Tools   Select Another Resource Report Type

http://www.glycosciences.de/tools/glyseq/

Service dedicated to statistically analyze the sequences around glycosylation sites. Glycosylation belongs to the most common and most important co- and postranslational modifications of proteins. Since it is often difficult to determine which potential glycosylation sites are in fact glycosylated, there is only few data available about glycoproteins. Sources from which such data can be retrieved are SwissProt and the Protein Data Bank (PDB). Data from the PDB is obtained using pdb2linucs and updated weekly. GlySeq is dedicated to statistically analyze these sequences, especially the areas around glycosylation sites.

Proper citation: GlySeq (RRID:SCR_001569) Copy   

•   Source: SciCrunch Registry

http://www.biopax.org/

Community standard for pathway data sharing. Standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. Supports data exchange between pathway data groups and thus reduces complexity of interchange between data formats by providing accepted standard format for pathway data. Open and collaborative effort by community of researchers, software developers, and institutions. BioPAX is defined in OWL DL and is represented in RDF/XML format.Uses W3C standard Web Ontology Language, OWL.

Proper citation: Biological Pathways Exchange (RRID:SCR_001681) Copy   

•   Source: SciCrunch Registry

https://people.cas.sc.edu/rorden/ezdicom/index.html

Software designed to display most medical images, including MRI, CT, X-ray, and ultrasound. All versions of ezDICOM can automatically detect the format of a medical image and display it on the screen. The software is easy to use, mature, and can view a wide range of medical images including proprietary formats as well as images in the DICOM standard. The software will also automatically recognize and display Analyze, GE (LX, Genesis), Interfile, Siemens (Magnetom, Somatom) and NEMA images.

Proper citation: ezDICOM (RRID:SCR_001686) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/21

Data and biological samples were collected by this consortium organizing international efforts to identify genes that determine an individual risk of type 1 diabetes. It originally focused on recruiting families with at least two siblings (brothers and/or sisters) who have type 1 diabetes (affected sibling pair or ASP families). The T1DGC completed enrollment for these families in August 2009. They completed enrollment of trios (father, mother, and a child with type 1 diabetes), as well as cases (people with type 1 diabetes) and controls (people with no history of type 1 diabetes) from populations with a low prevalence of this disease in January 2010. T1DGC Data and Samples: Phenotypic and genotypic data as well as biological samples (DNA, serum and plasma) for T1DGC participants have been deposited in the NIDDKCentral Repositories for future research.

Proper citation: Type 1 Diabetes Genetics Consortium (RRID:SCR_001557) Copy   

•   Source: SciCrunch Registry

https://protein.mpiib-berlin.mpg.de/cgi-bin/pdbs/2d-page/extern/index.cgi

The Proteome 2D-PAGE Database system for microbial research is a curated database for storing and investigating proteomics data. Software tools are available and for data submission, please contact the Database Curator. Established at the Max Plank Institution for Infection Biology, this system contains four interconnected databases: i.) 2D-PAGE Database: Two dimensional electrophoresis (2-DE) and mass spectrometry of diverse microorganisms and other organisms. This database currently contains 4971 identified spots and 1228 mass peaklists in 44 reference maps representing experiments from 24 different organisms and strains. The data were submitted by 84 Submitters from 24 Institutes and 12 nations. It also contains various software tools that are important in formatting and analyzing gels and mass peaks; software include: *TopSpot: Scanning the gel, editing the spots and saving the information *Fragmentation: Fragmentation of the gel image into sections *MS-Screener: Perl script to compare the similarity of MALDI-PMF peaklists *MS-Screener update: MS-Screener can be used to compare mass spectra (MALDI-MS(/MS) as well as ESI-MS/MS spectra) on the basis of their peak lists (.dta, .pkm, .pkt, or .txt files), to recalibrate mass spectra, to determine and eliminate exogenous contaminant peaks, and to create matrices for cluster analyses. *GelCali: Online calibration of the Mr- and pI-axis of 2-DE gels with mathematical regression methods ii.)Isotope Coded Affinity Tag (ICAT)-LC/MS database: Isotope Coded Affinity Tag (ICAT)-LC/MS data for Mycobacterium tuberculosis strain BCG versus H37Rv. iii.) FUNC_CLASS database: Functional classification of diverse microorganism. This database also integrates genomic, proteomic, and metabolic data. iv.) DIFF database: Presentation of differently regulated proteins obtained by comparative proteomic experiments using computerized gel image analysis.

