Challenges in modern biology demand shifting focus from components—genes and proteins—to their interacting whole. Integrating information from multiple genomic datasets is seen as a means to this end, capable of providing robust and accurate ways to unravel these functional associations. Integrative strategies, both novel and adapted from other well-studied organisms, are being employed in the model plant Arabidopsis thaliana to interpret genome-wide expression, metabolic profiling and protein interaction studies. Exciting inroads are being made in mining and interpretation of developmental, physiological and environmental-response ‘programs’ using sequence and functional information. The fundamental transcriptional regulatory logic is emerging in Arabidopsis, presently revealed as isolated conditional, spatial or temporal regulatory ‘modules’. This immediately calls for efforts towards assembling these building blocks together into a unifying model, thus creating standards for future work to compare with. As a young field, Arabidopsis systems biology is ripe with such an opportunity, now scarcely realizable in other model organisms.

Background Allotetraploid white clover (Trifolium repens L.) is an important forage legume widely cultivated in most temperate regions. Only a small number of microsatellite markers are publicly available and can be utilized in white clover breeding programs. The objectives of this study were to develop an integrated approach for microsatellite development and to evaluate the approach for the development of new SSR markers for white clover. Results Genomic libraries containing simple sequence repeat (SSR) sequences were constructed using a modified Fast Isolation by AFLP of Sequences COntaining repeats (FIASCO) procedure and phpSSRMiner was used to develop the microsatellite markers. SSR motifs were isolated using two biotin-labeled probes, (CA)17 and (ATG)12. The sequences of 6,816 clones were assembled into 1,698 contigs, 32% of which represented novel sequences based on BLASTN searches. Approximately 32%, 28%, and 16% of these SSRs contained hexa-, tri-, and di-nucleotide repeats, respectively. The most frequent motifs were the CA and ATG complementary repeats and the associated compound sequences. Primer pairs were designed for 859 SSR loci based on sequences from these genomic libraries and from GenBank white clover nucleotide sequences. A total of 191 SSR primers developed from the two libraries were tested for polymorphism in individual clones from the parental genotypes GA43 ('Durana'), 'SRVR' and six F1 progeny from a mapping population. Ninety two percent produced amplicons and 66% of these were polymorphic. Conclusion The combined approach of identifying SSR-enriched fragments by FIASCO coupled with the primer design and in silico amplification using phpSSRMiner represents an efficient and low cost pipeline for the large-scale development of microsatellite markers in plants. The approach described here could be readily adapted and utilized in other non-related species with none or limited genomic resources.

This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community.

Background Many common disorders have multiple genetic components which convey increased susceptibility. SNPs have been used to identify genetic components which are associated with a disease. Unfortunately, many studies using these methods suffer from low reproducibility due to lack of power. Results We present a set of programs which implement a novel method for searching for disease-associated genes using prior information to select and order genes from publicly available databases by their prior likelihood of association with the disease. These programs were used in a published study of childhood-onset SLE which yielded novel associations with modest sample size. Conclusions Using prior information to decrease the size of the problem space to an amount commensurate with available samples and resources while maintaining appropriate power enables researchers to increase their likelihood of discovering reproducible associations.

Large and medium scale computational molecular biology projects require accurate bioinformatics software and numerous heterogeneous biological databanks, which are distributed around the world. BioMAJ provides a flexible, robust, fully automated environment for managing such massive amounts of data. The JAVA application enables automation of the data update cycle process and supervision of the locally mirrored data repository. We have developed workflows that handle some of the most commonly used bioinformatics databases. A set of scripts is also available for post synchronization data treatment consisting of indexation or format conversion (for NCBI blast, SRS, EMBOSS, GCG, etc.). BioMAJ can be easily extended by personal homemade processing scripts. Source history can be kept via html reports containing statements of locally managed databanks. Availability: http://biomaj.genouest.org. BioMAJ is free open software.It is freely available under the CECILL version 2 license. Contact: biomaj@genouest.org Associate Editor: Prof. Dmitrij Frishman

Motivation: Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organising, and analysing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed. Methods: PuReD-MCL avoids using natural language processing (NLP) techniques directly; instead, it takes advantage of existing resources, available from PubMed. PuReD-MCL then clusters documents efficiently using the MCL graph clustering algorithm, which is based on graph flow simulation. This process allows users to analyse the results by highlighting important clues, and finally to visualise the clusters and all relevant information using an interactive graph layout algorithm, for instance BioLayout Express 3D. Results: The methodology was applied to two different datasets, previously used for the validation of the document clustering tool TextQuest. The first dataset involves the organisms E. coli and yeast, whereas the second is related to Drosophila development. PuReD-MCL successfully reproduces the annotated results obtained from TextQuest, while at the same time provides additional insights into the clusters and the corresponding documents. Availability: Source code in perl and R are available from http://tartara.csd.auth.gr/~theodos/

WikiProteins enables community annotation in a Wiki-based system. Extracts of major data sources have been fused into an editable environment that links out to the original sources. Data from community edits create automatic copies of the original data. Semantic technology captures concepts co-occurring in one sentence and thus potential factual statements. In addition, indirect associations between concepts have been calculated. We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery. The system is available for beta testing at http://www.wikiprofessional.org

Background The recent rapid accumulation of sequence resources of various crop species ensures an improvement in the genetics approach, including quantitative trait loci (QTL) analysis as well as the holistic population analysis and association mapping of natural variations. Because the tribe Triticeae includes important cereals such as wheat and barley, integration of information on the genetic markers in these crops should effectively accelerate map-based genetic studies on Triticeae species and lead to the discovery of key loci involved in plant productivity, which can contribute to sustainable food production. Therefore, informatics applications and a semantic knowledgebase of genome-wide markers are required for the integration of information on and further development of genetic markers in wheat and barley in order to advance conventional marker-assisted genetic analyses and population genomics of Triticeae species. Description: The Triticeae mapped expressed sequence tag (EST) database (TriMEDB) provides information, along with various annotations, regarding mapped cDNA markers that are related to barley and their homologues in wheat. The current version of TriMEDB provides map-location data for barley and wheat ESTs that were retrieved from 3 published barley linkage maps (the barley single nucleotide polymorphism database of the Scottish Crop Research Institute, the barley transcript map of Leibniz Institute of Plant Genetics and Crop Plant Research, and HarvEST barley ver. 1.63) and 1 diploid wheat map. These data were imported to CMap to allow the visualization of the map positions of the ESTs and interrelationships of these ESTs with public gene models and representative cDNA sequences. The retrieved cDNA sequences corresponding to each EST marker were assigned to the rice genome to predict an exon-intron structure. Furthermore, to generate a unique set of EST markers in Triticeae plants among the public domain, 3472 markers were assembled to form 2737 unique marker groups as contigs. These contigs were applied for pairwise comparison among linkage maps obtained from different EST map resources. Conclusions TriMEDB provides information regarding transcribed genetic markers and functions as a semantic knowledgebase offering an informatics facility for the acceleration of QTL analysis and for population genetics studies of Triticeae.

Background Research in the field of systems biology requires software for a variety of purposes. Software must be used to store, retrieve, analyze, and sometimes even to collect the data obtained from system-level (often high-throughput) experiments. Software must also be used to implement mathematical models and algorithms required for simulation and theoretical predictions on the system-level. Results We introduce a free, easy-to-use, open-source, integrated software platform called the Systems Biology Research Tool (SBRT) to facilitate the computational aspects of systems biology. The SBRT currently performs 35 methods for analyzing stoichiometric networks and 16 methods from fields such as graph theory, geometry, algebra, and combinatorics. New computational techniques can be added to the SBRT via process plug-ins, providing a high degree of evolvability and a unifying framework for software development in systems biology. Conclusions The Systems Biology Research Tool represents a technological advance for systems biology. This software can be used to make sophisticated computational techniques accessible to everyone (including those with no programming ability), to facilitate cooperation among researchers, and to expedite progress in the field of systems biology.

Systems biology represents an experimental approach to biology that attempts to study biological systems in a holistic rather than an atomistic manner. Ideally this involves gathering dynamic and global data sets as well as phenotypic data from different levels of the biological information hierarchy, integrating them and modeling them graphically and/or mathematically to generate mechanistic explanations for the emergent systems properties. This requires that the biological frontiers drive the development of new measurement and visualization technologies and the pioneering of new computational and mathematical tools—all of which requires a cross-disciplinary environment composed of biologists, chemists, computer scientists, engineers, mathematicians, physicists, and physicians speaking common discipline languages. The Institute for Systems Biology has aspired to pioneer and seamlessly integrate each of these concepts. Keywords: system, complexity, computation, networks, modeling, technology development