Tools   Select Another Resource Report Type

http://baygenomics.ucsf.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The BayGenomics gene-trap resource provides researchers with access to thousands of mouse embryonic stem (ES) cell lines harboring characterized insertional mutations in both known and novel genes. The major goal of BayGenomics is to identify genes relevant to cardiovascular and pulmonary disease.

Proper citation: BayGenomics (RRID:SCR_008168) Copy   

•   Source: SciCrunch Registry

http://www.uni-wh.de/pcogr

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 20,2019.The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data-base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies. The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. Here is a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.

Proper citation: Phylogenetic Clusters of Orthologous Groups Ranking (RRID:SCR_008223) Copy   

•   Source: SciCrunch Registry

http://vox.pharmacology.ucla.edu/home.html

Two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 cubic mm gene expression patterns in the brain obtained through voxelation. Voxelation employs high-throughput analysis of spatially registered voxels (cubes) to produce multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems.

Proper citation: Voxelation Map of Gene Expression in a Coronal Section of the Mouse Brain (RRID:SCR_008065) Copy   

•   Source: SciCrunch Registry

http://bioinfo-out.curie.fr/ittaca/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 6/12/25. ITTACA is a database created for Integrated Tumor Transcriptome Array and Clinical data Analysis. ITTACA centralizes public datasets containing both gene expression and clinical data and currently focuses on the types of cancer that are of particular interest to the Institut Curie: breast carcinoma, bladder carcinoma, and uveal melanoma. ITTACA is developed by the Institut Curie Bioinformatics group and the Molecular Oncology group of UMR144 CNRS/Institut Curie. A web interface allows users to carry out different class comparison analyses, including comparison of expression distribution profiles, tests for differential expression, patient survival analyses, and users can define their own patient groups according to clinical data or gene expression levels. The different functionalities implemented in ITTACA are: - To test if one or more gene, of your choice, is differentially expressed between two groups of samples exhibiting distinct phenotypes (Student and Wilcoxon tests). - The detection of genes differentially expressed (Significance Analysis of Microarrays) between two groups of samples. - The creation of histograms which represent the expression level according to a clinical parameter for each sample. - The computation of Kaplan Meier survival curves for each group. ITTACA has been developed to be a useful tool for comparing personal results to the existing results in the field of transcriptome studies with microarrays.

Proper citation: Integrated Tumor Transcriptome Array and Clinical data Analysis (RRID:SCR_008182) Copy   

•   Source: SciCrunch Registry

http://www.peroxisomedb.org/

The aim of the PEROXISOME database (PeroxisomeDB) is to gather, organize and integrate curated information on peroxisomal genes, their encoded proteins, their molecular function and metabolic pathway they belong to, and their related disorders. PeroxisomeDB contains the complete peroxisomal proteome of Homo sapiens (encoded by 85 genes) and Saccharomyces cerevisiae (encoded by 61 genes). Now, we have included 34 new organism genomes with the acquisition of 2426 new peroxisomal homolog proteins. PeroxisomeDB 2.0 integrates the peroxisomal metabolome of whole microbody family by the new incorporation of the glycosome proteomes of trypanosomatids and the glyoxysome proteome of Arabidopsis thaliana. The site also provides a Peroxisome Metabolome of peroxisomal genes and proteins, their molecular interactions and metabolic pathways, tools for comparative genomics, predictive tools. Sponsors: Preoxisome Database is funded by Institut de Gntique et deBiologie Molculaire et Cellulaire.

Proper citation: Peroxisome Database (RRID:SCR_008352) Copy   

•   Source: SciCrunch Registry

http://cssb.biology.gatech.edu/skolnick/files/gpcr/gpcr.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 19,2019.Database of tertiary structural modeling results of threading assembly refinement (TASSER) method for all 907 G protein-coupled receptors (GPCRs) in human genome. All sequences were collected from GPCR database http://www.gpcr.org/7tm/ and http://www.expasy.org/cgi-bin/lists?7tmrlist.txt. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. Sponsors: GPCR is funded by the University at Buffalo, Buffalo, New York.

Proper citation: Structure modeling of 907 G protein coupled receptors in the human genome (RRID:SCR_008351) Copy   

•   Source: SciCrunch Registry

http://genome.jgi.doe.gov/programs/metagenomes/index.jsf

Portal providing access to metagenomics projects, data and tools supported by the DOE Joint Genome Institute (JGI). A primary motivation for metagenomics is that most microbes found in nature exist in complex, interdependent communities and cannot readily be grown in isolation in the laboratory. One can, however, isolate DNA or RNA from the community as a whole, and studies of such communities have revealed a diversity of microbes far beyond those found in culture collections. It is suspected that these uncultivated organisms must harbor considerable as-yet undiscovered genomic, functional, and metabolic features and capabilities. Thus to fully explore microbial genomics, it is imperative that we access the genomes of these elusive players.