Proper citation: Proteome 2D-PAGE Database (RRID:SCR_001678) Copy   

•   Source: SciCrunch Registry

https://iscconsortium.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Consortium to advance the understanding of intestinal epithelial stem cell biology during development, homeostasis, regeneration and disease. Its immediate goals are to isolate, characterize, culture and validate populations of intestinal stem cells; answer major questions in stem cell biology of the intestinal epithelium; and accelerate research by making information and resources available to the research community. Resources include data sets, protocols, and a resource catalog. Long-term goals include: 1) laying the ground work for therapeutic manipulation of the intestinal epithelium 2) contributing to the greater understanding of stem cell biology through knowledge of the intestine as a model stem cell-driven system. Research Projects are housed at 8 institutions across the nation: Oregon Health & Science University, Stanford University, Stowers Institute for Medical Research, University of California, Los Angeles School of Medicine (UCLA) (partnered with the VA Greater Los Angeles), University of North Carolina, Chapel Hill (UNC), University of Oklahoma, University of Pennsylvania, and University of Pittsburgh.

Proper citation: Intestinal Stem Cell Consortium (RRID:SCR_001555) Copy   

•   Source: SciCrunch Registry

https://bvsalud.org/en/

VHL is a library, a decentralized and dynamic information-source collection, designed to provide equitable access to scientific knowledge on health. This collection operates as an Internet network of products and services, structured to progressively meet the need for information on health on the part of authorities, administrators, researchers, professors, students, professionals, the media and the general public. It sets itself apart from other information sources available on the Internet due to its selection criteria and quality control. The Virtual Health Library is envisioned as the broad of scientific and technical knowledge based in health-entered, organized, and stored in electronic format in the countries of the Region, universally accessible on the Internet and compatible with international databases. The VHL is a common space shared by information users, producers and intermediaries. It is based on the information paradigm set by the Internet, where users may interact with networks of information sources and with other users. The most important consequence of this paradigm is the convergence of functions and activities of production, intermediation and use of information sources. It will also be possible to enrich, schedule, reformulate, and/or translate the basic information sources into new information products and services, with value added, in order to meet more efficiently the information needs of users from specific communities. BIREME has established a plan of action to implement the Virtual Health Library based on 5 lines of action: promotion and marketing; realignment of traditional products and services; production of electronic publications; development of tools for integrating and locating information; and development of other Virtual Health Library components. Sponsors: VHL is supported by the Latin-American and Caribbean Center on Health Sciences Information.

Proper citation: Virtual Health Library (RRID:SCR_001676) Copy   

•   Source: SciCrunch Registry

http://ribosome.fandm.edu

This is a database of 16S and 23S ribosomal RNA mutations reported in literature, expanded to include mutations in ribosomal proteins and ribosomal factors. Access to the expanded versions of the 16S and 23S Ribosomal RNA Mutation Databases has been improved to permit searches of the lists of alterations for all the data from (1) one specific organism, (2) one specific nucleotide position, (3) one specific phenotype, or (4) a particular author. Please send bibliographic citations for published work to be included in The Ribosomal Mutation Database to the curator via email. The database currently consists of 1024 records, including 485 16S rRNA records from Escherichia coli, 37 16S-like rRNA records from other organisms, 421 23S rRNA records from E. coli, and 81 23S-like records from other organisms. The numbering of positions in all records corresponds to the numbering in E. coli. We welcome any suggested revisions to the database, as well as information about newly characterized 16S or 23S rRNA mutations. The expanded database will be renamed to The Ribosomal Mutation Database and will include mutations in ribosomal proteins and ribosomal factors.

Proper citation: Ribosomal Mutation Database (RRID:SCR_001677) Copy   

•   Source: SciCrunch Registry

http://incf.org/programs/atlasing/projects/waxholm-space

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 1st, 2023. Coordinate based reference space for the mapping and registration of neuroanatomical data. Users can download image volumes representing the canonical Waxholm Space (WHS) adult C57BL/6J mouse brain, which include T1-, T2*-, and T2-Weighted MR volumes (generated at the Duke Center for In-Vivo Microscopy), Nissl-stained optical histology (acquired at Drexel University), and a volume of labels. All volumes are represented at 21.5μ isotropic resolution. Datasets are provided as gzipped NIFTI files.

