The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis

We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/     

MetaCyc and AraCyc. Metabolic pathway databases for plant research

MetaCyc (http://metacyc.org) contains experimentally determined biochemical pathways to be used as a reference database for metabolism. In conjunction with the Pathway Tools software, MetaCyc can be used to computationally predict the metabolic pathway complement of an annotated genome. To increase the breadth of pathways and enzymes, more than 60 plant-specific pathways have been added or updated in MetaCyc recently. In contrast to MetaCyc, which contains metabolic data for a wide range of organisms, AraCyc is a species-specific database containing only enzymes and pathways found in the model plant Arabidopsis (Arabidopsis thaliana). AraCyc (http://arabidopsis.org/tools/aracyc/) was the first computationally predicted plant metabolism database derived from MetaCyc. Since its initial computational build, AraCyc has been under continued curation to enhance data quality and to increase breadth of pathway coverage. Twenty-eight pathways have been manually curated from the literature recently. Pathway predictions in AraCyc have also been recently updated with the latest functional annotations of Arabidopsis genes that use controlled vocabulary and literature evidence. AraCyc currently features 1,418 unique genes mapped onto 204 pathways with 1,156 literature citations. The Omics Viewer, a user data visualization and analysis tool, allows a list of genes, enzymes, or metabolites with experimental values to be painted on a diagram of the full pathway map of AraCyc. Other recent enhancements to both MetaCyc and AraCyc include implementation of an evidence ontology, which has been used to provide information on data quality, expansion of the secondary metabolism node of the pathway ontology to accommodate curation of secondary metabolic pathways, and enhancement of the cellular component ontology for storing and displaying enzyme and pathway locations within subcellular compartments.     

AGRA: analysis of gene ranking algorithms

Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. AVAILABILITY AND IMPLEMENTATION: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. CONTACT: simon.kocbek@uni-mb.si.     

AraCyc: a biochemical pathway database for Arabidopsis

AraCyc is a database containing biochemical pathways of Arabidopsis, developed at The Arabidopsis Information Resource (http://www.arabidopsis.org). The aim of AraCyc is to represent Arabidopsis metabolism as completely as possible with a user-friendly Web-based interface. It presently features more than 170 pathways that include information on compounds, intermediates, cofactors, reactions, genes, proteins, and protein subcellular locations. The database uses Pathway Tools software, which allows the users to visualize a bird's eye view of all pathways in the database down to the individual chemical structures of the compounds. The database was built using Pathway Tools' Pathologic module with MetaCyc, a collection of pathways from more than 150 species, as a reference database. This initial build was manually refined and annotated. More than 20 plant-specific pathways, including carotenoid, brassinosteroid, and gibberellin biosyntheses have been added from the literature. A list of more than 40 plant pathways will be added in the coming months. The quality of the initial, automatic build of the database was compared with the manually improved version, and with EcoCyc, an Escherichia coli database using the same software system that has been manually annotated for many years. In addition, a Perl interface, PerlCyc, was developed that allows programmers to access Pathway Tools databases from the popular Perl language. AraCyc is available at the tools section of The Arabidopsis Information Resource Web site (http://www.arabidopsis.org/tools/aracyc).     

KEGG spider: interpretation of genomics data in the context of the global gene metabolic network

KEGG spider is a web-based tool for interpretation of experimentally derived gene lists in order to gain understanding of metabolism variations at a genomic level. KEGG spider implements a 'pathway-free' framework that overcomes a major bottleneck of enrichment analyses: it provides global models uniting genes from different metabolic pathways. Analyzing a number of experimentally derived gene lists, we demonstrate that KEGG spider provides deeper insights into metabolism variations in comparison to existing methods.     

KEGG spider: interpretation of genomics data in the context of the global gene metabolic network

KEGG spider is a web-based tool for interpretation of experimentally derived gene lists in order to gain understanding of metabolism variations at a genomic level. KEGG spider implements a 'pathway-free' framework that overcomes a major bottleneck of enrichment analyses: it provides global models uniting genes from different metabolic pathways. Analyzing a number of experimentally derived gene lists, we demonstrate that KEGG spider provides deeper insights into metabolism variations in comparison to existing methods.     

The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized,curated gateway to Arabidopsis biology,research materials and community

Arabidopsis thaliana is the most widely-studied plant today. The concerted efforts of over 11 000 researchers and 4000 organizations around the world are generating a rich diversity and quantity of information and materials. This information is made available through a comprehensive on-line resource called the Arabidopsis Information Resource (TAIR) (http://arabidopsis.org), which is accessible via commonly used web browsers and can be searched and downloaded in a number of ways. In the last two years, efforts have been focused on increasing data content and diversity, functionally annotating genes and gene products with controlled vocabularies, and improving data retrieval, analysis and visualization tools. New information include sequence polymorphisms including alleles, germplasms and phenotypes, Gene Ontology annotations, gene families, protein information, metabolic pathways, gene expression data from microarray experiments and seed and DNA stocks. New data visualization and analysis tools include SeqViewer, which interactively displays the genome from the whole chromosome down to 10 kb of nucleotide sequence and AraCyc, a metabolic pathway database and map tool that allows overlaying expression data onto the pathway diagrams. Finally, we have recently incorporated seed and DNA stock information from the Arabidopsis Biological Resource Center (ABRC) and implemented a shopping-cart style on-line ordering system.     