Proper citation: Metagenomics Program at JGI (RRID:SCR_008804) Copy   

•   Source: SciCrunch Registry

https://bbgre.brc.iop.kcl.ac.uk

A database and associated tools for investigating the genetic basis of neurodisability. It combines phenotype information from patients with neurodevelopmental and behavioral problems with clinical genetic data, and displays this information on the human genome map. Basic access to genetic information (deletions, duplications) relating to participants with neurodevelopmental disorders is provided without an account; access to the full dataset requires an account. The genetic information that is available to view comprises potentially pathogenic copy number variation across the genome, detected by array comparative genome hybridization (aCGH) using a customized 44K oligonucleotide array.

Proper citation: Brain and Body Genetic Resource Exchange (RRID:SCR_008959) Copy   

•   Source: SciCrunch Registry

http://hipathdb.kobic.re.kr/

hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. (2010.03.09) hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways.

Proper citation: hiPathDB - human integrated Pathway DB with facile visualization (RRID:SCR_008900) Copy   

•   Source: SciCrunch Registry

http://www.medinfopoli.polimi.it/GFINDer/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 16, 2019. Multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyze the obtained classifications, aiding in better interpreting microarray experiment results.

Proper citation: GFINDer: Genome Function INtegrated Discoverer (RRID:SCR_008868) Copy   

•   Source: SciCrunch Registry

http://bioinfo.au.tsinghua.edu.cn/atie/

A database of publicly available genes, alternatively translational isoforms and their detailed annotation. Alternative translational initiation is one of mechanisms to increase the complexity level of an organism by alternative gene expression pathways. The use of alternative translation initiation codons in a singe mRNA contributes to the generation of protein diversity. The genes produce two or more versions of the encoded proteins, and the shorter version, initiated from a downstream in-frame start codon, lacks the N-terminal amino acids fragment of the full-length isoform version. Since the first discovery of alternative translation initiation, a small, yet growing, number of mRNAs initiating translation from alternative start codons have been reported. Various studies began to emerge focusing on this new field in gene expression and revealed the biological significance of the use of alternative initiation. In response to the need for systematic studies on genes involving alternative translational initiation, Alternative Translational Initiation Database(ATID) is established to provide data of publicly available genes, alternatively translational isoforms and their detailed annotation.

Proper citation: ATID: Alternative Translational Initiation Database (RRID:SCR_009432) Copy   

•   Source: SciCrunch Registry

http://genomics.senescence.info/genes/

Collection of annotated and manually curated data of genes related to aging divided into genes related to longevity and/or aging in model organisms (yeast, worms, flies, mice, etc.) and aging related human genes.

Proper citation: GenAge (RRID:SCR_010223) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000674.v1.p1

Human genetics data from an immense (78,000) and ethnically diverse population available for secondary analysis to qualified researchers through the database of Genotypes and Phenotypes (dbGaP). It offers the opportunity to identify potential genetic risks and influences on a broad range of health conditions, particularly those related to aging. The GERA cohort is part of the Research Program on Genes, Environment, and Health (RPGEH), which includes more than 430,000 adult members of the Kaiser Permanente Northern California system. Data from this larger cohort include electronic medical records, behavioral and demographic information from surveys, and saliva samples from 200,000 participants obtained with informed consent for genomic and other analyses. The RPGEH database was made possible largely through early support from the Robert Wood Johnson Foundation to accelerate such health research. The genetic information in the GERA cohort translates into more than 55 billion bits of genetic data. Using newly developed techniques, the researchers conducted genome-wide scans to rapidly identify single nucleotide polymorphisms (SNPs) in the genomes of the people in the GERA cohort. These data will form the basis of genome-wide association studies (GWAS) that can look at hundreds of thousands to millions of SNPs at the same time. The RPGEH then combined the genetic data with information derived from Kaiser Permanente''s comprehensive longitudinal electronic medical records, as well as extensive survey data on participants'' health habits and backgrounds, providing researchers with an unparalleled research resource. As information is added to the Kaiser-UCSF database, the dbGaP database will also be updated.

Proper citation: Resource for Genetic Epidemiology Research on Adult Health and Aging (RRID:SCR_010472) Copy   

•   Source: SciCrunch Registry

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-S1-S21

A microarray platform that uses the Illumina and Affymetrix GeneChip microarray technology for genome and transcriptome analyses and a web-based database that consists exclusively of high quality Affymetrix data from immunological experiments hosted by a public, non-profit consortium of three scientific public institutions aimed to develop, integrate and disseminate Functional Genomics.

