Towards the understanding of the cocoa transcriptome: Production and analysis of an exhaustive dataset of ESTs of Theobroma cacao L. generated from various tissues and under various conditions

Theobroma cacao L., is a tree originated from the tropical rainforest of South America. It is one of the major cash crops for many tropical countries. T. cacao is mainly produced on smallholdings, providing resources for 14 million farmers. Disease resistance and T. cacao quality improvement are two important challenges for all actors of cocoa and chocolate production. T. cacao is seriously affected by pests and fungal diseases, responsible for more than 40% yield losses and quality improvement, nutritional and organoleptic, is also important for consumers. An international collaboration was formed to develop an EST genomic resource database for cacao. Fifty-six cDNA libraries were constructed from different organs, different genotypes and different environmental conditions. A total of 149,650 valid EST sequences were generated corresponding to 48,594 unigenes, 12,692 contigs and 35,902 singletons. A total of 29,849 unigenes shared significant homology with public sequences from other species. Gene Ontology (GO) annotation was applied to distribute the ESTs among the main GO categories. A specific information system (ESTtik) was constructed to process, store and manage this EST collection allowing the user to query a database. To check the representativeness of our EST collection, we looked for the genes known to be involved in two different metabolic pathways extensively studied in other plant species and important for T. cacao qualities: the flavonoid and the terpene pathways. Most of the enzymes described in other crops for these two metabolic pathways were found in our EST collection. A large collection of new genetic markers was provided by this ESTs collection. This EST collection displays a good representation of the T. cacao transcriptome, suitable for analysis of biochemical pathways based on oligonucleotide microarrays derived from these ESTs. It will provide numerous genetic markers that will allow the construction of a high density gene map of T. cacao. This EST collection represents a unique and important molecular resource for T. cacao study and improvement, facilitating the discovery of candidate genes for important T. cacao trait variation. (Résumé d'auteur

ConsensusPathDB—a database for integrating human functional interaction networks

ConsensusPathDB is a database system for the integration of human functional interactions. Current knowledge of these interactions is dispersed in more than 200 databases, each having a specific focus and data format. ConsensusPathDB currently integrates the content of 12 different interaction databases with heterogeneous foci comprising a total of 26 133 distinct physical entities and 74 289 distinct functional interactions (protein–protein interactions, biochemical reactions, gene regulatory interactions), and covering 1738 pathways. We describe the database schema and the methods used for data integration. Furthermore, we describe the functionality of the ConsensusPathDB web interface, where users can search and visualize interaction networks, upload, modify and expand networks in BioPAX, SBML or PSI-MI format, or carry out over-representation analysis with uploaded identifier lists with respect to substructures derived from the integrated interaction network. The ConsensusPathDB database is available at: http://cpdb.molgen.mpg.d

Novel developments in SBGN-ED and applications

Systems Biology Graphical Notation (SBGN, http://sbgn.org) [1] is an emerging standard for graphical representations of biochemical and cellular processes studied in systems biology. Three different views (Process Description, Entity Relationship, and Activity Flow) cover several aspects of the represented processes in different levels of detail. SBGN helps to communicate biological knowledge more efficient and accurate between different research communities in the life sciences. However, to support SBGN, methods and tools for editing, validating, and translating of SBGN maps are necessary.
We present methods for these tasks and novel developments in SBGN-ED (www.sbgn-ed.org) [2], a tool which allows to create all three types of SBGN maps from scratch, to validate these maps for syntactical and semantical correctness, to translate maps from the KEGG database into SBGN, and to export SBGN maps into several file and image formats. SBGN-ED is based on VANTED (Visualization and Analysis of NeTworks containing Experimental Data, http://www.vanted.org) [3].
As applications of SBGN and SBGN-ED we present furthermore MetaCrop (http://metacrop.ipk-gatersleben.de) [4], a database that summarizes diverse information about metabolic pathways in crop plants, and RIMAS (Regulatory Interaction Maps of Arabidopsis Seed Development, http://rimas.ipk-gatersleben.de) [5], an information portal that provides a comprehensive overview of regulatory pathways and genetic interactions during Arabidopsis embryo and seed development. 

[1] Le Novère, N. et al. (2009) The Systems Biology Graphical Notation. Nature Biotechnology, 27, 735-741.
[2] Czauderna, T., Klukas, C., Schreiber, F. (2010) Editing, validating, and translating of SBGN maps. Bioinformatics, 26 (18), 2340-2341.
[3] Junker, B.H., Klukas, C., Schreiber, F. (2006) VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics, 7, 109+.
[4] Grafahrend-Belau, E., Weise, S., Koschützki, D., Scholz, U., Junker, B.H., Schreiber, F. (2008) MetaCrop - A detailed database of crop plant metabolism. Nucleic Acids Research, 36, D954-D958.
[5] Junker, A., Hartmann, A., Schreiber, F., Bäumlein, H. (2010) An engineer's view on regulation of seed development. Trends in Plant Science, 15(6), 303-307.
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Multi-membership gene regulation in pathway based microarray analysis

This article is available through the Brunel Open Access Publishing Fund. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results: We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions: We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.The work was sponsored by the studentship scheme of the School of Information Systems, Computing and Mathematics, Brunel Universit

Representing and analysing molecular and cellular function in the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed

Expansion of the BioCyc collection of pathway/genome databases to 160 genomes

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing