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Results for “Genomic database”

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At least 73 records · Page 4Linked to original sources
PubMed1999

Integrated pathway-genome databases and their role in drug discovery.

Integrated pathway-genome databases describe the genes and genome of an organism, as well as its predicted pathways, reactions, enzymes and metabolites. In conjunction with visualization and analysis software, these databases provide a framework for improved understanding of microbial physiology and for antimicrobial drug discovery. We describe pathway-based analyses of the genomes of a number of medically relevant microorganisms and a novel software tool that visualizes gene-expression data on a diagram showing the whole metabolic network of the microorganism.

Anti-Bacterial Agents↗
PubMed2006

TMBETA-GENOME: database for annotated beta-barrel membrane proteins in genomic sequences.

We have developed the database, TMBETA-GENOME, for annotated beta-barrel membrane proteins in genomic sequences using statistical methods and machine learning algorithms. The statistical methods are based on amino acid composition, reside pair preference and motifs. In machine learning techniques, the combination of amino acid and dipeptide compositions has been used as main attributes. In addition, annotations have been made using the criterion based on the identification of beta-barrel membrane proteins and exclusion of globular and transmembrane helical proteins. A web interface has been developed for identifying the annotated beta-barrel membrane proteins in all known genomes. The users have the feasibility of selecting the genome from the three kingdoms of life, archaea, bacteria and eukaryote, and five different methods. Further, the statistics for all genomes have been provided along with the links to different algorithms and related databases. It is freely available at http://tmbeta-genome.cbrc.jp/annotation/.

Algorithms↗
PubMed2005

The Candida Genome Database (CGD), a community resource for Candida albicans gene and protein information.

The Candida Genome Database (CGD) is a new database that contains genomic information about the opportunistic fungal pathogen Candida albicans. CGD is a public resource for the research community that is interested in the molecular biology of this fungus. CGD curators are in the process of combing the scientific literature to collect all C.albicans gene names and aliases; to assign gene ontology terms that describe the molecular function, biological process, and subcellular localization of each gene product; to annotate mutant phenotypes; and to summarize the function and biological context of each gene product in free-text description lines. CGD also provides community resources, including a reservation system for gene names and a colleague registry through which Candida researchers can share contact information and research interests. CGD is publicly funded (by NIH grant R01 DE15873-01 from the NIDCR) and is freely available at http://www.candidagenome.org/.

Candida albicans↗
PubMed2003

An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms.

We sought to identify all genes in the Candida albicans genome database whose deduced proteins would likely be soluble secreted proteins (the secretome). While certain C. albicans secretory proteins have been studied in detail, more data on the entire secretome is needed. One approach to rapidly predict the functions of an entire proteome is to utilize genomic database information and prediction algorithms. Thus, we used a set of prediction algorithms to computationally define a potential C. albicans secretome. We first assembled a validation set of 47 C. albicans proteins that are known to be secreted and 47 that are known not to be secreted. The presence or absence of an N-terminal signal peptide was correctly predicted by SignalP version 2.0 in 47 of 47 known secreted proteins and in 47 of 47 known non-secreted proteins. When all 6165 C. albicans ORFs from CandidaDB were analysed with SignalP, 495 ORFs were predicted to encode proteins with N-terminal signal peptides. In the set of 495 deduced proteins with N-terminal signal peptides, 350 were predicted to have no transmembrane domains (or a single transmembrane domain at the extreme N-terminus) and 300 of these were predicted not to be GPI-anchored. TargetP was used to eliminate proteins with mitochondrial targeting signals, and the final computationally-predicted C. albicans secretome was estimated to consist of up to 283 ORFs. The C. albicans secretome database is available at http://info.med.yale.edu/intmed/infdis/candida/

Algorithms↗
PubMed2006

The Candida Genome Database: facilitating research on Candida albicans molecular biology.

