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http://mga.bionet.nsc.ru/soft/maia-1.0/

Software package of programs for complex segregation analysis in animal pedigrees.

Proper citation: MAIA (RRID:SCR_007153) Copy   

•   Source: SciCrunch Registry

http://www.biomanta.org/

This project encompasses development of novel biological network analysis methods and infrastructure for querying biological data in a semantically-enabled format, and aims to create a semantic interactome model. Research within the BioMANTA project will focus on computational modelling and analysis, primarily using Semantic Web technologies and Machine Learning methods, of large-scale protein-protein interaction and compound activity networks across a wide variety of species. A range of information such as kinetic activity, tissue expression, and subcellular localization and disease state attributes will be included in the resulting data model. Protein interactions are a fundamental component of biological processes. Many proteins are functional only in multimeric complexes, or require interaction partners to achieve their correct localisation or function. For this reason, the study of protein-protein interaction (PPI) networks has become an area of growing interest in computational biology. Through the use of Semantic Web technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL), interaction data is modelled to create a knowledge representation in which meaning is vested in the ontology rather than instances of data. Stochastic and computational intelligence methods are applied to this data to infer high coverage networks. Semantic inferencing is used to infer previously unknown and meaningful pathways. Major project components: - The BioMANTA Ontology:- An OWL DL ontology incorporating the PSI-MI Ontology, the NCBI Taxonomy, and elements of BioPax ontology and Gene Ontology (describing subcellular localisation). This allows us to re-use existing ontologies, thereby reducing overheads associated with knowledge acquisition in the ontology development process. We are able to integrate existing public data that contain annotation in these formats. - Data conversion & semantic protein integration:- A set of software components that convert protein-protein databases (DIP, MPact, IntAct, etc.) from PSI-MI XML to RDF compliant with the BioMANTA ontology. These software allow us to make these protein-protein interaction datasets (and more generally, any PSI-MI XML data) semantically available for querying and inference within BioMANTA. - A RDF triple store based on RDF Molecules and the MapReduce architecture:- A proof-of-concept RDF triple store using RDF molecules and Hadoop scale-out architectures. Regular RDF graphs are deconstructed into RDF molecules, which are distributed over distributed compute nodes in the MapReduce architecture, and are subsequently combined to form equivalent RDF graphs. Such an approach makes the distributed SPARQL querying and reasoning on RDF triple stores possible. - A quantitative framework to integrate networks extracted from independent data sources (gene expression, subcellular localization, and ortholog mapping):- The model is multi-layer, with a first layer based on Decision Trees where each Decision tree is built on each dataset independently. The tree nodes are cut using Shannon''s entropy (mutual information); the decision of these independent trees is integrated using logistic regression, and the parameters are optimised using maximum likelihood. Sponsors: This resource is supported by the Pfizer Global Research and Development, the Institute for Molecular Bioscience (IMB), and the University of Queensland, Australia.

Proper citation: BioMANTA (RRID:SCR_007177) Copy   

•   Source: SciCrunch Registry

http://humancyc.org/

The HumanCyc database describes human metabolic pathways and the human genome. By presenting metabolic pathways as an organizing framework for the human genome, HumanCyc provides the user with an extended dimension for functional analysis of Homo sapiens at the genomic level. A computational pathway analysis of the human genome assigned human enzymes to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary step toward quantitative modeling of metabolism. HumanCyc contains the complete genome sequence of Homo sapiens, as presented in Build 31. Data on the human genome from Ensembl, LocusLink and GenBank were carefully merged to create a minimally redundant human gene set to serve as an input to SRI''s PathoLogic software, which generated the database and predicted Homo sapiens metabolic pathways from functional information contained in the genome''s annotation. SRI did not re-annotate the genome, but worked with the gene function assignments in Ensembl, LocusLink, and GenBank. The resulting pathway/genome database (PGDB) includes information on 28,783 genes, their products and the metabolic reactions and pathways they catalyze. Also included are many links to other databases and publications. The Pathway Tools software/database bundle includes HumanCyc and the Pathway Tools software suite and is available under license. This form of HumanCyc is faster and more powerful than the Web version.

Proper citation: HumanCyc: Encyclopedia of Homo sapiens Genes and Metabolism (RRID:SCR_007050) Copy   

•   Source: SciCrunch Registry

http://sites.huji.ac.il/malaria/

Data set of metabolic pathways for the malaria parasite based on the present knowledge of parasite biochemistry and on pathways known to occur in other unicellular eukaryotes. This site extracted the pertinent information from the universal sites and presented them in an educative and informative format. The site also includes, cell-cell interactions (cytoadherence and rosetting), invasion of the erythrocyte by the parasite and transport functions. It also contains an artistic impression of the ultrastructural morphology of the interaerythrocytic cycle stages and some details about the morphology of mitochondria and the apicoplast. Most pathways are relevant to the erythrocytic phase of the parasite cycle. All maps were checked for the presence of enzyme-coding genes as they are officially annotated in the Plasmodium genome (http://plasmodb.org/). The site is constructed in a hierarchical pattern that permits logical deepening: * Grouped pathways of major chemical components or biological process ** Specific pathways or specific process *** Chemical structures of substrates and products or process **** Names of enzymes and their genes or components of process Each map is linked to other maps thus enabling to verify the origin of a substrate or the fate of a product. Clicking on the EC number that appears next to each enzyme, connects the site to BRENDA, SWISSPROT ExPASy ENZYME, PlasmoDB and to IUBMB reaction scheme. Clicking of the name of a metabolite, connects the site to KEGG thus providing its chemical structure and formula. Next to each enzyme there is a pie that depicts the stage-dependent transcription of the enzyme''s coding gene. The pie is constructed as a clock of the 48 hours of the parasite cycle, where red signifies over-transcription and green, under-transcription. Clicking on the pie links to the DeRisi/UCSF transcriptome database.

Proper citation: Malaria Parasite Metabolic Pathways (RRID:SCR_007072) Copy   

•   Source: SciCrunch Registry

http://www.vbase2.org/

Integrative database of germ-line V genes from the immunoglobulin loci of human and mouse. It presents V gene sequences extracted from the EMBL nucleotide sequence database and Ensembl together with links to the respective source sequences. Based on the properties of the source sequences, V genes are classified into 3 different classes: * Class 1: genomic and rearranged evidence * Class 2: genomic evidence only * Class 3: rearranged evidence only This allows careful sequence quality validation by the user. References to other immunological databases ( KABAT, IMGT/LIGM and VBASE ) are given to provide all public annotation data for each V gene. The VBASE2 database can be accessed either by the Direct Query interface or by the DNAPLOT Query interface. The Sequences given by the user are aligned with DNAPLOT against the VBASE2 database. Direct Query allows to enter sequence IDs and names (Field 1), choose species, locus, V gene family and class (Field 2) or search for 100% sequences (Field 3). At the DNAPLOT Query, the sequences given by the user are aligned with DNAPLOT against the VBASE2 database. The DNAPLOT program offers V gene nucleotide sequence alignment referring to the IMGT V gene unique numbering. The Quick Search can be used either for Direct Query to search for sequence IDs and V gene names or for DNAPLOT Query for up to 5 sequences. The new Fab Analysis allows you to align Fab, scFab, scAb or scFv sequences with DNAPLOT against the VBASE2 database, where both heavy and light chain are analyzed.

Proper citation: VBASE2 (RRID:SCR_007082) Copy   

•   Source: SciCrunch Registry

http://www.komp.org/

Repository of mouse vectors, ES cells, mice, embryos, and sperm generated by NIH KOMP Mutagenesis Project. In addition, KOMP Repository offers services in support of KOMP products, including ES cell microinjection, vector cloning, post-insertional modification of cloned ES cells, cryopreservation, assisted reproduction techniques (IVF, ICSI) and mouse breeding, pathology services, phenotyping services, etc. KOMP Repository is final component of more than $50 million trans-NIH initiative to increase availability of genetically altered mice and related materials. The University of California, Davis (UC Davis) and Children''s Hospital Oakland Research Institute (CHORI) in Oakland, Calif., are collaborating to preserve, protect, and make available about 8,500 types of knockout mice and related products available to research community. Products are generated by two KOMP mutagenesis teams (CSD consortium and Regeneron Inc). All KOMP products generated by CSD consortium and Regeneron are available through KOMP Repository. Notice as of December 19, 2019: Materials from KOMP Repository have been deposited into MMRRC, including all mouse models and mouse embryonic stem cell lines. Eventually www.komp.org will be sunsetting, and IMSR will remove KOMP Repository listings, since they were double listed in MMRRC. MMRRC will contain the most accurate and up to date resource models.