Ovarian Kaleidoscope database: ten years and beyond

Ovarian Kaleidoscope database (OKdb) is an online, searchable, public database containing text-based and DNA microarray data to facilitate research by ovarian researchers. Using key words and predetermined categories, users can search ovarian gene information based on gene function, cell type of expression, cellular localization, hormonal regulation, mutant phenotypes, chromosomal location, ligand-receptor relationship, and other criteria, either alone or in combination. For individual genes, users can access more than 10 extensive DNA microarray datasets to interrogate gene expression patterns in a development-specific and cell type-specific manner. All ligand and receptor genes expressed in the ovary are matched to facilitate investigation of paracrine/autocrine signaling. More than 3500 ovarian genes in the database are matched to 185 gene pathways in the Kyoto Encyclopedia of Genes and Genomes to allow for elucidation of gene interactions and relationships. In addition to >400 genes with infertility or subfertility phenotypes when mutated in mice or humans, the OKdb also lists ~50 and ~40 genes associated with polycystic ovarian syndrome and primary ovarian insufficiency, respectively. The expanding OKdb is updated weekly and allows submission of new genes by ovarian researchers to allow instant access to DNA microarray datasets for newly submitted genes. The present database is a virtual community for ovarian researchers and allows users to instantaneously provide their comments for individual gene pages based on an automated Web-discussion system. In the coming years, we will continue to add new features to serve the ovarian research community.     

GOCompare: An R package to compare functional enrichment analysis between two species

Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments.     

List mania: interpreting microarray results with the L2L server

A new server for interpreting microarray results, list to list(L2L), is described. This tool offers a unique approach to understandthe meaning of microarray results, based on comparing them topreviously identified lists of differentially expressed genes.The usefulness of the server is demonstrated by studying differentialexpression in primary tumours versus metastases in colon cancer.     

Probing differentially expressed genes against a microarray database for in silico suppressor/enhancer and inhibitor/activator screens

High-density oligonucleotide arrays are widely used for analysis of gene expression on a genomic scale, but the generated data remain largely inaccessible for comparative analysis purposes. Similarity searches in databases with differentially expressed gene (DEG) lists may be used to assign potential functions to new genes and to identify potential chemical inhibitors/activators and genetic suppressors/enhancers. Although this is a very promising concept, it requires the compatibility and validity of the DEG lists to be significantly improved. Using Arabidopsis and human datasets, we have developed guidelines for the performance of similarity searches against databases that collect microarray data. We found that, in comparison with many other methods, a rank-product analysis achieves a higher degree of inter- and intra-laboratory consistency of DEG lists, and is advantageous for assessing similarities and differences between them. To support this concept, we developed a tool called MASTA (microarray overlap search tool and analysis), and re-analyzed over 600 Arabidopsis microarray expression datasets. This revealed that large-scale searches produce reliable intersections between DEG lists that prove to be useful for genetic analysis, thus aiding in the characterization of cellular and molecular mechanisms. We show that this approach can be used to discover unexpected connections and to illuminate unanticipated interactions between individual genes.     

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.     

The Arabidopsis SeedGenes Project

The SeedGenes database (http://www.seedgenes.org) presents molecular and phenotypic information on essential, non-redundant genes of Arabidopsis that give a seed phenotype when disrupted by mutation. Experimental details are synthesized for efficient use by the community and organized into two major sections in the database, one dealing with genes and the other with mutant alleles. The database can be queried for detailed information on a single gene to create a SeedGenes Profile. Queries can also generate lists of genes or mutants that fit specified criteria. The long-term goal is to establish a complete collection of Arabidopsis genes that give a knockout phenotype. This information is needed to focus attention on genes with important cellular functions in a model plant and to assess from a genetic perspective the extent of functional redundancy in the Arabidopsis genome.     

Prioritisation and Network Analysis of Crohn's Disease Susceptibility Genes

Recent Genome-Wide Association Studies (GWAS) have revealed numerous Crohn''s disease susceptibility genes and a key challenge now is in understanding how risk polymorphisms in associated genes might contribute to development of this disease. For a gene to contribute to disease phenotype, its risk variant will likely adversely communicate with a variety of other gene products to result in dysregulation of common signaling pathways. A vital challenge is to elucidate pathways of potentially greatest influence on pathological behaviour, in a manner recognizing how multiple relevant genes may yield integrative effect. In this work we apply mathematical analysis of networks involving the list of recently described Crohn''s susceptibility genes, to prioritise pathways in relation to their potential development of this disease. Prioritisation was performed by applying a text mining and a diffusion based method (GRAIL, GPEC). Prospective biological significance of the resulting prioritised list of proteins is highlighted by changes in their gene expression levels in Crohn''s patients intestinal tissue in comparison with healthy donors.     

AtGGM2014, an Arabidopsis gene co-expression network for functional studies

Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies.     

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.     

GO‐At : in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO‐At (gene ontology prediction in A. thaliana), a method that combines five data types (co‐expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two‐step approach, GO‐At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the ‘best hit’ from the first list, and achieves success rates of up to 79%. GO‐At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO‐At’s ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi‐associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web‐based implementation of GO‐At ( http://www.bioinformatics.leeds.ac.uk/goat ) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.