Proper citation: Waxholm Space (RRID:SCR_001592) Copy   

•   Source: SciCrunch Registry

http://www.neurologychannel.com/

A topical portal which provides information about conditions that affect the nervous system (brain, spinal cord, nerves, and muscles), such as stroke (brain attack), Alzheimer's disease, and back pain. It is a physician developed and monitored source of neurology information for consumers. Additionally, it contains comprehensive condition and treatment information, as well as interactive tools.

Proper citation: Neurologychannel (RRID:SCR_001597) Copy   

•   Source: SciCrunch Registry

https://github.com/Ensembl

Public database that stores areas of genome that differ between individual genomes (variants) and, where available, associated disease and phenotype information. Different types of variants for several species: single nucleotide polymorphisms (SNPs), short nucleotide insertions and/or deletions, and longer variants classified as structural variants (including CNVs). Effects of variants on the Ensembl transcripts and regulatory features for each species are predicted. You can run same analysis on your own data using Variant Effect Predictor. These data are integrated with other data sources in Ensembl, and can be accessed using the API or website. For several different species in Ensembl, they import variation data (SNPs, CNVs, allele frequencies, genotypes, etc) from a variety of sources (e.g. dbSNP). Imported variants and alleles are subjected to quality control process to flag suspect data. In human, they calculate linkage disequilibrium for each variant, by population.

Proper citation: Ensembl Variation (RRID:SCR_001630) Copy   

•   Source: SciCrunch Registry

http://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&BLAST_PROGRAMS=megaBlast&PAGE_TYPE=BlastSearch

Web application to search nucleotide databases using a nucleotide query. Algorithms: blastn, megablast, discontiguous megablast.

Proper citation: BLASTN (RRID:SCR_001598) Copy   

•   Source: SciCrunch Registry

http://type1diabetes.jax.org/

International repository for importation, curation, genotypic and phenotypic validation, cryopreservation, and distribution of mouse stocks of value to the type 1 diabetes scientific community holding over 250 genetically modified or congenic mouse stocks that are being used to dissect genetic and biologic features of T1D. They provide extensive genotypic and phenotypic quality control and genetic stabilization for these strains, as well as incidence studies when available. An added value of T1DR stocks is their ability to propel advances in related areas of science, including research in non-T1D autoimmunity and infectious diseases. The staff provides information and technical assistance regarding selection and use of existing T1DR models, and will provide limited support for development of new models considered to be of high-value for the T1D community. The resource includes strains generated at the Jackson Laboratory as well as strains donated by external scientists. Investigators are highly encouraged to donate a strain to ensure its preservation and availability to other researchers.

Proper citation: Type 1 Diabetes Resource (RRID:SCR_001475) Copy   

•   Source: SciCrunch Registry

http://www.diabetestrialnet.org/

International network of researchers who are exploring ways to prevent, delay and reverse the progression of type 1 diabetes. It is conducting clinical trials with researchers from 18 Clinical Centers in the United States, Canada, Finland, United Kingdom, Italy, Germany, Australia and New Zealand. In addition, more than 150 medical centers and physician offices are participating in the TrialNet network. Studies are available for people newly diagnosed with type 1 diabetes, as well as for relatives of people with type 1 diabetes who are at greater risk of developing the disease. This NIH-sponsored clinical trials network conducts studies designed to evaluate new approaches to prevent or ameliorate type 1 diabetes specifically by interdicting the type 1 diabetes disease process. These include interventions designed to decrease beta-cell destruction and/or enhance beta-cell survival. Studies are conducted in non-diabetic persons at risk of type 1 diabetes in an effort to delay the development of type 1 diabetes as a clinical disease; or (if initiated prior to appearance of autoimmunity) in an effort to delay the appearance of autoimmunity; or in individuals with type 1 diabetes who are either newly diagnosed or have evidence of sustained beta cell function. Studies include long-term follow-up of subjects developing type 1 diabetes. The TrialNet network also supports natural history and genetics studies in populations screened for or enrolled in studies conducted by the TrialNet study group. In addition, TrialNet will evaluate methodologies that enhance the conduct of clinical trials interdicting the type 1 diabetes disease process.

