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Life science researchers want biological information in their interest to become available to them as soon as possible. A monitoring system is a solution that relieves biologists from periodic exploration of databases. In particular, it allows them to express their interest in certain data by means of queries/constraints; they are then notified when new data arrives satisfying these queries/constraints. We describe a sequence monitoring system XSeqM where users can combine metadata queries on sequence records with constraints on an alignment against a given source sequence. The system is an XML-based solution where constraints are specified through search fields in a user-friendly web interface and which are then translated to corresponding XPath-expressions. The system is easily extensible as addition of new databases to the system then only amounts to the specification of new mappings from search fields to XPath-expressions. To protect private source sequences obtained in labs, it is imperative that researchers do not have to upload their sequences to a general untrusted system, but that they can run XSeqM locally. To keep the system light-weight, we therefore introduce an optimization technique based on query containment to reduce the number of XPath-evaluations which constitutes the bottleneck of the system. We experimentally validate this technique and show that it can drastically improve the running time. [ABSTRACT FROM AUTHOR]

Scientific experiments are becoming increasingly large and complex, with a commensurate increase in the amount and complexity of data generated. Data, both intermediate and final results, is derived by chaining and nesting together multiple database searches and analytical tools. In many cases, the means by which the data are produced is not known, making the data difficult to interpret and the experiment impossible to reproduce. Provenance in scientific workflows is thus of paramount importance. In this paper, we provide a formal model of provenance for scientific workflows which is general (i.e. can be used with existing workflow systems, such as Kepler, myGrid and Chimera) and sufficiently expressive to answer the provenance queries we encountered in a number of case studies. Interestingly, our model not only takes into account the chained and nested structure of scientific workflows, but allows asks for provenance at different levels of abstraction (user views). [ABSTRACT FROM AUTHOR]

Steps in scientific workflows often generate collections of results, causing the data flowing through workflows to become increasingly nested. Because conventional workflow components (or actors) typically operate on simple or application-specific data types, additional actors often are required to manage these nested data collections. As a result, conventional workflows become increasingly complex as data becomes more nested. This paper describes a new paradigm for developing scientific workflows that transparently manages nested data collections. Collection-oriented workflows have a number of advantages over conventional approaches including simpler workflow designs (e.g., requiring fewer actors and control-flow constructs) that are invariant under changes in data nesting. Our implementation within the Kepler scientific workflow system enables the explicit representation of collections and collection schemas, concurrent operation over collection contents via multi-level pipeline parallelism, and allows collection-aware actors to be composed readily from conventional actors. [ABSTRACT FROM AUTHOR]

Workflows are gaining importance as a means for modelling and enacting in silico scientific experiments. A major issue which arises when aggregating a collection of analysis operations within a workflow is the compatibility of their inputs and outputs: the analysis operations are supplied by independently developed web services which are likely to have incompatible inputs and outputs. We use the term mismatch to refer to such incompatibility. This paper characterises the mismatches a scientific workflow may suffer from and specifies mappings for their resolution. [ABSTRACT FROM AUTHOR]

The huge amount of unstructured information generated by academic and industrial research groups must be easily available to facilitate scientific projects. In particular, information that is conveyed by unstructured or semi-structured text represents a vast resource for the scientific community. Systems capable of mining these textual data sets are the only option to unveil the information hidden in free text on a large scale. The BioLT Literature Mining Tool allows exhaustive extraction of information from text resources. Using advanced tagger/parser mechanisms and topic-specific dictionaries, the BioLT tool delivers structured relationships. Beyond information hidden in free text, other resources in biological and medical research are relevant, including experimental data from "-omics" platforms, phenotype information and clinical data. The BioXM Knowledge Management Environment efficiently models such complex research environments. This platform enables scientists to create knowledge networks with flexible workflows for handling experimental information and metadata, including annotation or ontologies. Information from public databases can be incorporated using the embedded BioRS Integration and Retrieval System. Users can navigate and modify the information networks. Thus, research projects can be modeled and extended dynamically. [ABSTRACT FROM AUTHOR]