Proper citation: Genopolis (RRID:SCR_010975) Copy   

•   Source: SciCrunch Registry

http://www.genomicus.biologie.ens.fr/genomicus-72.01/cgi-bin/search.pl

A genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Proper citation: Genomicus (RRID:SCR_011791) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/HMMgene/

Data analysis service for prediction of vertebrate and C. elegans genes.

Proper citation: HMMgene (RRID:SCR_011933) Copy   

•   Source: SciCrunch Registry

http://cgap.nci.nih.gov/Chromosomes/Mitelman

The web site includes genomic data for humans and mice, including transcript sequence, gene expression patterns, single-nucleotide polymorphisms, clone resources, and cytogenetic information. Descriptions of the methods and reagents used in deriving the CGAP datasets are also provided. An extensive suite of informatics tools facilitates queries and analysis of the CGAP data by the community. One of the newest features of the CGAP web site is an electronic version of the Mitelman Database of Chromosome Aberrations in Cancer. The data in the Mitelman Database is manually culled from the literature and subsequently organized into three distinct sub-databases, as follows: -The sub-database of cases contains the data that relates chromosomal aberrations to specific tumor characteristics in individual patient cases. It can be searched using either the Cases Quick Searcher or the Cases Full Searcher. -The sub-database of molecular biology and clinical associations contains no data from individual patient cases. Instead, the data is pulled from studies with distinct information about: -Molecular biology associations that relate chromosomal aberrations and tumor histologies to genomic sequence data, typically genes rearranged as a consequence of structural chromosome changes. -Clinical associations that relate chromosomal aberrations and/or gene rearrangements and tumor histologies to clinical variables, such as prognosis, tumor grade, and patient characteristics. It can be searched using the Molecular Biology and Clinical (MBC) Associations Searcher -The reference sub-database contains all the references culled from the literature i.e., the sum of the references from the cases and the molecular biology and clinical associations. It can be searched using the Reference Searcher. CGAP has developed six web search tools to help you analyze the information within the Mitelman Database: -The Cases Quick Searcher allows you to query the individual patient cases using the four major fields: aberration, breakpoint, morphology, and topography. -The Cases Full Searcher permits a more detailed search of the same individual patient cases as above, by including more cytogenetic field choices and adding search fields for patient characteristics and references. -The Molecular Biology Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to gene rearrangements as a consequence of cytogenetic aberrations. -The Clinical Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to clinical associations of cytogenetic aberrations and/or gene rearrangements. -The Recurrent Chromosome Aberrations Searcher provides a way to search for structural and numerical abnormalities that are recurrent, i.e., present in two or more cases with the same morphology and topography. -The Reference Searcher queries only the references themselves, i.e., the references from the individual cases and the molecular biology and clinical associations. Sponsors: This database is sponsored by the University of Lund, Sweden and have support from the Swedish Cancer Society and the Swedish Children''s Cancer Foundation

Proper citation: Mitelman Database of Chromosome Aberrations in Cancer (RRID:SCR_012877) Copy   

•   Source: SciCrunch Registry

http://biome.ewha.ac.kr:8080/GSEAWebApp/

An integrative platform for diverse types of gene set analysis with annotation network navigation. It includes tools for statistical analysis, visualization of annotation relationships, retrieval of genes from annotation database, and set operation for gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, they have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations.

Proper citation: GARNET (RRID:SCR_012033) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

•   Source: SciCrunch Registry

http://www.genepaint.org/MapP56_01.htm

Abbreviated reference atlas for the P56 mouse. All sections were nissl stained and digitized. To assist in the initial identification of sites of gene expression sites, maps of brains are available for E15.5, P7 and the adult. These maps depict the boundaries of major brain regions (cortex, thalamus, striatum, globus pallidus, ventral striatum, septum, basal forebrain, hippocampus, midbrain, pons, medulla, cerebellum) and also show the more prominent nerve tracts. Maps are most efficiently used by placing the window depicting the map of interest next to the gene expression image. Browsing is permitted between planes of sectioning thus allowing the most appropriate plane to be selected. Abbreviations are found in a list accessed by clicking the see abbreviation link at the bottom of each map. Alternatively, passing the cursor directly across the abbreviation on the map will result in the appearance of the appropriate term in the rider on top of the map panel. The annotation of anatomical details such as brain nuclei is currently beyond the scope of the GenePaint database. Hence, such information on the anatomy of the brain and embryo should be obtained from published atlases of mouse anatomy (Kaufman, 1995; Paxinos and Franklin, 2001; Jacobowitz and Abbott, 1997; Schambra et al., 1992; Valverde1998).

Proper citation: GenePaint P56 Mouse Atlas (RRID:SCR_002788) Copy   

•   Source: SciCrunch Registry