Proper citation: Type 1 Diabetes TrialNet (RRID:SCR_001508) Copy   

•   Source: SciCrunch Registry

http://ancora.genereg.net/

Web resource that provides data and tools for exploring genomic organization of highly conserved noncoding elements (HCNEs) for multiple genomes. It includes a genome browser that shows HCNE locations and features novel HCNE density plots as a powerful tool to discover developmental regulatory genes and distinguish their regulatory elements and domains. They identify HCNEs as non-exonic regions of high similarity between genome sequences from distantly related organisms, such as human and fish, and provide tools for studying the distribution of HCNEs along chromosomes. Major peaks of HCNE density along chromosomes most often coincide with developmental regulatory genes. Their aim with this site is to aid discovery of developmental regulatory genes, their regulatory domains and their fundamental regulatory elements.

Proper citation: Ancora (RRID:SCR_001623) Copy   

•   Source: SciCrunch Registry

http://www.statepi.jhsph.edu/ckid/

Prospective, observational cohort study of children with mild to moderate chronic kidney disease (CKD) to: (1) determine risk factors for progression of pediatric chronic kidney disease (CKD); (2) examine the impact of CKD on neurocognitive development; (3) examine the impact of CKD on risk factors for cardiovascular disease, and; (4) examine the impact of CKD on growth. The CKiD study population will include a cohort of 540 children, age 1 16 years, expected to be enrolled over a 24-month period.

Proper citation: CKID A Prospective Cohort Study of Kidney Disease in Children (RRID:SCR_001500) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.4/bioc/html/rMAT.html

Software package for normalizing and analyzing tiling arrays and ChIP-chip data. It is the R-version of a MAT program.

Proper citation: rMAT (RRID:SCR_001583) Copy   

•   Source: SciCrunch Registry

http://www.citregistry.org/

Collect, analyze, and communicate on comprehensive and current data on all islet/beta cell transplants in human recipients performed in North America, as well as some European and Australian centers to expedite progress and promote safety in islet/beta cell transplantation. This site serves as a repository for general information concerning protocols, clinical transplantation sites, publications, and other information of interest to the general community. Annual Reports are available. Islet/beta cell transplantation is a complex procedure with many factors contributing to the outcome. Compiling and analyzing data from all transplant centers in the US, Canada, as well as some European and Australian centers will accelerate the identification of both critical risk factors and key determinants of success and thereby guide transplant centers in developing and refining islet/beta cell transplant protocols. The inclusion of the term collaborative in the name of the Registry emphasizes the importance of collaboration in fulfilling the CITR mission and goals. Close collaboration with the transplant centers will ensure that relevant questions are addressed, that data submitted are accurate and complete, and that the needs of the transplant community are served. Information on how to participate as a CITR Transplant Center and to receive a transplant center application is available through the website. Progress in islet transplantation depends entirely on complete, high-quality medical data, including the information patients consented to report to the Collaborative Islet Transplant Registry. To make it as easy as possible to provide updated information about patient's health, an on-line questionnaire is available or patients can mail it to their transplant center. This information is very important in the continuing search for a cure for Type 1 diabetes.

Proper citation: Collaborative Islet Transplant Registry (RRID:SCR_001466) Copy   

•   Source: SciCrunch Registry

https://phenogen.org

Website for analyzing microarray data. Software toolbox for storing, analyzing and integrating microarray data and related genotype and phenotype data. The site is particularly suited for combining QTL and microarray data to search for candidate genes contributing to complex traits. In addition, the site allows, if desired by the investigators, sharing of the data. Investigators can conduct in-silico microarray experiments using their own and/or shared data. There are five major sections of the site: Genome/Transcriptome Data Browser, Microarray Analysis Tools, Gene List Analysis Tools, QTL Tools, and Downloads. The genome/transcriptome data browser combines a genome browser with all the microarray, RNA-Seq, and Genomic Sequencing data. This provides an effective platform to view all of this data side by side. Source code is available on GitHub.

Proper citation: PhenoGen Informatics (RRID:SCR_001613) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.12/bioc/html/CGHcall.html

Software that calls aberrations for array CGH data using a six state mixture model and several biological concepts. It is written in R.

Proper citation: CGHcall (RRID:SCR_001578) Copy   

•   Source: SciCrunch Registry