Our workflow platform offers a view of the different tools available as a single and uniform pool of services readily available for enhancing query processing. This proposal is based on an architecture for publishing biological data and services, and is designed to be a flexible client for making use of BioMOBY servers, extending them with persistency of the information retrieved for each user. We also present in this paper some biological results, which have been obtained by taking advantage of the proposed workflow execution system. This work has been developed and implemented in the National Institute for Bioinformatics (INB) in Spain (available at http://www.inab.org/MOWServ). [ABSTRACT FROM AUTHOR]

With the tremendous development in size, the complexity of ontology increases. Thus ontology evaluation becomes extremely important for developers to determine the fundamental characteristics of ontologies in order to improve the quality, estimate cost and reduce future maintenance. Our research examines the concepts and their hierarchy in ontology conceptual model, the common feature of most ontologies, which reflects the fundamental complexity. We suggest some well-defined metrics of complexity, which mainly examine the quantity, ratio and correlativity of concepts and relationships, to evaluate ontology from the viewpoint of complexity and evolution. In the study, we measured three ontologies in Gene Ontology to verify our metrics. The results indicate that these metrics works well, and the biological process ontology is the most complex one from the view of complexity, and the molecular function ontology is the unsteadiest one from the view of evolution. [ABSTRACT FROM AUTHOR]

Over a period of several years we apply the systems biology approach to investigate the dynamic regulatory mechanisms controlling the expression of segmentation genes in Drosophila embryo. Due to ongoing data acquisition, development of new processing and analysis methods, as well as modification and improvement of old ones serious problems arose with data and workflows management. Different geographical location of research groups poses additional difficulties. To solve these problems we have developed an information management system using multiagent and REST architectures. This system is easily extendable to deal with new data processing and analysis methods, flexible in specification and modification of these methods, scalable and supports distributed processing and analysis of data. [ABSTRACT FROM AUTHOR]

The Distributed Annotation System (DAS) is a protocol for sharing of biological data which allows for dynamical data integration. It has become widely used in both the genome and protein bioinformatics communities. Here we provide an overview of the available DAS infrastructure and present our latest developments, including a registration server that facilitates service discovery by DAS clients while automatically monitoring service availability. Currently there are 108 registered DAS servers, provided by 24 institutions in 10 countries. [ABSTRACT FROM AUTHOR]

Despite the availability of antiretroviral combination therapies, success in drug treatment of HIV-infected patients is limited. One reason for therapy failure is the development of drug-resistant genetic variants. In principle, the viral genomic sequence provides resistance information and could thus guide the selection of an optimal drug combination. In practice however, the benefit of this procedure is impaired by (1) the difficulty in inferring the clinically relevant information from the genotype of the virus and (2) the restricted availability of this information. We have developed a secure platform for collaborative research aimed at optimizing anti-HIV therapies, called Arevir. A relational database schema was designed and implemented together with a web-based user interface. Our system provides a basis for monitoring patients, decision-support, and computational analyses. Thus, it merges clinical, diagnostic and bioinformatics efforts to exploit genomic and patient therapy data in clinical practice. [ABSTRACT FROM AUTHOR]

This paper describes a technique for efficiently searching metabolic pathways similar to a given query pathway, from a pathway database. Metabolic pathways can be converted into labeled directed graphs where the nodes represent chemical compounds. Similarity between two graphs can be computed using a metric based on Maximal Common Subgraph (MCS). By maintaining an inverted file that indexes all pathways in a database on their edges, our algorithm finds and ranks all pathways similar to the user input query pathway in time, which is linear in the total number of occurrences of the edges in common with the query in the entire database. [ABSTRACT FROM AUTHOR]

The Open Biomedical Ontologies (OBO) is a consortium that serves as a repository of ontologies that are structured like directed acyclic graphs. In this paper we present a language DQL for querying a database of directed acyclic graphs. The query language has a comprehension style syntax and contains a pattern specification sub-language DPL. DPL can be viewed as an extension of tree-pattern query language like XPath. The language allows extraction of nodes, paths and subgraphs from DAGs, and permits construction of result structures by composing them. We show that using such a language on OBO ontologies (such as the gene ontology), we can express more complex and scientifically valuable queries. [ABSTRACT FROM AUTHOR]

Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases. As the main steps the method contains specification of grouping rules, pairwise grouping between entries, actual grouping of similar entries, and evaluation and analysis of the results. Often, different strategies can be used in the different steps. The method enables exploration of the influence of the choices and supports evaluation of the results with respect to given classifications. The grouping method is illustrated by test cases based on different strategies and classifications. The results show the complexity of the similarity-based grouping tasks and give deeper insights in the selected grouping tasks, the analyzed data source, and the influence of different strategies on the results. [ABSTRACT FROM AUTHOR]

We introduce the BioFuice approach for integrating data from different private and public data sources and ontologies. BioFuice follows a peer-to-peer-like data integration based on bidirectional mappings. Sources and mappings are associated with a domain model to support a semantically meaningful interoperability. BioFuice extends the generic iFuice integration platform which utilizes specific operators for data fusion and workflow-like script programs. BioFuice supports explorative data analysis and query and search capabilities. We outline the integration approach by an illustrating scenario, the architecture of BioFuice and its query interface. [ABSTRACT FROM AUTHOR]

To establish a clean basis for studying alternative splicing and gene regulation in life science projects, a powerful data modeling and also a strict validation procedure for assigning levels of reliability to given gene models is essential. One common problem of public genome databases are insufficiently organized and linked description data, which make it difficult to study relations of the alternative isoforms of a gene that are relevant for medi cine and plant genome research. This is a severe obstacle for the integration of biological data and motivated us to establish a new modeling instance and that we call splice template or sTMP. Every sTMP has a unique splicing pattern, but the length of the first and the last exon remains undefined. This allows to model different gene isoforms with the same splicing pattern. By utilizing this more fine-grained data structure, many cases of plurivalent mRNA-CDS relations are uncovered. There are more than 3,000 extra CDSs in the human genome compatible with the categories sTMP, mRNA and CDS, which exceed the classical one-to-one relations of mRNAs and CDSs. In one case, 11 extra CDSs are compatible with one mRNA. Crosslinks between mRNAs derived from different sTMPs leading to the same CDS are now accessible as well as disease-related ruptures in UTR regions. This allows discovering and validating disease and tissue specific differences in alternative splicing, gene expression and regulation. Another problem in public databases is a too much relaxed standard for labeling genes "confirmed by ESTs and full-length-cDNAs." We provide a pipeline that handles gene annotations from different sources, integrates them into complex gene models and assigns strict validation tags, constrained by a local low-error model for the alignments of genome annotation and transcripts. The data structures are being implemented and made publicly available at the Plant Data Warehouse of the Bioinformatics Center Gatersleben-Halle (http://portal.bic-gh.de/sTMP). Keywords:Gene and genome annotation, alternative splicing, data integration, splice template, validation and confirmation, quality control, Fasta-XML format. [ABSTRACT FROM AUTHOR]

The recent technological advancements in biological research have allowed researchers to advance their knowledge of the domain far beyond expectations. The advent of easily accessible biological web databases such as NCBI databases and associated tools such as BLAST are key components to this development. However, with the growing number of these web based biological research tools and data sources, the time necessary to invest in becoming a domain expert is immense. Therefore, it is important to allow for easy user deployment of the wealth of available data sources and tools necessary to conduct biological research. In this paper we discuss an approach to create and maintain a robust ontology knowledge base that serves as the core for SIBIOS, a workflow based integration system for bioinformatics tools and data sources. Further, deployment of the ontology in various components of SIBIOS is discussed. Keywords: Data integration, scientific workflows, ontologies, fault tolerance. [ABSTRACT FROM AUTHOR]

Simulating networks of biochemical reactions require reliable kinetic data. In order to facilitate the access to such kinetic data we have developed SABIO-RK, a curated database with information about biochemical reactions and their kinetic properties. The data are manually extracted from literature and verified by curators, concerning standards, formats and controlled vocabularies. This process is supported by tools in a semi-automatic manner. SABIO-RK contains and merges information about reactions such as reactants and modifiers, organism, tissue and cellular location, as well as the kinetic properties of the reactions. The type of the kinetic mechanism, modes of inhibition or activation, and corresponding rate equations are presented together with their parameters and measured values, specifying the experimental conditions under which these were determined. Links to other databases enable the user to gather further information and to refer to the original publication. Information about reactions and their kinetic data can be exported to an SBML file, allowing users to employ the information as the basis for their simulation models. [ABSTRACT FROM AUTHOR]