Proper citation: Knockout Mouse Project Repository (RRID:SCR_007318) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/node/305

The Connectivity Map aims to generate a detailed map that links gene patterns associated with disease to corresponding patterns produced by drug candidates and a variety of genetic manipulations. The Connectivity Map is the most comprehensive effort yet for using genomics in a drug-discovery framework. It allows researchers to screen compounds against genome-wide disease signatures, rather than a pre-selected set of target genes. Drugs are paired with diseases using sophisticated pattern-matching methods with a high level of resolution and specificity. To build a Connectivity Map, the Broad Institute brings together molecular biologists, genomics specialists, computational scientists, pharmacologists, chemists and chemical biologists, as well as expertise from across the breadth and depth of medicine.Connectivity map is a large public database of signatures of drugs and genes, and pattern-matching tools to detect similarities among these signatures.The parent site for the Broad Institute at MIT has a software library of software applications developed for use in genetic analysis.

Proper citation: National Institute of Mental Health (NIMH) Human Genetics Initiative (RRID:SCR_007436) Copy   

•   Source: SciCrunch Registry

http://www.geisha.arizona.edu/geisha/

Online repository for chicken in situ hybridization information. This site presents whole mount in situ hybridization images and corresponding probe and genomic information for genes expressed in chicken embryos in Hamburger Hamilton stages 1-25 (0.5-5 days). The GEISHA project began in 1998 to investigate using high throughput whole mount in situ hybridization to identify novel, differentially expressed genes in chicken embryos. An initial expression screen of approximately 900 genes demonstrated feasibility of the approach, and also highlighted the need for a centralized repository of in situ hybridization expression data. Objectives: The goals of the GEISHA project are to obtain whole mount in situ hybridization expression information for all differentially expressed genes in the chicken embryo between HH stages 1-25, to integrate expression data with the chicken genome browsers, and to offer this information through a user-friendly graphical user interface. In situ hybridization images are obtained from three sources: 1. In house high throughput in situ hybridization screening: cDNAs obtained from several embryonic cDNA libraries or from EST repositories are screened for expression using high throughput in situ hybridization approaches. 2. Literature curation: Agreements with journals permit posting of published in situ hybridization images and related information on the GEISHA site. 3. Unpublished in situ hybridization information from other laboratories: laboratories generally publish only a small fraction of their in situ hybridization data. High quality images for which probe identity can be verified are welcome additions to GEISHA.

Proper citation: GEISHA - Gallus Expression in Situ Hybridization Analysis: A Chicken Embryo Gene Expression Database (RRID:SCR_007440) Copy   

•   Source: SciCrunch Registry

http://senselab.med.yale.edu/ordb/

Database of vertebrate olfactory receptors genes and proteins. It supports sequencing and analysis of these receptors by providing a comprehensive archive with search tools for this expanding family. The database also incorporates a broad range of chemosensory genes and proteins, including the taste papilla receptors (TPRs), vomeronasal organ receptors (VNRs), insect olfaction receptors (IORs), Caenorhabditis elegans chemosensory receptors (CeCRs), and fungal pheromone receptors (FPRs). ORDB currently houses chemosensory receptors for more than 50 organisms. ORDB contains public and private sections which provide tools for investigators to analyze the functions of these very large gene families of G protein-coupled receptors. It also provides links to a local cluster of databases of related information in SenseLab, and to other relevant databases worldwide. The database aims to house all of the known olfactory receptor and chemoreceptor sequences in both nucleotide and amino acid form and serves four main purposes: * It is a repository of olfactory receptor sequences. * It provides tools for sequence analysis. * It supports similarity searches (screens) which reduces duplicate work. * It provides links to other types of receptor information, e.g. 3D models. The database is accessible to two classes of users: * General public www users have full access to all the public sequences, models and resources in the database. * Source laboratories are the laboratories that clone olfactory receptors and submit sequences in the private or public database. They can search any sequence they deposited to the database against any private or public sequence in the database. This user level is suited for laboratories that are actively cloning olfactory receptors.

