The synthesis of spatial data from the various sources available on the Web is a major challenge for current applications based on Web-based information retrieval and spatial decision-making. This article addresses spatial data fusion, with particular emphasis on its application in Spatial Data Infrastructures (SDIs). Possibilities for the integration of SDI and Semantic Web developments in the context of spatial data fusion are reviewed with a focus on the harmonized description and usage of feature relations. Specifically, potential applications of Linked Data principles are discussed in detail. On this basis, a classification and a decomposition of fusion processes in a service-oriented environment are proposed. A prototype implementation demonstrates the feasibility and usability of the approach using Open Geospatial Consortium (OGC) and Semantic Web standards.

BACKGROUND: Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. RESULTS: We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. CONCLUSIONS: The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.

MOTIVATION: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays. RESULTS: Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. AVAILABILITY: Dynamic deterministic effects propagation networks is implemented in the R programming language and available at http://www.dkfz.de/mga2/ddepn/.

UNLABELLED: Protein microarrays are well-established as sensitive tools for proteomics. Particularly, the microspot immunoassay (MIA) platform enables a quantitative analysis of (phospho-) proteins in complex solutions (e.g. cell lysates or blood plasma) and with low consumption of samples and reagents. Despite numerous biological and clinical applications of MIAs there is currently no user-friendly open source data analysis software available with versatile options for data analysis and data visualization. Here, we introduce the open source software QuantProReloaded that is specifically designed for the analysis of data from MIA experiments. AVAILABILITY AND IMPLEMENTATION: QuantProReloaded is written in R and Java and is open for download under the BSB license at http://code.google.com/p/quantproreloaded/.

MOTIVATION: KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory. RESULTS: We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway. AVAILABILITY: KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml).

BACKGROUND: High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways. RESULTS: In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example. CONCLUSION: Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package domainsignatures, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.

BACKGROUND: The identification of patterns in biological sequences is a key challenge in genome analysis and in proteomics. Frequently such patterns are complex and highly variable, especially in protein sequences. They are frequently described using terms of regular expressions (RegEx) because of the user-friendly terminology. Limitations arise for queries with the increasing complexity of patterns and are accompanied by requirements for enhanced capabilities. This is especially true for patterns containing ambiguous characters and positions and/or length ambiguities. RESULTS: We have implemented the 3of5 web application in order to enable complex pattern matching in protein sequences. 3of5 is named after a special use of its main feature, the novel n-of-m pattern type. This feature allows for an extensive specification of variable patterns where the individual elements may vary in their position, order, and content within a defined stretch of sequence. The number of distinct elements can be constrained by operators, and individual characters may be excluded. The n-of-m pattern type can be combined with common regular expression terms and thus also allows for a comprehensive description of complex patterns. 3of5 increases the fidelity of pattern matching and finds ALL possible solutions in protein sequences in cases of length-ambiguous patterns instead of simply reporting the longest or shortest hits. Grouping and combined search for patterns provides a hierarchical arrangement of larger patterns sets. The algorithm is implemented as internet application and freely accessible. The application is available at http://dkfz.de/mga2/3of5/3of5.html. CONCLUSION: The 3of5 application offers an extended vocabulary for the definition of search patterns and thus allows the user to comprehensively specify and identify peptide patterns with variable elements. The n-of-m pattern type offers an improved accuracy for pattern matching in combination with the ability to find all solutions, without compromising the user friendliness of regular expression terms.

BACKGROUND: The German cDNA Consortium has been cloning full length cDNAs and continued with their exploitation in protein localization experiments and cellular assays. However, the efficient use of large cDNA resources requires the development of strategies that are capable of a speedy selection of truly useful cDNAs from biological and experimental noise. To this end we have developed a new high-throughput analysis tool, CAFTAN, which simplifies these efforts and thus fills the gap between large-scale cDNA collections and their systematic annotation and application in functional genomics. RESULTS: CAFTAN is built around the mapping of cDNAs to the genome assembly, and the subsequent analysis of their genomic context. It uses sequence features like the presence and type of PolyA signals, inner and flanking repeats, the GC-content, splice site types, etc. All these features are evaluated in individual tests and classify cDNAs according to their sequence quality and likelihood to have been generated from fully processed mRNAs. Additionally, CAFTAN compares the coordinates of mapped cDNAs with the genomic coordinates of reference sets from public available resources (e.g., VEGA, ENSEMBL). This provides detailed information about overlapping exons and the structural classification of cDNAs with respect to the reference set of splice variants. The evaluation of CAFTAN showed that is able to correctly classify more than 85% of 5950 selected "known protein-coding" VEGA cDNAs as high quality multi- or single-exon. It identified as good 80.6 % of the single exon cDNAs and 85 % of the multiple exon cDNAs. The program is written in Perl and in a modular way, allowing the adoption of this strategy to other tasks like EST-annotation, or to extend it by adding new classification rules and new organism databases as they become available. We think that it is a very useful program for the annotation and research of unfinished genomes. CONCLUSION: CAFTAN is a high-throughput sequence analysis tool, which performs a fast and reliable quality prediction of cDNAs. Several thousands of cDNAs can be analyzed in a short time, giving the curator/scientist a first quick overview about the quality and the already existing annotation of a set of cDNAs. It supports the rejection of low quality cDNAs and helps in the selection of likely novel splice variants, and/or completely novel transcripts for new experiments.