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At least 397 records · Page 22Linked to original sources
PubMed2005

Genome-wide analysis of gene transcription in the hypothalamus.

As the genomic regions containing loci predisposing to obesity-related traits are mapped in human population screens and mouse genetic studies, identification of susceptibility genes will increasingly be facilitated by bioinformatic methods. We hypothesized that candidate genes can be prioritized by their expression levels in tissues of central importance in obesity. Our objective was to develop a combined bioinformatics and molecular paradigm to identify novel genes as candidates for murine or human obesity genetic modifiers based on their differential expression patterns in the hypothalamus compared with other murine tissues. We used bioinformatics tools to search publicly available gene expression databases using criteria designed to identify novel genes differentially expressed in the hypothalamus. We used RNA methods to determine their expression sites and levels of expression in the hypothalamus of the murine brain. We identified the chromosomal location of the novel genes in mice and in humans and compared these locations with those of genetic loci predisposing to obesity-related traits. We developed a search strategy that correctly identified a set of genes known to be important in hypothalamic function as well as a candidate gene for Prader-Willi syndrome that was not previously identified as differentially expressed in the hypothalamus. Using this same strategy, we identified and characterized a set of 11 genes not previously known to be differentially expressed in the murine hypothalamus. Our results demonstrate the feasibility of combined bioinformatics and molecular approaches to the identification of genes that are candidates for obesity-related disorders in humans and mice.

Animals↗
PubMed2004

Light-weight integration of molecular biological databases.

MOTIVATION: Due to the increasing number of molecular biological databases and the exponential growth of their contents, database integration is an important topic of research in bioinformatics. Existing approaches in this area have in common that considerable efforts are needed to provide integrated access to heterogeneous data sources. RESULTS: This article describes the LIMBO architecture as a light-weight approach to molecular biological database integration. By building systems upon this architecture, the efforts needed for database integration can be significantly lowered. AVAILABILITY: As an illustration of the principle usefulness of the underlying ideas, a prototypical implementation based upon the LIMBO architecture is described. This implementation is exclusively based on freely available open source components like the PostgreSQL database management system and the BioRuby project. Additional files and modified components are available upon request from the author.

Computational Biology↗
PubMed2004

SS-Wrapper: a package of wrapper applications for similarity searches on Linux clusters.

BACKGROUND: Large-scale sequence comparison is a powerful tool for biological inference in modern molecular biology. Comparing new sequences to those in annotated databases is a useful source of functional and structural information about these sequences. Using software such as the basic local alignment search tool (BLAST) or HMMPFAM to identify statistically significant matches between newly sequenced segments of genetic material and those in databases is an important task for most molecular biologists. Searching algorithms are intrinsically slow and data-intensive, especially in light of the rapid growth of biological sequence databases due to the emergence of high throughput DNA sequencing techniques. Thus, traditional bioinformatics tools are impractical on PCs and even on dedicated UNIX servers. To take advantage of larger databases and more reliable methods, high performance computation becomes necessary. RESULTS: We describe the implementation of SS-Wrapper (Similarity Search Wrapper), a package of wrapper applications that can parallelize similarity search applications on a Linux cluster. Our wrapper utilizes a query segmentation-search (QS-search) approach to parallelize sequence database search applications. It takes into consideration load balancing between each node on the cluster to maximize resource usage. QS-search is designed to wrap many different search tools, such as BLAST and HMMPFAM using the same interface. This implementation does not alter the original program, so newly obtained programs and program updates should be accommodated easily. Benchmark experiments using QS-search to optimize BLAST and HMMPFAM showed that QS-search accelerated the performance of these programs almost linearly in proportion to the number of CPUs used. We have also implemented a wrapper that utilizes a database segmentation approach (DS-BLAST) that provides a complementary solution for BLAST searches when the database is too large to fit into the memory of a single node. CONCLUSIONS: Used together, QS-search and DS-BLAST provide a flexible solution to adapt sequential similarity searching applications in high performance computing environments. Their ease of use and their ability to wrap a variety of database search programs provide an analytical architecture to assist both the seasoned bioinformaticist and the wet-bench biologist.

Algorithms↗
PubMed2006

A comprehensive SAGE database for the analysis of gammadelta T cells.

Gammadelta T cells have been conserved since the adaptive immune system arose, yet their importance is still unclear. In an attempt to compensate for the lack of a broad knowledge base of gammadelta T cells across species, global analyses of gammadelta T cell transcriptomes have been performed using serial analysis of gene expression (SAGE). Twelve new SAGE libraries were generated from the following bovine lymphocyte populations: magnetic bead-sorted blood gammadelta T cells, spleen gammadelta T cells and enriched spleen alphabeta T cells from a single calf, both rested and Con A/IL2 stimulated, and flow cytometry-sorted blood gammadelta and alphabeta T cells each either rested, Con A/IL2, or phorbol 12 myristate 13-acetate/ionomycin stimulated. These new libraries complement two earlier SAGE libraries of circulating gammadelta T cell subsets. These databases were analyzed using new web-based bioinformatic tools, which allow the user to rapidly compare gene expression patterns within these and other SAGE and standard expressed sequence tag libraries generated from different cell types and different species. These analyses revealed striking differences between blood and spleen gammadelta T cells and how these cells respond to mitogenic stimulation. These analyses also confirm previous studies that suggested that global gene expression in gammadelta and alphabeta T cells is quite similar; however, a 5-fold increase in gammadelta T cell-specific transcripts could be induced by Con A/IL2 stimulation. These new public databases provide additional resources for the annotation/analysis of global gene expression in gammadelta T cells, which will facilitate studies of the biology of this enigmatic lymphoid cell.