The Candida Genome Database (CGD; http://www.candidagenome.org) is a resource for information about the Candida albicans genomic sequence and the molecular biology of its encoded gene products. CGD collects and organizes data from the biological literature concerning C. albicans, and provides tools for viewing, searching, analysing, and downloading these data. CGD also serves as an organizing centre for the C. albicans research community, providing a gene-name registry, contact information, and research community news. This article describes the information contained in CGD and how to access it, either from the perspective of a bench scientist interested in the function of one or a few genes, or from the perspective of a biologist or bioinformatician interpreting large-scale functional genomic datasets.

Alleles↗
PubMed2005

The Mouse Genome Database (MGD): from genes to mice--a community resource for mouse biology.

The Mouse Genome Database (MGD) forms the core of the Mouse Genome Informatics (MGI) system (http://www.informatics.jax.org), a model organism database resource for the laboratory mouse. MGD provides essential integration of experimental knowledge for the mouse system with information annotated from both literature and online sources. MGD curates and presents consensus and experimental data representations of genotype (sequence) through phenotype information, including highly detailed reports about genes and gene products. Primary foci of integration are through representations of relationships among genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse and to build and implement the data and semantic standards that are essential for comparative genome analysis. Recent improvements in MGD discussed here include the enhancement of phenotype resources, the re-development of the International Mouse Strain Resource, IMSR, the update of mammalian orthology datasets and the electronic publication of classic books in mouse genetics.

Animals↗
PubMed2001

The Mouse Genome Database (MGD): integration nexus for the laboratory mouse.

The Mouse Genome Database (MGD) is the community database resource for the laboratory mouse, a key model organism for interpreting the human genome and for understanding human biology and disease (http://www.informatics.jax.org). MGD provides standard nomenclature and consensus map positions for mouse genes and genetic markers; it provides a curated set of mammalian homology records, user-defined chromosomal maps, experimental data sets and the definitive mouse 'gene to sequence' reference set for the research community. The integration and standardization of these data sets facilitates the transition between mouse DNA sequence, gene and phenotype annotations. A recent focus on allele and phenotype representations enhances the ability of MGD to organize and present data for exploring the relationship between genotype and phenotype. This link between the genome and the biology of the mouse is especially important as phenotype information grows from large mutagenesis projects and genotype information grows from large-scale sequencing projects.

Alleles↗
PubMed2003

Saccharomyces Genome Database (SGD) provides biochemical and structural information for budding yeast proteins.

The Saccharomyces Genome Database (SGD: http://genome-www.stanford.edu/Saccharomyces/) has recently developed new resources to provide more complete information about proteins from the budding yeast Saccharomyces cerevisiae. The PDB Homologs page provides structural information from the Protein Data Bank (PDB) about yeast proteins and/or their homologs. SGD has also created a resource that utilizes the eMOTIF database for motif information about a given protein. A third new resource is the Protein Information page, which contains protein physical and chemical properties, such as molecular weight and hydropathicity scores, predicted from the translated ORF sequence.

Amino Acid Motifs↗
PubMed2026

An integrated culturomic and genomic database and analysis platform for methanogenic archaea.

Methanogenic archaea research is challenged by limited strain resources, fragmented genomic data, inconsistent genome quality, substantial uncultured lineages, and difficulties in laboratory culturing, hindering advances in biogas production, climate mitigation, and microbial ecology. These archaea play crucial roles in global carbon cycling and anaerobic environments, yet scattered data and unculturable strains limit systematic studies and applications. To address this, we created MethArDB (Methanogenic Archaeal Genome Database), a specialized database for methanogenic archaea, compiling 3919 genomes, 87 host-associated plasmids, and 42 phages, with standardized quality classifications (complete, scaffold, draft), protein sequences, and metadata on geography, habitats, metabolism, and inheritable elements. Integrated MethArCT (Methanogenic Archaeal Culturomics Toolkit) employs a dual-threshold orthologous/paralogous protein analysis to evaluate metabolic pathway completeness, predicting cultivation parameters and suggesting candidate cultivation strategies, including potential medium formulations and conditions, to support strain isolation. Overall, MethArDB and MethArCT form an integrated platform combining genomics and culturomics to facilitate methanogenic archaea research. Database URL:  http://methardb.cn.