Proper citation: Olfactory Receptor DataBase (RRID:SCR_007830) Copy   

•   Source: SciCrunch Registry

http://genomics.senescence.info/

Collection of databases and tools designed to help researchers study the genetics of human ageing using modern approaches such as functional genomics, network analyses, systems biology and evolutionary analyses. A major resource in HAGR is GenAge, which includes a curated database of genes related to human aging and a database of ageing- and longevity-associated genes in model organisms. Another major database in HAGR is AnAge. Featuring over 4,000 species, AnAge provides a compilation of data on aging, longevity, and life history that is ideal for the comparative biology of aging. GenDR is a database of genes associated with dietary restriction based on genetic manipulation experiments and gene expression profiling. Other projects include evolutionary studies, genome sequencing, cancer genomics, and gene expression analyses. The latter allowed them to identify a set of genes commonly altered during mammalian aging which represents a conserved molecular signature of aging. Software, namely in the form of scripts for Perl and SPSS, is made available for users to perform a variety of bioinformatic analyses potentially relevant for studying aging. The Perl toolkit, entitled the Ageing Research Computational Tools (ARCT), provides modules for parsing files, data-mining, searching and downloading data from the Internet, etc. Also available is an SPSS script that can be used to determine the demographic rate of aging for a given population. An extensive list of links regarding computational biology, genomics, gerontology, and comparative biology is also available.

Proper citation: Human Ageing Genomic Resources (RRID:SCR_007700) Copy   

•   Source: SciCrunch Registry

http://flybrain.neurobio.arizona.edu/

An interactive database of the Drosophila melanogaster nervous system. It is used by the drosophila neuroscience community and by other researchers studying arthropod brain structure. Flybrain contains neuroanatomical peer reviewed descriptions of the central and peripheral nervous system of Drosophila melanogaster. It also contains an introductory hypertext tour guide to the basic structure of the nervous system, as well as more specific information concerning different anatomical structures, developmental stages, and visualization techniques for the Drosophila nervous system. Additionally, The site contains schematic representations, a 3D project, immunocytology stains, a library of golgi impregnations, and enhancer-trap images.

Proper citation: MIRROR: FlyBrain, An Online Atlas and Database of the Drosophila Nervous System (RRID:SCR_007661) Copy   

•   Source: SciCrunch Registry

http://www.dnaform.jp/products/cage_e.html

Expression profiling and promoter identification software tool for transcriptional network analysis and transcriptome characterization. DeepCAGE, the combination of next-generation sequencing with next generation expression profiling provides unsurpassed solutions for expression profiling and genome annotation. CAGE will be the experimental approach at need to link gene expression and control regions in the genome. With the availability of next-generation sequencing methods, DNAFORM now offers DeepCAGE services. DeepCAGE libraries are prepared for direct analysis by an Illumina/Solexa Sequencer. One sequencing run using one channel on an Illumina/Solexa Sequencer can yield in over 4,000,000 reads per sample. CAGE is based on our full-length cDNA library technology, where an adaptor is ligated to the 5''''-end of full-length cDNAs, which introduces a recognition site for a Class IIs restriction endonuclease adjacent to the 5''''-end of the cDNA. The Class IIs restriction endonuclease, here MmeI, allows for the cloning of short tags as derived from the 5''''-end of transcripts into concatemers for high-throughput sequencing. CAGE tags are further characterized by mapping to genomic sequences, which enables the identification of transcriptional start sites. As such CAGE can contribute to projects in Gene Discovery, Gene Expression, and Promoter Identification. After the genome sequencing projects have provided us with the genetic blueprints for many organisms, new questions have to be answered on how to correlate the observed genotypes with related phenotypes, and how to understand the regulation of genetic information in time and space. The dynamics of living systems and the functional behavior of cells in multicellular organisms has thus become the subject of the emerging field of system biology. Integration of experimental approaches and computer aided theories on a system level will be the fundamental principle to drive systems biology in order to understand the principles behind complex regulatory networks, which will be an ambitious goal requiring new approaches in life sciences. For ordering and additional information, please contact us under contact_at_dnaform.jp