Pharmacogenomics explores the impact of individual genomic variations in health problems such as adverse drug reactions. Records of millions of genomic variations, mostly known as Single Nucleotide Polymorphisms (SNP), are available today in various overlapping and heterogeneous databases. Selecting and extracting from these databases or from private sources a proper set of polymorphisms are the first steps of a KDD (Knowledge Discovery in Databases) process in pharmacogenomics. It is however a tedious task hampered by the heterogeneity of SNP nomenclatures and annotations. Standards for representing genomic variants have been proposed by the Human Genome Variation Society (HGVS). The SNP-Converter application is aimed at converting any SNP description into an HGVS-compliant pivot description and vice versa. Used in the frame of a knowledge system, the SNP-Converter application contributes as a wrapper to semantic data integration and enrichment. [ABSTRACT FROM AUTHOR]

Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible. [ABSTRACT FROM AUTHOR]

An essential part of bioinformatic research concerns the iterative process of validating hypotheses by analyzing facts stored in databases and in published literature. This process can be enhanced by language technology methods, in particular by automatic text understanding. Since it is becoming increasingly difficult to keep up with the vast number of scientific articles being published, there is a need for more easily accessible representations of the current knowledge. The goal of the research described in this paper is to develop a system aimed to support the large-scale research on metabolic and regulatory pathways by extracting relations between biological objects from descriptions found in literature. We present and evaluate the procedures for semantico-syntactic tagging, dividing the text into parts concerning previous research and current research, syntactic parsing, and transformation of syntactic trees into logical representations similar to the pathway graphs utilized in the Kyoto Encyclopaedia of Genes and Genomes. [ABSTRACT FROM AUTHOR]

Public biological databases contain vast amounts of rich data that can also be used to create and evaluate new biological hypothesis. We propose a method for link discovery in biological databases, i.e., for prediction and evaluation of implicit or previously unknown connections between biological entities and concepts. In our framework, information extracted from available databases is represented as a graph, where vertices correspond to entities and concepts, and edges represent known, annotated relationships between vertices. A link, an (implicit and possibly unknown) relation between two entities is manifested as a path or a subgraph connecting the corresponding vertices. We propose measures for link goodness that are based on three factors: edge reliability, relevance, and rarity. We handle these factors with a proper probabilistic interpretation. We give practical methods for finding and evaluating links in large graphs and report experimental results with Alzheimer genes and protein interactions. [ABSTRACT FROM AUTHOR]

The goal of the EU FP6 project DIAMONDS http://www.sbcellcycle.org is to build a computational platform for studying the cell-cycle regulation process in several different (model) organisms (S. cerevisiae, S. pombe, A. thaliana and human). This platform will enable wet-lab biologists to use a systems biology approach encompassing data integration, modeling and simulation, thereby supporting analysis and interpretation of biochemical pathways involved in the cell cycle. To facilitate the computational handling of cell-cycle specific knowledge a detailed cell-cycle ontology is essential. The currently existing cell-cycle branch of the Gene Ontology (GO) provides only a static view and it is not rich enough to support in-depth cell-cycle studies. In this work, an enhanced Cell-Cycle Ontology (CCO) is proposed as an extension to existing GO. Besides the classical add-ons given by an ontology (data repository, knowledge sharing, validation, annotation, and so on), CCO is intended to further evolve into a knowledge-based system that provides reasoning services oriented to hypotheses evaluation in the context of cell-cycle studies. A data integration pipeline prototype, covering the entire life cycle of the knowledge base, is presented. Concrete problems and initial results related to the implementation of automatic format mappings between ontologies and inconsistency checking issues are discussed in detail. [ABSTRACT FROM AUTHOR]

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources. [ABSTRACT FROM AUTHOR]