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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

http://www.cincinnatichildrens.org/research/divisions/t/teen-labs/default/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 29,2023. Consortium made up of five clinical centers and a data coordinating center. The goal of Teen-LABS is to conduct clinical, epidemiological, and behavioral research in adolescent bariatric surgery, through an observational prospective study protocol. Teen-LABS is an ancillary study to LABS, an observational study of adult bariatric surgery. Research staff, certified in standardized uniform data collection according to the protocol, collect data at pre-operative research visits, at surgery, 30 days and six months post-operative, and annual post-operative research visits at the five participating centers.

Proper citation: Teen-Longitudinal Assessment of Bariatric Surgery (RRID:SCR_001492) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/CGEN.html

Software R package for analysis of case-control studies in genetic epidemiology.

Proper citation: CGEN (RRID:SCR_001251) Copy   

•   Source: SciCrunch Registry

http://www.morpholinodatabase.org/

Central database to house data on morpholino screens currently containing over 700 morpholinos including control and multiple morpholinos against the same target. A publicly accessible sequence-based search opens this database for morpholinos against a particular target for the zebrafish community. Morpholino Screens: They set out to identify all cotranslationally translocated genes in the zebrafish genome (Secretome/CTT-ome). Morpholinos were designed against putative secreted/CTT targets and injected into 1-4 cell stage zebrafish embryos. The embryos were observed over a 5 day period for defects in several different systems. The first screen examined 184 gene targets of which 26 demonstrated defects of interest (Pickart et al. 2006). A collaboration with the Verfaillie laboratory examined the knockdown of targets identified in a comparative microarray analysis of hematopoietic stem cells demonstrating how microarray and morpholino technologies can be used in conjunction to enrich for defects in specific developmental processes. Currently, many collaborations are underway to identify genes involved in morphological, kidney, skin, eye, pigment, vascular and hematopoietic development, lipid metabolism and more. The screen types referred to in the search functions are the specific areas of development that were examined during the various screens, which include behavior, general morphology, pigmentation, toxicity, Pax2 expression, and development of the craniofacial structures, eyes, kidneys, pituitary, and skin. Only data pertaining to specific tests performed are presented. Due to the complexity of this international collaboration and time constraints, not all morpholinos were subjected to all screen types. They are currently expanding public access to the database. In the future we will provide: * Mortality curves and dose range for each morpholino * Preliminary data regarding the effectiveness of each morpholino * Expanded annotation for each morpholino * External linkage of our morpholino sequences to ZFIN and Ensembl. To submit morpholino-knockdown results to MODB please contact the administrator for a user name and password.

Proper citation: Morpholino Database (RRID:SCR_001378) Copy   

•   Source: SciCrunch Registry

http://neuro.imm.dtu.dk/wiki/Main_Page

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 10, 2025. Semantic wiki with structured information, primarily from functional and molecular neuroimaging papers, but there are also other types of papers, e.g., from personality genetics. It lists results from neuroimaging studies, such as Talairach coordinates and brain volume measurements, as well as software packages and brain regions. SQL dumps of the structured information in the wiki is available so complex queries can be formed. The Brede Wiki templates store the structured information from neuroscience papers and editors may add free format text. Template definitions format the data so it is presented as tables on the formatted wiki-page. From a given PMID a web-service can format information from PubMed for inclusion in the Brede Wiki. A Matlab script can extract coordinates from SPM5 and format them in the Talairach coordinate template format.

Proper citation: Brede Wiki (RRID:SCR_001411) Copy   

•   Source: SciCrunch Registry

http://www.g-node.org/projects/odml

Mark up language for collecting and exchanging metadata in an automated, computer-based fashion, developed for neuroscience, specifically, neurophysiology experiments. In odML arbitrary metadata information is stored as extended key-value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability.

Proper citation: Open metadata mark up language (RRID:SCR_001376) Copy   

•   Source: SciCrunch Registry

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

R software that detects enriched regions of ChIP-seq data sets jointly.

Proper citation: Jmosaics (RRID:SCR_001094) Copy   

•   Source: SciCrunch Registry

http://www.softgenetics.com/mutationSurveyor.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software for DNA sequencing analysis that integates with Sanger Sequencing files generated by Applied Biosystems Genetic Analyzers, MegaBACE, and Beckman CEQ electrophoresis systems. It can be used to find single nucleotide polymorphisms (SNPs), insertions and deletions (INDELS), and somatic mutations in direct sequencing, PCR sequencing, mitochondrial DNA sequencing, and resequencing projects.

Proper citation: Mutation Surveyor (RRID:SCR_001247) Copy   

•   Source: SciCrunch Registry

http://genecodes.com/sequencher

Software for Next-Generation DNA sequencing, Sanger DNA analysis, and RNA sequencing. It contains sequence analysis tools which include reference-guided alignments, de novo assembly, variant calling, and SNP analyses. It has integrated the Cufflinks suite for in-depth transcript analysis and differential gene expression of RNA-Seq data.

Proper citation: Sequencher (RRID:SCR_001528) Copy   

•   Source: SciCrunch Registry

http://www.genome.jp/kegg/expression/

Database for mapping gene expression profiles to pathways and genomes. Repository of microarray gene expression profile data for Synechocystis PCC6803 (syn), Bacillus subtilis (bsu), Escherichia coli W3110 (ecj), Anabaena PCC7120 (ana), and other species contributed by the Japanese research community.

