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A SEED-quality automated service that annotates complete or nearly complete bacterial and archaeal genomes across the entire phylogenetic tree. RAST can also be used to analyze draft genomes.
Proper citation: RAST Server (RRID:SCR_014606) Copy
A peer review management system which encourages timely and high quality peer review by using a credit system. Reviewers complete reviews using a general webform on academickarma.org, and if that review is submited to an editor within ten days, the reviewer is awarded a certain amount of points (karma credits). The author and editor both receive the review. Editors can use Academic Karma to identify specialized reviewers for future reference. A reviewer's personal profile, which includes the amount of karma credits they've received, is connected to their ORCID account and publication record for an overview of the reviewer's work.
Proper citation: Academic Karma (RRID:SCR_014017) Copy
A production service that gives researchers the ability to create and manage long-term identifiers so that they can to track usage, get credit for their work, share their data, and have the data reused for additional research. As a result, EZID identifiers also make it possible to increase citations, to build on previous work, to conduct new research, and avoid duplicating previous efforts. EZID identifiers provide a simple but powerful way to track research materials, including datasets, throughout their life cycle. In this way, researchers can share their data, get more citations, and track their results.
Proper citation: EZID (RRID:SCR_006473) Copy
A system providing resolvable persistent Uniform Resource Identifiers (URIs) used to identify data for the scientific community, with a current focus on the Life Sciences domain. The provision of resolvable identifiers (URLs) fits well with the Semantic Web vision, and the Linked Data initiative. It provides direct access to the identified data using one chosen physical location (or resource). If more than one physical locations providing the data are recorded in the Registry, then you can access them via the top banner or by using a profile.
Proper citation: Identifiers.org (RRID:SCR_003735) Copy
http://edge.oncology.wisc.edu/edge.php
THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. EDGE is a scientific resource for toxicology-related gene expression information. The site contains databases and analyses of gene expression studies following exposure to a variety of chemicals or physiological changes. The ultimate goal of the EDGE is to map transcriptional changes from chemical exposure that will someday be used as a diagnostic fingerprint to predict toxicity as well as provide valuable insights into the basic molecular changes responsible. EDGE gives you the ability to easily answer the following fundamental questions about your data 1. Can I compare transcriptional profiles across treatments? 2. What genes respond to my treatment? 3. What influences my favorite gene(s)? One of the major objectives of toxicology is to understand the adverse health effects that result from exposure to foreign chemicals. The traditional method for assessing the toxicity of a test chemical is very resource intensive; requiring the commitment of large amounts of money, time, and animals. According to the National Toxicology Program (NTP), each chemical study requires between 2 and 4 million dollars and several years to complete. Due to the cost and labor intensive nature of these studies, the number of chemicals currently tested by the NTP stands at less than 500. Given these statistics and the fact that there are approximately 70,000 chemicals in commerce today, it is increasingly apparent that alternative methods for assessing toxic potential must be explored if a significant portion of the remaining chemicals is to be tested. One potential solution is to develop a comprehensive database that describes alterations in gene expression resulting from chemical exposure. The pattern of transcriptional activity will not only be highly sensitive indicator of chemical exposure, but that this pattern will be diagnostic for mechanistically linked toxicants. In our laboratory, we have chosen to address this problem through a combination of high throughput sequencing of expressed sequence tags (ESTs) and construction of custom toxicology-related cDNA microarrays derived from the unique ESTs identified in the sequencing effort. By using this approach, we can simultaneously develop a quantitative gene expression profile using ESTs and the reagents for further analyzing these changes in a rapid, highly parallel manner. In addition, the expression profiles are not biased for preselected favorite genes. The resulting gene expression pattern can then be used as diagnostic fingerprint to predict toxicity and/or carcinogenicity as well as provide valuable insight into the basic biochemical and molecular changes responsible for toxicity. Submission of total RNA for Bradfield Lab Microarray Microarray comparisons are made between untreated, control animals and animals treated with ONE treatment. Please make sure the RNA submitted adheres to this experimental design. Necessary information is available on the site.
Proper citation: EDGE: Environment, Drugs and Gene Expression (RRID:SCR_008187) Copy
http://www.aquatichabitats.com
Aquatic Habitats (AHAB) is the worlds largest manufacturer of housing systems for aquatic research animals. We are biologists first and engineers second, so we understand the complexity of aquatic life and how to sustain it. Our turnkey systems are secure, efficient and as fail-safe as possible.