Proper citation: CAGE (RRID:SCR_007574) Copy   

•   Source: SciCrunch Registry

http://claire.bardel.free.fr/software.html

Software package to perform phylogeny based association and localization analysis.Used for association detection and localization of susceptibility sites using haplotype phylogenetic trees. Performs these two phylogeny-based analysis: tests association between candidate gene and disease; pinpoints markers (SNPs) that are putative disease susceptibility loci.

Proper citation: ALTree (RRID:SCR_007562) Copy   

•   Source: SciCrunch Registry

http://organelledb.lsi.umich.edu/

Database of organelle proteins, and subcellular structures / complexes from compiled protein localization data from organisms spanning the eukaryotic kingdom. All data may be downloaded as a tab-delimited text file and new localization data (and localization images, etc) for any organism relevant to the data sets currently contained in Organelle DB is welcomed. The data sets in Organelle DB encompass 138 organisms with emphasis on the major model systems: S. cerevisiae, A. thaliana, D. melanogaster, C. elegans, M. musculus, and human proteins as well. In particular, Organelle DB is a central repository of yeast protein localization data, incorporating results from both previous and current (ongoing) large-scale studies of protein localization in Saccharomyces cerevisiae. In addition, we have manually curated several recent subcellular proteomic studies for incorporation in Organelle DB. In total, Organelle DB is a singular resource consolidating our knowledge of the protein composition of eukaryotic organelles and subcellular structures. When available, we have included terms from the Gene Ontologies: the cellular component, molecular function, and biological process fields are discussed more fully in GO. Additionally, when available, we have included fluorescent micrographs (principally of yeast cells) visualizing the described protein localization. Organelle View is a visualization tool for yeast protein localization. It is a visually engaging way for high school and undergraduate students to learn about genetics or for visually-inclined researchers to explore Organelle DB. By revealing the data through a colorful, dimensional model, we believe that different kinds of information will come to light.

Proper citation: Organelle DB (RRID:SCR_007837) Copy   

•   Source: SciCrunch Registry

http://compgen.bio.unipd.it/bioinfo/

Welcome to the computational genomics laboratory''s home page. Main research interests: computational biology, bioinformatics and systems biology applied to the study of the mechanisms underlying gene expression regulation. They also develop bioinformatic software aiming at helping the research community solve some problems.

Proper citation: Computational Genomics Laboratory (RRID:SCR_007901) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/gxa/

Gene Expression Atlas is a semantically enriched database of meta-analysis based summary statistics over a curated subset of ArrayExpress Archive, servicing queries for condition-specific gene expression patterns as well as broader exploratory searches for biologically interesting genes/samples. The EBI Gene Expression Atlas Blog discusses ideas, features and problems of creating a large scale meta-analytical atlas of gene expression from publicly available microarray data. Atlas REST API provides all the results available in the main web application in a pragmatic, easy to use form - simple HTTP GET queries as input and either JSON or XML formats as output. Gene Expression Atlas goals: 1. Provision of a statistically robust framework for integration of gene expression experiment results across different platforms at a meta-analytical level 2. A simple interface for identifying strong differential expression candidate genes in conditions of interest 3. Integration of ontologies for high quality annotation of gene and sample attributes 4. Construction of new gene expression summarized views, with a view to analysis of putative signaling pathway targets, discovery of correlated gene expression patterns and the identification of condition/tissue-specific patterns of gene expression.