Genome, Archaeal↗
PubMed2000

An interactive web-based Pseudomonas aeruginosa genome database: discovery of new genes, pathways and structures.

Using the complete genome sequence of Pseudomonas: aeruginosa PAO1, sequenced by the Pseudomonas: Genome Project (ftp://ftp.pseudomonas. com/data/pacontigs.121599), a genome database (http://pseudomonas. bit.uq.edu.au/) has been developed containing information on more than 95% of all ORFs in Pseudomonas: aeruginosa. The database is searchable by a variety of means, including gene name, position, keyword, sequence similarity and Pfam domain. Automated and manual annotation, nucleotide and peptide sequences, Pfam and SMART domains (where available), Medline and GenBank links and a scrollable, graphical representation of the surrounding genomic landscape are available for each ORF. Using the database has revealed, among other things, that P. aeruginosa contains four chemotaxis systems, two novel general secretion pathways, at least three loci encoding F17-like thin fimbriae, six novel filamentous haemagglutinin-like genes, a number of unusual composite genetic loci related to vgr/RHS: elements in Escherichia coli, a number of fix-like genes encoding a micro-oxic respiration system, novel biosynthetic pathways and 38 genes containing domains of unknown function (DUF1/DUF2). It is anticipated that this database will be a useful bioinformatic tool for the Pseudomonas: community that will continue to evolve.

Adhesins, Bacterial↗
PubMed1999

Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure.

The Saccharomyces Genome Database (SGD) collects and organizes information about the molecular biology and genetics of the yeast Saccharomyces cerevisiae. The latest protein structure and comparison tools available at SGD are presented here. With the completion of the yeast sequence and the Caenorhabditis elegans sequence soon to follow, comparison of proteins from complete eukaryotic proteomes will be an extremely powerful way to learn more about a particular protein's structure, its function, and its relationships with other proteins. SGD can be accessed through the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/

Computational Biology↗
PubMed2002

Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO).

The Saccharomyces Genome Database (SGD) resources, ranging from genetic and physical maps to genome-wide analysis tools, reflect the scientific progress in identifying genes and their functions over the last decade. As emphasis shifts from identification of the genes to identification of the role of their gene products in the cell, SGD seeks to provide its users with annotations that will allow relationships to be made between gene products, both within Saccharomyces cerevisiae and across species. To this end, SGD is annotating genes to the Gene Ontology (GO), a structured representation of biological knowledge that can be shared across species. The GO consists of three separate ontologies describing molecular function, biological process and cellular component. The goal is to use published information to associate each characterized S.cerevisiae gene product with one or more GO terms from each of the three ontologies. To be useful, this must be done in a manner that allows accurate associations based on experimental evidence, modifications to GO when necessary, and careful documentation of the annotations through evidence codes for given citations. Reaching this goal is an ongoing process at SGD. For information on the current progress of GO annotations at SGD and other participating databases, as well as a description of each of the three ontologies, please visit the GO Consortium page at http://www.geneontology.org. SGD gene associations to GO can be found by visiting our site at http://genome-www.stanford.edu/Saccharomyces/.

Animals↗
PubMed2000

Farm animal genome databases.

The requirements for bioinformatics resources to support genome research in farm animals is reviewed. The resources developed to meet these needs are described. Resource databases and associated tools have been developed to handle experimental data. Several of these systems serve the needs of multinational collaborations. Genome databases have been established to provide contemporary summaries of the status of genome maps in a range of farm and domestic animals along with experimental details and citations. New resources and tools will be required to address the informatics needs of emerging technologies such as microarrays. However, continued investment is also required to maintain the currency and utility of the current systems, especially the genome databases.

Animals↗
PubMed2006

The Rat Genome Database, update 2007--easing the path from disease to data and back again.

