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Found 54 result(s)
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Oral Cancer Gene Database is an initiative of the Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai. The present database, version II, consists of 374 genes. It is developed as a user friendly site that would provide the scientist, information and external links from one place. The database is accessed through a list of all genes, and Keyword Search using gene name or gene symbol, chromosomal location, CGH (in %), and molecular weight. Interaction Network shows the interaction between genes for particular biological processes and molecular functions.
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DEG hosts records of currently available essential genomic elements, such as protein-coding genes and non-coding RNAs, among bacteria, archaea and eukaryotes. Essential genes in a bacterium constitute a minimal genome, forming a set of functional modules, which play key roles in the emerging field, synthetic biology.
The Gene database provides detailed information for known and predicted genes defined by nucleotide sequence or map position. Gene supplies gene-specific connections in the nexus of map, sequence, expression, structure, function, citation, and homology data. Unique identifiers are assigned to genes with defining sequences, genes with known map positions, and genes inferred from phenotypic information. These gene identifiers are used throughout NCBI's databases and tracked through updates of annotation. Gene includes genomes represented by NCBI Reference Sequences (or RefSeqs) and is integrated for indexing and query and retrieval from NCBI's Entrez and E-Utilities systems.
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SilkDB is a database of the integrated genome resource for the silkworm, Bombyx mori. This database provides access to not only genomic data including functional annotation of genes, gene products and chromosomal mapping, but also extensive biological information such as microarray expression data, ESTs and corresponding references. SilkDB will be useful for the silkworm research community as well as comparative genomics
MalaCards is an integrated database of human maladies and their annotations, modeled on the architecture and richness of the popular GeneCards database of human genes. MalaCards mines and merges varied web data sources to generate a computerized web card for each human disease. Each MalaCard contains disease specific prioritized annotative information, as well as links between associated diseases, leveraging the GeneCards relational database, search engine, and GeneDecks set-distillation tool. As proofs of concept of the search/distill/infer pipeline we find expected elucidations, as well as potentially novel ones.
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The CyberCell database (CCDB) is a comprehensive collection of detailed enzymatic, biological, chemical, genetic, and molecular biological data about E. coli (strain K12, MG1655). It is intended to provide sufficient information and querying capacity for biologists and computer scientists to use computers or detailed mathematical models to simulate all or part of a bacterial cell at a nanoscopic (10-9 m), mesoscopic (10-8 m).The CyberCell database CCDB actually consists of 4 browsable databases: 1) the main CyberCell database (CCDB - containing gene and protein information), 2) the 3D structure database (CC3D – containing information for structural proteomics), 3) the RNA database (CCRD – containing tRNA and rRNA information), and 4) the metabolite database (CCMD – containing metabolite information). Each of these databases is accessible through hyperlinked buttons located at the top of the CCDB homepage. All CCDB sub-databases are fully web enabled, permitting a wide variety of interactive browsing, search and display operations. and microscopic (10-6 m) level.
The Allele Frequency Net Database (AFND) is a public database which contains frequency information of several immune genes such as Human Leukocyte Antigens (HLA), Killer-cell Immunoglobulin-like Receptors (KIR), Major histocompatibility complex class I chain-related (MIC) genes, and a number of cytokine gene polymorphisms. The Allele Frequency Net Database (AFND) provides a central source, freely available to all, for the storage of allele frequencies from different polymorphic areas in the Human Genome. Users can contribute the results of their work into one common database and can perform database searches on information already available. We have currently collected data in allele, haplotype and genotype format. However, the success of this website will depend on you to contribute your data.
Project Achilles is a systematic effort aimed at identifying and cataloging genetic vulnerabilities across hundreds of genomically characterized cancer cell lines. The project uses genome-wide genetic perturbation reagents (shRNAs or Cas9/sgRNAs) to silence or knock-out individual genes and identify those genes that affect cell survival. Large-scale functional screening of cancer cell lines provides a complementary approach to those studies that aim to characterize the molecular alterations (e.g. mutations, copy number alterations) of primary tumors, such as The Cancer Genome Atlas (TCGA). The overall goal of the project is to identify cancer genetic dependencies and link them to molecular characteristics in order to prioritize targets for therapeutic development and identify the patient population that might benefit from such targets. Project Achilles data is hosted on the Cancer Dependency Map Portal (DepMap) where it has been harmonized with our genomics and cellular models data. You can access the latest and all past datasets here: https://depmap.org/portal/download/all/
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A small genotype data repository containing data used in recent papers from the Estonian Biocentre. Most of the data pertains to human population genetics. PDF files of the papers are also freely available.
OrtholugeDB contains Ortholuge-based orthology predictions for completely sequenced bacterial and archaeal genomes. It is also a resource for reciprocal best BLAST-based ortholog predictions, in-paralog predictions (recently duplicated genes) and ortholog groups in Bacteria and Archaea. The Ortholuge method improves the specificity of high-throughput orthology prediction.
Phytozome is the Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute. Families of related genes representing the modern descendants of ancestral genes are constructed at key phylogenetic nodes. These families allow easy access to clade-specific orthology/paralogy relationships as well as insights into clade-specific novelties and expansions.
Online Mendelian Inheritance in Animals (OMIA) is a catalogue/compendium of inherited disorders, other (single-locus) traits, and genes in 218 animal species (other than human and mouse and rats, which have their own resources) authored by Professor Frank Nicholas of the University of Sydney, Australia, with help from many people over the years. OMIA information is stored in a database that contains textual information and references, as well as links to relevant PubMed and Gene records at the NCBI, and to OMIM and Ensembl.
OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. Its official home is omim.org.
The Plant Metabolic Network (PMN) provides a broad network of plant metabolic pathway databases that contain curated information from the literature and computational analyses about the genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism in plants. The PMN currently houses one multi-species reference database called PlantCyc and 22 species/taxon-specific databases.
InnateDB is a publicly available database of the genes, proteins, experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. The database captures an improved coverage of the innate immunity interactome by integrating known interactions and pathways from major public databases together with manually-curated data into a centralised resource. The database can be mined as a knowledgebase or used with our integrated bioinformatics and visualization tools for the systems level analysis of the innate immune response.
<<<!!!<<< This site is no longer maintained and is provided for reference only. Some functionality or links may not work. For all enquiries please contact the Ensembl Helpdesk http://www.ensembl.org/Help/Contact >>>!!!>>> PhytoPath is a new bioinformatics resource that integrates genome-scale data from important plant pathogen species with literature-curated information about the phenotypes of host infection. Using the Ensembl Genomes browser, it provides access to complete genome assembly and gene models of priority crop and model-fungal, oomycete and bacterial phytopathogens. PhytoPath also links genes to disease progression using data from the curated PHI-base resource. PhytoPath portal is a joint project bringing together Ensembl Genomes with PHI-base, a community-curated resource describing the role of genes in pathogenic infection. PhytoPath provides access to genomic and phentoypic data from fungal and oomycete plant pathogens, and has enabled a considerable increase in the coverage of phytopathogen genomes in Ensembl Fungi and Ensembl Protists. PhytoPath also provides enhanced searching of the PHI-base resource as well as the fungi and protists in Ensembl Genomes.
MetaCyc is a curated database of experimentally elucidated metabolic pathways from all domains of life. MetaCyc contains pathways involved in both primary and secondary metabolism, as well as associated metabolites, reactions, enzymes, and genes. The goal of MetaCyc is to catalog the universe of metabolism by storing a representative sample of each experimentally elucidated pathway. MetaCyc applications include: Online encyclopedia of metabolism, Prediction of metabolic pathways in sequenced genomes, Support metabolic engineering via enzyme database, Metabolite database aids. metabolomics research.
The Expression Atlas provides information on gene expression patterns under different biological conditions such as a gene knock out, a plant treated with a compound, or in a particular organism part or cell. It includes both microarray and RNA-seq data. The data is re-analysed in-house to detect interesting expression patterns under the conditions of the original experiment. There are two components to the Expression Atlas, the Baseline Atlas and the Differential Atlas. The Baseline Atlas displays information about which gene products are present (and at what abundance) in "normal" conditions (e.g. tissue, cell type). It aims to answer questions such as "which genes are specifically expressed in human kidney?". This component of the Expression Atlas consists of highly-curated and quality-checked RNA-seq experiments from ArrayExpress. It has data for many different animal and plant species. New experiments are added as they become available. The Differential Atlas allows users to identify genes that are up- or down-regulated in a wide variety of different experimental conditions such as yeast mutants, cadmium treated plants, cystic fibrosis or the effect on gene expression of mind-body practice. Both microarray and RNA-seq experiments are included in the Differential Atlas. Experiments are selected from ArrayExpress and groups of samples are manually identified for comparison e.g. those with wild type genotype compared to those with a gene knock out. Each experiment is processed through our in-house differential expression statistical analysis pipeline to identify genes with a high probability of differential expression.
The Cellular Phenotype database stores data derived from high-throughput phenotypic studies and it is being developed as part of the Systems Microscopy Network of Excellence project. The aim of the Cellular Phenotype database is to provide easy access to phenotypic data and facilitate the integration of independent phenotypic studies. Through its interface, users can search for a gene of interest, or a collection of genes, and retrieve the loss-of-function phenotypes observed, in human cells, by suppressing the expression of the selected gene(s), through RNA interference (RNAi), across independent phenotypic studies. Similarly, users can search for a phenotype of interest and retrieve the RNAi reagents that have caused such phenotype and the associated target genes. Information about specific RNAi reagents can also be obtained when searching for a reagent ID.
The mission of the GO Consortium is to develop a comprehensive, computational model of biological systems, ranging from the molecular to the organism level, across the multiplicity of species in the tree of life. The Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes. This knowledge is both human-readable and machine-readable, and is a foundation for computational analysis of large-scale molecular biology and genetics experiments in biomedical research.
<<<!!!<<< This repository is no longer available. >>>!!!>>> NetPath is currently one of the largest open-source repository of human signaling pathways that is all set to become a community standard to meet the challenges in functional genomics and systems biology. Signaling networks are the key to deciphering many of the complex networks that govern the machinery inside the cell. Several signaling molecules play an important role in disease processes that are a direct result of their altered functioning and are now recognized as potential therapeutic targets. Understanding how to restore the proper functioning of these pathways that have become deregulated in disease, is needed for accelerating biomedical research. This resource is aimed at demystifying the biological pathways and highlights the key relationships and connections between them. Apart from this, pathways provide a way of reducing the dimensionality of high throughput data, by grouping thousands of genes, proteins and metabolites at functional level into just several hundreds of pathways for an experiment. Identifying the active pathways that differ between two conditions can have more explanatory power than just a simple list of differentially expressed genes and proteins.
The HomoloGene database provides a system for the automated detection of homologs among annotated genes of genomes across multiple species. These homologs are fully documented and organized by homology group. HomoloGene processing uses proteins from input organisms to compare and sequence homologs, mapping back to corresponding DNA sequences.