical changes in oil fractions containing sulfur compounds and lower the sulfur content, (3) decrease organic nitrogen content, and (4) decrease the concentration of trace metals. Current data indicate that the overall effect is due to simultaneous reactions yielding products with relatively higher concentration of saturates and lower concentrations of aromatics and resins. The compositional changes depend on the microbial species and the chemistry of the crudes. Economic analysis of a potential technology based on the available data indicate that such a technology, used in a pre-refinery mode, may be cost efficient and promising. In the present paper, the background of oil biocatalysis and some recent results will be discussed

PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data

Summary: Finding significant differences between the expression levels of genes or proteins across diverse biological conditions is one of the primary goals in the analysis of functional genomics data. However, existing methods for identifying differentially expressed genes or sets of genes by comparing measures of the average expression across predefined sample groups do not detect differential variance in the expression levels across genes in cellular pathways. Since corresponding pathway deregulations occur frequently in microarray gene or protein expression data, we present a new dedicated web application, PathVar, to analyze these data sources. The software ranks pathway-representing gene/protein sets in terms of the differences of the variance in the within-pathway expression levels across different biological conditions. Apart from identifying new pathway deregulation patterns, the tool exploits these patterns by combining different machine learning methods to find clusters of similar samples and build sample classification models

PIG—the pathogen interaction gateway

Protein–protein interactions (PPIs) play a vital role in initiating infection in a number of pathogens. Identifying which interactions allow a pathogen to infect its host can help us to understand methods of pathogenesis and provide potential targets for therapeutics. Public resources for studying host–pathogen systems, in particular PPIs, are scarce. To facilitate the study of host–pathogen PPIs, we have collected and integrated host–pathogen PPI (HP–PPI) data from a number of public resources to create the Pathogen Interaction Gateway (PIG). PIG provides a text based search and a BLAST interface for searching the HP–PPI data. Each entry in PIG includes information such as the functional annotations and the domains present in the interacting proteins. PIG provides links to external databases to allow for easy navigation among the various websites. Additionally, PIG includes a tool for visualizing a single HP–PPI network or two HP–PPI networks. PIG can be accessed at http://pig.vbi.vt.edu

Reactome: a knowledgebase of biological pathways

Reactome, located at http://www.reactome.org is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed. Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome relevant to model organism research communities. The database is publicly available under open source terms, which allows both its content and its software infrastructure to be freely used and redistributed

Using Multiple Ontologies to Integrate Complex Biological Data

The strength of the rat as a model organism lies in its utility in pharmacology, biochemistry and physiology research. Data resulting from such studies is difficult to represent in databases and the creation of user-friendly data mining tools has proved difficult. The Rat Genome Database has developed a comprehensive ontology-based data structure and annotation system to integrate physiological data along with environmental and experimental factors, as well as genetic and genomic information. RGD uses multiple ontologies to integrate complex biological information from the molecular level to the whole organism, and to develop data mining and presentation tools. This approach allows RGD to indicate not only the phenotypes seen in a strain but also the specific values under each diet and atmospheric condition, as well as gender differences. Harnessing the power of ontologies in this way allows the user to gather and filter data in a customized fashion, so that a researcher can retrieve all phenotype readings for which a high hypoxia is a factor. Utilizing the same data structure for expression data, pathways and biological processes, RGD will provide a comprehensive research platform which allows users to investigate the conditions under which biological processes are altered and to elucidate the mechanisms of disease

Reactome: a knowledge base of biologic pathways and processes

Reactome, an online curated resource for human pathway data, can be used to infer equivalent reactions in non-human species and as a tool to aid in the interpretation of microarrays and other high-throughput data sets

The Molecule Role Ontology: An Ontology for Annotation of Signal Transduction Pathway Molecules in the Scientific Literature

In general, it is not easy to specify a single sequence identity for each molecule name that appears in a pathway in the scientific literature. A molecule name may stand for concepts of various granularities, from concrete objects such as H-Ras and ERK1 to abstract concepts or categories such as Ras and MAPK. Typically, the relations among molecule names derive a hierarchical structure; without a proper way to handle this knowledge, it becomes ever more difficult to develop a reliable pathway database. This paper describes an ontology that is designed to annotate molecules in the scientific literature on signal transduction pathways

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