The Rat Genome Database (RGD, http://rgd.mcw.edu) is one of the core resources for rat genomics and recent developments have focused on providing support for disease-based research using the rat model. Recognizing the importance of the rat as a disease model we have employed targeted curation strategies to curate genes, QTL and strain data for neurological and cardiovascular disease areas. This work has centered on rat but also includes data for mouse and human to create 'disease portals' that provide a unified view of the genes, QTL and strain models for these diseases across the three species. The disease curation efforts combined with normal curation activities have served to greatly increase the content of the database, particularly for biological information, including gene ontology, disease, pathway and phenotype ontology annotations. In addition to improving the features and database content, community outreach has been expanded to demonstrate how investigators can leverage the resources at RGD to facilitate their research and to elicit suggestions and needs for future developments. We have published a number of papers that provide additional information on the ontology annotations and the tools at RGD for data mining and analysis to better enable researchers to fully utilize the database.

Animals↗
PubMed2005

The maize genetics and genomics database. The community resource for access to diverse maize data.

The Maize Genetics and Genomics Database (MaizeGDB) serves the maize (Zea mays) research community by making a wealth of genetics and genomics data available through an intuitive Web-based interface. The goals of the MaizeGDB project are 3-fold: to provide a central repository for public maize information; to present the data through the MaizeGDB Web site in a way that recapitulates biological relationships; and to provide an array of computational tools that address biological questions in an easy-to-use manner at the site. In addition to these primary tasks, MaizeGDB team members also serve the community of maize geneticists by lending technical support for community activities, including the annual Maize Genetics Conference and various workshops, teaching researchers to use both the MaizeGDB Web site and Community Curation Tools, and engaging in collaboration with individual research groups to make their unique data types available through MaizeGDB.

Base Sequence↗
PubMed2004

Sequence-based approach to finding functional lipases from microbial genome databases.

A sequence-based approach was used to retrieve functional lipases from microbial genome databases. Many novel genes assigned as putative lipases were tested using the criteria of the typical lipase sequence rule, based on a consensus sequence of a catalytic triad (Ser, Asp, His) and oxyanion hole sequence (HG). To obtain the lipase genes satisfying the sequence rule, PCR cloning was performed, while the lipase activities were tested using a tributyrin/tricaprylin plate and p-nitrophenyl caproate. Among nine putative lipases from four strains, five functional lipolytic proteins were obtained from Archaeoglobus fulgidus, Deinococcus radiodurans, and Agrobacterium tumefaciens. All five lipases exhibited a relatively low sequence similarity (less than 26.7%) with known lipases and turned out to belong to different lipase families. Accordingly, the current results indicate that the proposed strategic approach based on the microbial genome is an efficient and rapid method for finding novel and functional lipases.

Agrobacterium tumefaciens↗
PubMed2005

wFleaBase: the Daphnia genome database.

BACKGROUND: wFleaBase is a database with the necessary infrastructure to curate, archive and share genetic, molecular and functional genomic data and protocols for an emerging model organism, the microcrustacean Daphnia. Commonly known as the water-flea, Daphnia's ecological merit is unequaled among metazoans, largely because of its sentinel role within freshwater ecosystems and over 200 years of biological investigations. By consequence, the Daphnia Genomics Consortium (DGC) has launched an interdisciplinary research program to create the resources needed to study genes that affect ecological and evolutionary success in natural environments. DISCUSSION: These tools include the genome database wFleaBase, which currently contains functions to search and extract information from expressed sequenced tags, genome survey sequences and full genome sequencing projects. This new database is built primarily from core components of the Generic Model Organism Database project, and related bioinformatics tools. SUMMARY: Over the coming year, preliminary genetic maps and the nearly complete genomic sequence of Daphnia pulex will be integrated into wFleaBase, including gene predictions and ortholog assignments based on sequence similarities with eukaryote genes of known function. wFleaBase aims to serve a large ecological and evolutionary research community. Our challenge is to rapidly expand its content and to ultimately integrate genetic and functional genomic information with population-level responses to environmental challenges. URL: http://wfleabase.org/.

Animals↗