OnTheFly is a web-based application that applies biological named entity recognition to enrich Microsoft Office, PDF and plain text documents. The input files are converted into the HTML format and then sent to the Reflect tagging server, which highlights biological entity names like genes, proteins and chemicals, and attaches to them JavaScript code to invoke a summary popup window. The window provides an overview of relevant information about the entity, such as a protein description, the domain composition, a link to the 3D structure and links to other relevant online resources. OnTheFly is also able to extract the bioentities mentioned in a set of files and to produce a graphical representation of the networks of the known and predicted associations of these entities by retrieving the information from the STITCH database. AVAILABILITY: http://onthefly.embl.de Supplementary material: http://onthefly.embl.de/FAQ.html.

BACKGROUND: Environments and their organic content are generally not static and isolated, but in a constant state of exchange and interaction with each other. Through physical or biological processes, organisms, especially microbes, may be transferred between environments whose characteristics may be quite different. The transferred microbes may not survive in their new environment, but their DNA will be deposited. In this study, we compare two environmental sequencing projects to find molecular evidence of transfer of microbes over vast geographical distances. METHODOLOGY: By studying synonymous nucleotide composition, oligomer frequency and orthology between predicted genes in metagenomics data from two environments, terrestrial and aquatic, and by correlating with phylogenetic mappings, we find that both environments are likely to contain trace amounts of microbes which have been far removed from their original habitat. We also suggest a bias in direction from soil to sea, which is consistent with the cycles of planetary wind and water. CONCLUSIONS: Our findings support the Baas-Becking hypothesis formulated in 1934, which states that due to dispersion and population sizes, microbes are likely to be found in widely disparate environments. Furthermore, the availability of genetic material from distant environments is a possible font of novel gene functions for lateral gene transfer.

Time-series analysis of whole-genome expression data during Drosophila melanogaster development indicates that up to 86% of its genes change their relative transcript level during embryogenesis. By applying conservative filtering criteria and requiring 'sharp' transcript changes, we identified 1534 maternal genes, 792 transient zygotic genes, and 1053 genes whose transcript levels increase during embryogenesis. Each of these three categories is dominated by groups of genes where all transcript levels increase and/or decrease at similar times, suggesting a common mode of regulation. For example, 34% of the transiently expressed genes fall into three groups, with increased transcript levels between 2.5-12, 11-20, and 15-20 h of development, respectively. We highlight common and distinctive functional features of these expression groups and identify a coupling between downregulation of transcript levels and targeted protein degradation. By mapping the groups to the protein network, we also predict and experimentally confirm new functional associations.

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.

To test the impact of environments on genome evolution, we analysed the relative abundance of the nucleotides guanine and cytosine ('GC content') of large numbers of sequences from four distinct environmental samples (ocean surface water, farm soil, an acidophilic mine drainage biofilm and deep-sea whale carcasses). We show that the GC content of complex microbial communities seems to be globally and actively influenced by the environment. The observed nucleotide compositions cannot be easily explained by distinct phylogenetic origins of the species in the environments; the genomic GC content may change faster than was previously thought, and are also reflected in the amino-acid composition of the proteins in these habitats.

SUMMARY: Medusa is a java application for visualizing and manipulating graphs of interaction, such as data from the STRING database. It features an intuitive user interface developed with the help of biologists. Medusa is optimized for accessing protein interaction data from STRING, but can be used for any type of graph from any scientific field. AVAILABILITY: Medusa, along with sample data sets and instructions, can be downloaded from http://www.bork.embl.de/medusa.

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene-phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.

A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, 'association' can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein-protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730,000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.