Proper citation: Gene Expression Atlas (RRID:SCR_007989) Copy   

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http://mamep.molgen.mpg.de/

Database of gene expression in whole-mount mouse embryos derived from in situ hybridization on mid-gestation mouse embryos. A genome wide screening for genes showing a tissue restricted expression pattern in mid-gestation embryos is performed to identify genes that are likely to play an important role in the regulatory networks controlling pattern formation and organogenesis. The screening provides the basis for imaging the molecular anatomy of the mouse embryo, and for creating a gene resource for a directed functional analysis of developmental processes. The experimental protocol is available. Pattern genes in MAMEP: 1912 Images in MAMEP: 23994

Proper citation: MAMEP - Molecular Anatomy of the Mouse Embryo Project (RRID:SCR_007768) Copy   

•   Source: SciCrunch Registry

http://www.bio.dist.unige.it/

The Bio-Lab is a Bioengineering and Bioimages laboratory and it''s part of DIST (Department of Computer Science, Control Systems and Telecommunications- University of Genoa). The main research activities of the Bioengineering Laboratory are related with the acquisition, processing and rendering of bioimages with the aim of exploring the links between structure and functionality of biological systems (images of cells in optical confocal microscopy) and for diagnostic and therapeutical processes (images of the human body in Magnetic Resonance, Computerized X-ray Tomography, ultrasounds and other modalities). The main research activities of the laboratory are related to: - Bioimages: medical/biological image analysis and visualization methods are investigated, with a deep attention to the applications development for the driven therapy. - Bioinformatics: in this field, advanced technologies and knowledge related to production and treatment of gene expression microarray distributed data have been implemented. - Tissue Engineering: this research activity is addressed to the development and testing of biomaterials, coupled with adult stem cells for the in vivo bone formation. - Neuroinformatics: the interaction between results of functional neuroimaging and brain computational models has been studied. Among the equipment of the Bioengineering Laboratory there are of particular interest: 2D-3D cell culture bioreactor systems and a virtual reality platform for the interaction person-bioimages, medical data and 3D models. The Bio-Lab also manages a GRID node belonging to the international GILDA network, which is built by 6 server, for a total amount of 12 CPU, 7 GB RAM and 500 GB Disk Space. Additionally, the confocal laser is a very essential tool in this lab; it is a scanning optical microscope with a hardware/software framework virtual reality-like for the interaction person-bioimages, with stereovision, and equipment for head tracking and pointing. The Bio-Lab collaborates with several research institutes and centers, such as the National Institute of Nuclear Physics, the National Research Council, and the Advanced Biotechnology Center (visit Research area or Project area in the web site). Promising collaborations are also on going with different technological companies in the national scenario. The laboratory has also been involved in several international and national projects funded by the European Commission and MUR. Specifically, it is involved in several projects funded by the European Commission projects related to the area of Health Care Telematics, with special reference to emergency telemedicine.

Proper citation: biolab - Bioengineering and Bioimages Laboratory (RRID:SCR_008029) Copy   

•   Source: SciCrunch Registry

http://www.dnaftb.org/dnaftb/

An animated primer on the basics of DNA, genes, and heredity organized around three key concepts: Classical Genetics, Molecules of Genetics, and Genetic Organization and Control. The science behind each concept is explained by: animation, image gallery, video interviews, problem, biographies, and links.

Proper citation: DNA From The Beginning: AN Animated Primer on the Basics of DNA, Genes, and Heredity (RRID:SCR_008028) Copy   

•   Source: SciCrunch Registry

http://locus.jouy.inra.fr/cgi-bin/bovmap/intro.pl

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database containing information on the cattle genome comprising loci list, phenes list, homology query, cattle maps, gene list, and chromosome homology. The objective of BovMap is to develop a set of anchored loci for the cattle genome map. In total, 58 clones were hybridized with chromosomes and identified loci on 22 of the 31 different bovine chromosomes. Three clones contained satellite DNA. Two or more markers were placed on 12 chromosomes. Sequencing of the microsatellites and flanking regions was performed directly from 43 cosmids, as previously reported. Primers were developed for 39 markers and used to describe the polymorphism associated with the corresponding loci. Users are also allowed to summit their own data for Bovmap. An integrated cytogenetic and meiotic map of the bovine genome has also been developed around the Bovmap database. One objective that Bovmap uses as the mapping strategy for the bovine genome uses large insert clones as a tool for physical mapping and as a source of highly polymorphic microsatellites for genetic typing.

Proper citation: BovMap Database (RRID:SCR_008145) Copy   

•   Source: SciCrunch Registry