Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Ondex Web is a new web-based implementation of the network visualization and exploration tools from the Ondex data integration platform. New features such as context-sensitive menus and annotation tools provide users with intuitive ways to explore and manipulate the appearance of heterogeneous biological networks. Ondex Web is open source, written in Java and can be easily embedded into Web sites as an applet. Ondex Web supports loading data from a variety of network formats, such as XGMML, NWB, Pajek and OXL. AVAILABILITY AND IMPLEMENTATION: http://ondex.rothamsted.ac.uk/OndexWeb.

BACKGROUND: In response to the rapid growth of available genome sequences, efforts have been made to develop automatic inference methods to functionally characterize them. Pipelines that infer functional annotation are now routinely used to produce new annotations at a genome scale and for a broad variety of species. These pipelines differ widely in their inference algorithms, confidence thresholds and data sources for reasoning. This heterogeneity makes a comparison of the relative merits of each approach extremely complex. The evaluation of the quality of the resultant annotations is also challenging given there is often no existing gold-standard against which to evaluate precision and recall. RESULTS: In this paper, we present a pragmatic approach to the study of functional annotations. An ensemble of 12 metrics, describing various aspects of functional annotations, is defined and implemented in a unified framework, which facilitates their systematic analysis and inter-comparison. The use of this framework is demonstrated on three illustrative examples: analysing the outputs of state-of-the-art inference pipelines, comparing electronic versus manual annotation methods, and monitoring the evolution of publicly available functional annotations. The framework is part of the AIGO library (http://code.google.com/p/aigo) for the Analysis and the Inter-comparison of the products of Gene Ontology (GO) annotation pipelines. The AIGO library also provides functionalities to easily load, analyse, manipulate and compare functional annotations and also to plot and export the results of the analysis in various formats. CONCLUSIONS: This work is a step toward developing a unified framework for the systematic study of GO functional annotations. This framework has been designed so that new metrics on GO functional annotations can be added in a very straightforward way.

BACKGROUND: Combining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems. RESULTS: We have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters. CONCLUSIONS: Relationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.

Brief Bioinform 2009;10,676–93. doi:10.1093/bib/bbp047. In the above article, Artem Lysenko’s name was incorrectly listed as ‘Atem Lysenko’. The publisher apologises for this error.

The development of a systems based approach to problems in plant sciences requires integration of existing information resources. However, the available information is currently often incomplete and dispersed across many sources and the syntactic and semantic heterogeneity of the data is a challenge for integration. In this article, we discuss strategies for data integration and we use a graph based integration method (Ondex) to illustrate some of these challenges with reference to two example problems concerning integration of (i) metabolic pathway and (ii) protein interaction data for Arabidopsis thaliana. We quantify the degree of overlap for three commonly used pathway and protein interaction information sources. For pathways, we find that the AraCyc database contains the widest coverage of enzyme reactions and for protein interactions we find that the IntAct database provides the largest unique contribution to the integrated dataset. For both examples, however, we observe a relatively small amount of data common to all three sources. Analysis and visual exploration of the integrated networks was used to identify a number of practical issues relating to the interpretation of these datasets. We demonstrate the utility of these approaches to the analysis of groups of coexpressed genes from an individual microarray experiment, in the context of pathway information and for the combination of coexpression data with an integrated protein interaction network.

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.

UNLABELLED: The high cost of genotyping single nucleotide polymorphisms (SNPs) generally prohibits the systematic mapping of entire genetic linkage regions in order to find the polymorphisms associated with increased risk of disease. In practice, SNPs are selected at approximately equal spacing across the linkage region to try to locate a SNP lying in the haplotype block of the disease SNP. The size of the haplotype block may not be known, however, and SNPs taken from public domain sources may not in fact be polymorphic. Our program will choose a subset of the SNPs in a linkage region so as to maximize the expected proportion of the sequence that lies within a given distance of a real SNP. AVAILABILITY: The software is available, free of charge, for academic use on request from the authors. SUPPLEMENTARY INFORMATION: www.oxagen.co.uk

UNLABELLED: Certain types of genetic analysis are simplified by assembling a collection of unrelated individuals, e.g. case-control experiments. If a family study is being performed then it will be necessary to extract subsets of unrelated, available individuals from pedigress. Our program provides an optimal method for performing this task. AVAILABILITY: The software is available, free of charge, on request from Oxagen Ltd. SUPPLEMENTARY INFORMATION: http://www.oxagen.co.uk

This review provides a description of the background to the first international conference on Intelligent Systems for Molecular Biology and a problem-oriented overview of the papers presented. It focuses on genome analysis, gene identification, protein and RNA structure prediction and function, the modelling of biochemical pathways and data and knowledge bases. The range and quality of papers indicates that intelligent systems is emerging as an important sub-field of computational applications in the biological sciences.

Many areas of scientific endeavour can be characterized as the attempt to provide a consistent interpretation of a broad range of heterogeneous data and theories. In the area of protein structure prediction, for example, there are many types of diverse mutually constraining data and theories of protein structural organization that need to be integrated in order to produce a single consistent prediction (or set of predictions) of the protein structure from the experimentally derived amino acid sequence data. Understanding the role and function of proteins in the control of cell growth is an important part of contemporary cancer research. Protein structure prediction is immensely (combinatorially) complex, and traditional computational approaches to problems such as this have been based on the ‘generate and test’ paradigm in which hypotheses are first generated and then tested against any relevant constraints. In this paper we demonstrate the benefits of a new approach to solving large constrained combinatorial problems which uses the ‘constrain-and-generate’ paradigm and the ElipSys parallel constraint logic programming system. In ElipSys, constraints are used for a priori pruning of the search tree while parallelism enhances the efficiency of the remaining search. Initial results show several orders of magnitude increase in performance over a sequential logic programming (Prolog) approach. The improved performance can be attributed to the complementary actions of the constraint handling and support for parallelism in the ElipSys runtime system. Taken together, the additional performance and new knowledge representation techniques made possible using ElipSys significantly extend the range and complexity of scientific problems that can be addressed using logic programming languages.