The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) provides a non-redundant collection of sequences representing genomic data, transcripts and proteins. Although the goal is to provide a comprehensive dataset representing the complete sequence information for any given species, the database pragmatically includes sequence data that are currently publicly available in the archival databases. The database incorporates data from over 2400 organisms and includes over one million proteins representing significant taxonomic diversity spanning prokaryotes, eukaryotes and viruses. Nucleotide and protein sequences are explicitly linked, and the sequences are linked to other resources including the NCBI Map Viewer and Gene. Sequences are annotated to include coding regions, conserved domains, variation, references, names, database cross-references, and other features using a combined approach of collaboration and other input from the scientific community, automated annotation, propagation from GenBank and curation by NCBI staff.

NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

Summary In conclusion, the eFP Browser is a convenient tool forinterpreting and visualizing gene expression and other data. Notonly is it valuable for its compatibility to existing resources but ithas also been loaded with several useful data sets. The variousmodes and other features allow the user to extract an array ofconclusions and/or generate useful hypotheses. We hope thatmany researchers will be able to use the eFP Browser both tounderstand particular microarray or other experimental results, aswell as to communicate their own findings. MATERIALS AND METHODS The eFP Browser is implemented in Python and makes use of thePython Imaging Library (PIL) Build 1.1.5 (www.python.org),which we modified to provide an optimized flood pixel re-placement function called replaceFill, and other Python modules,as described on the eFP Browser development homepage. Theinputs for the eFP Browser are illustrated in Figure 1. Apictographic representation of the sample collection as a Targa-based image is required, as is an XML control file, shown in detailin Figure 1B. Two other inputs are a database of gene identifiersand their appropriate microarray element lookups and annota-tions, and a database of gene expression values for the givensamples. In the case of the Arabidopsis, Cell and Mouse eFPBrowsers, we have mirrored publicly-available microarray datafrom several sources – described in the Data Sources andsubsequent two sections – in our Bio-Array Resource [10]. Theseinputs are used by the eFP Browser algorithm to generate anoutput image for a user’s gene identifier.The eFP Browser algorithm itself is programmed in an object-oriented manner. The main program, efpWeb.cgi, is responsiblefor the creation of the HTML code for the user interface andpresentation of the output image. It calls on four modules tocomplete the task. These modules are 1) efp.py, which performsmost of the functions for the generation of the output image,including the parsing of the XML control file, average andstandard deviation calculations, fold-change relative to controlvalue calculations, and image map HTML code; 2) efpDb.py,which connects to the gene expression, microarray element andannotation databases, and returns the appropriate values uponbeing called; 3) efpImg.py, which formulates the actual colourreplace calls on the Targa input image; and 4) efpXML.py, whichidentifies the XML control files that are present in the eFPBrowser’s data directory. These are displayed to the user in theData Source drop-down, thus obviating the need to have themhard-coded in the main efpWeb.cgi program.In the case of the Cell eFP Browser, data in the SUBAdatabase indicate the presence of a given protein in a particularsubcellular location, either based on computational methods or asmolecularly documented by mass spectrometric analysis ofsubcellular fractions, GFP fusions etc. [11]. We have used a simpleheuristic to turn these data into a confidence score for a given geneproduct’s presence in a given subcellular compartment:confidence~X

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An “Electronic Fluorescent Pictograph” Browser for Exploring and Analyzing Large-Scale Biological Data Sets

Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops.

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Genome sequencing and analysis of the model grass Brachypodium distachyon

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Genome sequencing and analysis of themodel grass Brachypodium distachyon

Cytosine methylation, a common form of DNA modification that antagonizes transcription, is found at transposons and repeats in vertebrates, plants and fungi. Here we have mapped DNA methylation in the entire Arabidopsis thaliana genome at high resolution. DNA methylation covers transposons and is present within a large fraction of A. thaliana genes. Methylation within genes is conspicuously biased away from gene ends, suggesting a dependence on RNA polymerase transit. Genic methylation is strongly influenced by transcription: moderately transcribed genes are most likely to be methylated, whereas genes at either extreme are least likely. In turn, transcription is influenced by methylation: short methylated genes are poorly expressed, and loss of methylation in the body of a gene leads to enhanced transcription. Our results indicate that genic transcription and DNA methylation are closely interwoven processes.

Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription

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.

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

Controlled vocabularies are increasingly used by databases to describe genes and gene products because they facilitate identification of similar genes within an organism or among different organisms. One of The Arabidopsis Information Resource's goals is to associate all Arabidopsis genes with terms developed by the Gene Ontology Consortium that describe the molecular function, biological process, and subcellular location of a gene product. We have also developed terms describing Arabidopsis anatomy and developmental stages and use these to annotate published gene expression data. As of March 2004, we used computational and manual annotation methods to make 85,666 annotations representing 26,624 unique loci. We focus on associating genes to controlled vocabulary terms based on experimental data from the literature and use The Arabidopsis Information Resource-developed PubSearch software to facilitate this process. Each annotation is tagged with a combination of evidence codes, evidence descriptions, and references that provide a robust means to assess data quality. Annotation of all Arabidopsis genes will allow quantitative comparisons between sets of genes derived from sources such as microarray experiments. The Arabidopsis annotation data will also facilitate annotation of newly sequenced plant genomes by using sequence similarity to transfer annotations to homologous genes. In addition, complete and up-to-date annotations will make unknown genes easy to identify and target for experimentation. Here, we describe the process of Arabidopsis functional annotation using a variety of data sources and illustrate several ways in which this information can be accessed and used to infer knowledge about Arabidopsis and other plant species.

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Mary Montoya

Papers

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

Functional Annotation of the Arabidopsis Genome Using Controlled Vocabularies