Animals↗
PubMed2005

PineappleDB: an online pineapple bioinformatics resource.

BACKGROUND: A world first pineapple EST sequencing program has been undertaken to investigate genes expressed during non-climacteric fruit ripening and the nematode-plant interaction during root infection. Very little is known of how non-climacteric fruit ripening is controlled or of the molecular basis of the nematode-plant interaction. PineappleDB was developed to provide the research community with access to a curated bioinformatics resource housing the fruit, root and nematode infected gall expressed sequences. DESCRIPTION: PineappleDB is an online, curated database providing integrated access to annotated expressed sequence tag (EST) data for cDNA clones isolated from pineapple fruit, root, and nematode infected root gall vascular cylinder tissues. The database currently houses over 5600 EST sequences, 3383 contig consensus sequences, and associated bioinformatic data including splice variants, Arabidopsis homologues, both MIPS based and Gene Ontology functional classifications, and clone distributions. The online resource can be searched by text or by BLAST sequence homology. The data outputs provide comprehensive sequence, bioinformatic and functional classification information. CONCLUSION: The online pineapple bioinformatic resource provides the research community with access to pineapple fruit and root/gall sequence and bioinformatic data in a user-friendly format. The search tools enable efficient data mining and present a wide spectrum of bioinformatic and functional classification information. PineappleDB will be of broad appeal to researchers investigating pineapple genetics, non-climacteric fruit ripening, root-knot nematode infection, crassulacean acid metabolism and alternative RNA splicing in plants.

Alternative Splicing↗
PubMed2006

FGDB: a comprehensive fungal genome resource on the plant pathogen Fusarium graminearum.

The MIPS Fusarium graminearum Genome Database (FGDB) is a comprehensive genome database on one of the most devastating fungal plant pathogens of wheat and barley. FGDB provides information on two gene sets independently derived by automated annotation of the F.graminearum genome sequence. A complete manually revised gene set will be completed within the near future. The initial results of systematic manual correction of gene calls are already part of the current gene set. The database can be accessed to retrieve information from bioinformatics analyses and functional classifications of the proteins. The data are also organized in the well established MIPS catalogs and novel query techniques are available to search the data. The comprehensive set of gene calls was also used for the design of an Affymetrix GeneChip. The resource is accessible on http://mips.gsf.de/genre/proj/fusarium/.

Databases, Genetic↗
PubMed2006

PepSeeker: a database of proteome peptide identifications for investigating fragmentation patterns.

Proteome science relies on bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. The resulting secondary ion mass spectra are compared with protein sequence databases in order to identify the amino acid sequence. Although these search tools (e.g. SEQUEST, Mascot, X!Tandem, Phenyx) are frequently successful, much is still not understood about the amino acid sequence patterns which promote/protect particular fragmentation pathways, and hence lead to the presence/absence of particular ions from different ion series. In order to advance this area, we have developed a database, PepSeeker (http://nwsr.smith.man.ac.uk/pepseeker), which captures this peptide identification and ion information from proteome experiments. The database currently contains >185,000 peptides and associated database search information. Users may query this resource to retrieve peptide, protein and spectral information based on protein or peptide information, including the amino acid sequence itself represented by regular expressions coupled with ion series information. We believe this database will be useful to proteome researchers wishing to understand gas phase peptide ion chemistry in order to improve peptide identification strategies. Questions can be addressed to j.selley@manchester.ac.uk.

Databases, Protein↗
PubMed2003

WU-Blast2 server at the European Bioinformatics Institute.

Since 1995, the WU-BLAST programs (http://blast.wustl.edu) have provided a fast, flexible and reliable method for similarity searching of biological sequence databases. The software is in use at many locales and web sites. The European Bioinformatics Institute's WU-Blast2 (http://www.ebi.ac.uk/blast2/) server has been providing free access to these search services since 1997 and today supports many features that both enhance the usability and expand on the scope of the software.

Computational Biology↗
PubMed2006

The Universal Protein Resource (UniProt): an expanding universe of protein information.

The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at http://www.uniprot.org or downloaded at ftp://ftp.uniprot.org/pub/databases/.

Databases, Protein↗
PubMed2005

High performance workflow implementation for protein surface characterization using grid technology.

BACKGROUND: This study concerns the development of a high performance workflow that, using grid technology, correlates different kinds of Bioinformatics data, starting from the base pairs of the nucleotide sequence to the exposed residues of the protein surface. The implementation of this workflow is based on the Italian Grid.it project infrastructure, that is a network of several computational resources and storage facilities distributed at different grid sites. METHODS: Workflows are very common in Bioinformatics because they allow to process large quantities of data by delegating the management of resources to the information streaming. Grid technology optimizes the computational load during the different workflow steps, dividing the more expensive tasks into a set of small jobs. RESULTS: Grid technology allows efficient database management, a crucial problem for obtaining good results in Bioinformatics applications. The proposed workflow is implemented to integrate huge amounts of data and the results themselves must be stored into a relational database, which results as the added value to the global knowledge. CONCLUSION: A web interface has been developed to make this technology accessible to grid users. Once the workflow has started, by means of the simplified interface, it is possible to follow all the different steps throughout the data processing. Eventually, when the workflow has been terminated, the different features of the protein, like the amino acids exposed on the protein surface, can be compared with the data present in the output database.

Automation↗