Proper citation: Aquatic Habitats (RRID:SCR_008597) Copy
http://www.broad.mit.edu/mpg/grail/
A tool to examine relationships between genes in different disease associated loci. Given several genomic regions or SNPs associated with a particular phenotype or disease, GRAIL looks for similarities in the published scientific text among the associated genes. As input, users can upload either (1) SNPs that have emerged from a genome-wide association study or (2) genomic regions that have emerged from a linkage scan or are associated common or rare copy number variants. SNPs should be listed according to their rs#''s and must be listed in HapMap. Genomic Regions are specified by a user-defined identifier, the chromosome that it is located on, and the start and end base-pair positions for the region. Grail can take two sets of inputs - Query regions and Seed regions. Seed regions are definitely associated SNPs or genomic regions, and Query regions are those regions that the user is attempting to evaluate agains them. In many applications the two sets are identical. Based on textual relationships between genes, GRAIL assigns a p-value to each region suggesting its degree of functional connectivity, and picks the best candidate gene. GRAIL is developed by Soumya Raychaudhuri in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, and the Broad Institute. GRAIL is described in manuscript, currently in preparation.
Proper citation: Gene Relationships Across Implicated Loci (RRID:SCR_008537) Copy
Wheaton Industries Inc. is a leading marketer, manufacturer and re-packager of containers, laboratory ware, instrumentation and associated products and services sold principally to customers in the general laboratory, life sciences, and diagnostics and reagent / chemicals packaging markets. Our products and services are marketed and sold globally through two divisions. The laboratory research products are sold through Wheaton Science Products, and the packaging products are sold through Wheaton Science Packaging.
Proper citation: Wheaton Industry Inc (RRID:SCR_008565) Copy
Cambridge, Massachusetts-based biotechnology company focused on cancer. Focus areas are blood cancers and solid tumors. Compounds: ponatinib, AP26113, ridaforolimus and AP1903.
Proper citation: ARIAD (RRID:SCR_008559) Copy
http://ipmb.sinica.edu.tw/affy/
Affymetrix Gene Expression Service Lab, AGESL was established by IPMB, IMB and IBS, Academia Sinica and opened for service in June 2004. The lab provides a full service from quality control of customer-provided RNA samples to raw data acquisition, including Affymetrix recommended QC procedures, cDNA synthesis, in vitro transcription, fragmentation, hybridization, washing, staining and scanning. Sponsors: This resource is supported by Affymetrix, Inc. Keywords: Gene, Expression, Service, Laboratory, RNA, Data, Synthesis, cDNA, In vitro, Transcription, Fragmentation, Hybrdization, Washing, Staining, Scanning,
Proper citation: Affymetrix Gene Expression Service Lab (RRID:SCR_008396) Copy
Griffin (G-protein-receptor interacting feature finding instrument) is a high-throughput system to predict GPCR - G-protein coupling selectively with the input of GPCR sequence and ligand molecular weight. This system consists of two parts: 1) HMM section using family specific multiple alignment of GPCRs, 2) SVM section using physico-chemical feature vectors in GPCR sequence. G-protein coupled receptors (GPCR), which is composed of seven transmembrane helices, play a role as interface of signal transduction. The external stimulation for GPCR, induce the coupling with G-protein (Gi/o, Gq/11, Gs, G12/13) followed by different kinds of signal transduction to inner cell. About half of distributed drugs are intending to control this GPCR - G-protein binding system, and therefore this system is important research target for the development of effective drug. For this purpose, it is necessary to monitor, effectively and comprehensively, of the activation of G-protein by identifying ligand combined with GPCR. Since, at present, it is difficult to construct such biochemical experiment system, if the answers for experimental results can be prepared beforehand by using bioinformatics techniques, large progress is brought to G-protein related drug design. Previous works for predicting GPCR-G protein coupling selectivity are using sequence pattern search, statistical models, and HMM representations showed high sensitivity of predictions. However, there are still no works that can predict with both high sensitivity and specificity. In this work we extracted comprehensively the physico-chemical parameters of each part of ligand, GPCR and G-protein, and choose the parameters which have strong correlation with the coupling selectivity of G-protein. These parameters were put as a feature vector, used for GPCR classification based on SVM.
Proper citation: G protein receptor interaction feature finding instrument (RRID:SCR_008343) Copy
Roche NimbleGen, Inc. is a leading innovator, manufacturer and supplier of a proprietary suite of DNA microarrays, consumables, instruments and services. Roche NimbleGen uniquely produces high-density arrays of long oligo probes that provide greater information content and higher data quality necessary for studying the full diversity of genomic and epigenomic variation. Roche NimbleGen is enabling a new era of High-Definition Genomics by providing scientists with cost-effective, high-throughput tools for extracting and integrating complex data on important forms of genomic and epigenomic variation not previously accessible on a genome-wide scale. Scientists can thus obtain a clearer understanding of genomic and epigenomic structure and function and how they impact biology and medicine. This improved performance is made possible by Roche NimbleGen''s proprietary Maskless Array Synthesis (MAS) technology, which uses digital light processing and rapid, high-yield photochemistry to synthesize long oligo, high-density DNA microarrays with extreme flexibility. NimbleGen Systems was established in 1999. The MAS technology is the result of research collaborations between the departments of biotechnology, genetics, physics, and semiconductor engineering at the University of Wisconsin - Madison. Roche NimbleGen has the exclusive worldwide license to the MAS technology from the Wisconsin Alumni Research Foundation (WARF).
Proper citation: Roche NimbleGen (RRID:SCR_008571) Copy
http://go.princeton.edu/cgi-bin/GOTermFinder
The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: Generic GO Term Finder (RRID:SCR_008870) Copy
APID Interactomes (Agile Protein Interactomes DataServer) provides information on the protein interactomes of numerous organisms, based on the integration of known experimentally validated protein-protein physical interactions (PPIs). The interactome data includes a report on quality levels and coverage over the proteomes for each organism included. APID integrates PPIs from primary databases of molecular interactions (BIND, BioGRID, DIP, HPRD, IntAct, MINT) and also from experimentally resolved 3D structures (PDB) where more than two distinct proteins have been identified. This collection references protein interactors, through a UniProt identifier.
Proper citation: Agile Protein Interactomes DataServer (RRID:SCR_008871) Copy
http://plantgrn.noble.org/LegumeIP/
LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.
Proper citation: LegumeIP (RRID:SCR_008906) Copy
NeuroImaging laboratory focused on detecting early brain changes associated with cognitive decline and dementia that manages the neuroimaging component of all studies at the Layton Aging and Alzheimer's Center including acquisition and archival services, as well as volumetric analysis of anonymized MRI scans. Assistance with resulting data is also available, including statistical analysis, and preparation of materials for presentation and publication. The Layton Center also manages a library of thousands of digitized MRI scans, including what is believed to be the largest collection of longitudinal MRI scans of cognitively intact elderly subjects. The OADC Neuroimaging Lab conducts MRI studies on both 3 and 7T MRI systems using advanced sequences, employing a multimodal approach to brain imaging research.
Proper citation: Layton Center NeuroImaging Laboratory (RRID:SCR_008823) Copy
http://meme.nbcr.net/meme/cgi-bin/gomo.cgi
Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: GOMO - Gene Ontology for Motifs (RRID:SCR_008864) Copy
http://vortex.cs.wayne.edu/projects.htm#OE2GO
Onto-Express is a web-based tool in the Onto-Tools suite that performs automated function profiling for a list of differentially expressed genes. However, Onto-Express does not support functional profiling for the organisms that do not have annotations in public domain, or use of custom (i.e. user-defined) ontologies. This limitation is also true for most of the other existing tools for functional profiling, which means that researchers working with uncommon organisms and/or new annotations or ontologies may be forced to construct such profiles manually. Onto-Express To Go (OE2GO) is a new tool added to the Onto-Tools ensemble to address these issues. OE2GO is built on top of OE to leverage its existing functionality. In OE2GO, the users now have an option to use either the Onto-Tools database as a source of functional annotations or provide their own annotations in a separate file. Currently, OE2GO supports annotation file in the Gene Ontology format. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: Onto-Express To Go (OE2GO) (RRID:SCR_008854) Copy
The mission is to advance medical and biological research by providing the scientific community with standardized, high quality metabolic and physiologic phenotyping services for mouse models of diabetes, diabetic complications, obesity and related disorders.
Proper citation: National Mouse Metabolic Phenotyping Centers (RRID:SCR_008997) Copy
Web application for simulating SNP genotypes for case-control and affected-child trio studies by resampling from Phase I/II HapMap SNP data. The user provides a list of SNPs to be genotyped, along with a disease model file that describes causal SNPs and their effect sizes. The simulation tool is appropriate for candidate regions or whole-genome scans. (entry from Genetic Analysis Software)
Proper citation: HAP-SAMPLE (RRID:SCR_009234) Copy
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