Proper citation: Kyoto Encyclopedia of Genes and Genomes Expression Database (RRID:SCR_001120) Copy   

•   Source: SciCrunch Registry

http://scitools.idtdna.com/analyzer/Applications/OligoAnalyzer/

Web-based application for analyzing oligonucleotides. Analysis proceeds after the sequence has been entered and the calculations modified based on target type, oligo concentration, sodium ion concentration, magnesium ion concentration, and dNTP concentration.

Proper citation: Integrated DNA Technologies OligoAnalyzer (RRID:SCR_001363) Copy   

•   Source: SciCrunch Registry

https://www.unafold.org/

Software package for nucleic acid folding and hybridization prediction. It has capabilities to predict folding for single-stranded RNA or DNA through a combination of free energy minimization, partition function calculations and stochastic sampling. The program runs on Unix and Linux platforms as well as Mac OS X and Windows.

Proper citation: UNAFold (RRID:SCR_001360) Copy   

•   Source: SciCrunch Registry

http://isp.imm.dtu.dk/thor/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022.Center hosting a number of related projects concerning neural networks, functional neuroimaging, multimedia signal processing, and biomedical signal processing. Neuroinformatics is a research field rooted in classical disciplines like signal processing, biology, physics, computer science and engineering. Neuroinformatics combines learning from the brain and learning about the brain. By studying information processing in the brain neuroinformatics invents new computing paradigms (e.g., artificial neural networks) with the objective of understanding the dynamics of the conscious mind. Artificial neural networks is an active neuroinformatics research field, which combines many approaches to adaptive signal processing in solving real world problems. They began using neural networks for general nonlinear adaptive signal processing. Since 1991 the CONNECT groups have participated in the development of neural computing as an advanced, non-linear statistical tool, which has been applied to forecasting within dynamical systems, pattern recognition, and medical image analysis, particularly functional neuroimages. While neural computing has largely been viewed as a black box approach, they have initiated research aimed at opening this black box, using hypertext, multimedia, and interactivity. Their key objective is to convert abstract models into intuitive knowledge through interactive visualization.

Proper citation: THOR Center for Neuroinformatics (RRID:SCR_001400) Copy   

•   Source: SciCrunch Registry

http://globin.cse.psu.edu/

Data and tools for studying the function of DNA sequences, with an emphasis on those involved in the production of hemoglobin. It includes information about naturally-occurring human hemoglobin mutations and their effects, experimental data related to the regulation of the beta-like globin gene cluster, and software tools for comparing sequences with one another to discover regions that are likely to play significant roles.

Proper citation: Globin Gene Server (RRID:SCR_001480) Copy   

•   Source: SciCrunch Registry

https://jhuccs1.us/nash/

Clinical research network to focus on the etiology, contributing factors, natural history, complications, and therapy of nonalcoholic steatohepatitis. They research the nature and underlying cause of Nonalcoholic Steatohepatitis (NASH) and conduct clinical studies on prevention and treatment. Approximately 1,500 pediatric and adult participants throughout the United States and Canada with nonalcoholic fatty liver disease (NAFLD) have enrolled into a database. The NASH CRN has recently reopened the database to enroll additional pediatric and adult participants with NAFLD. Serum, liver tissue, and genomic DNA samples are being collected and stored in the NIDDKrepository for ongoing as well as future studies. A three-arm randomized, placebo-controlled clinical trial of pioglitazone versus vitamin E completed enrollment in 2009. In addition to this adult trial, a similar trial in pediatric NASH patients randomized 180 children to receive treatment with vitamin E, metformin, or placebo.

Proper citation: Nonalcoholic Steatohepatitis Clinical Research Network (RRID:SCR_001519) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/45

Study group and network for a 2008 longitudinal study for the etiology, diagnosis, treatment, and outcome of acute liver failure in infants, children, and adolescents. Data from patients include urine, bile, serum, liver tissue, cell lines derived from fibroblast culture, and DNA.

Proper citation: Pediatric Acute Liver Failure Study (RRID:SCR_001478) Copy   

•   Source: SciCrunch Registry

http://www.stjudebgem.org/web/mainPage/mainPage.php

This database contains gene expression patterns assembled from mouse nervous tissues at 4 time points throughout brain development including embryonic (e) day 11.5, e15.5, postnatal (p) day 7 and adult p42. Using a high throughput in situ hybridization approach we are assembling expression patterns from selected genes and presenting them in a searchable database. The database includes darkfield images obtained using radioactive probes, reference cresyl violet stained sections, the complete nucleotide sequence of the probes used to generate the data and all the information required to allow users to repeat and extend the analyses. The database is directly linked to Pubmed, LocusLink, Unigene and Gene Ontology Consortium housed at the National Center for Biotechnology Information (NCBI) in the National Library of Medicine. These data are provided freely to promote communication and cooperation among research groups throughout the world.

Proper citation: Brain Gene Expression Map (RRID:SCR_001517) Copy   

•   Source: SciCrunch Registry

http://www.well.ox.ac.uk/happy/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package for Multipoint QTL Mapping in Genetically Heterogeneous Animals (entry from Genetic Analysis Software) The method is implemented in a C-program and there is now an R version of HAPPY. You can run HAPPY remotely from their web server using your own data (or try it out on the data provided for download).

Proper citation: Happy (RRID:SCR_001395) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/genoset.html

Software package to load, manipulate, and plot copynumber and BAF data by providing classes similar to ExpressionSet for copy number analysis. The class extends ExpressionSet by adding a locData slot for a RangedData or GRanegs object. This object contains feature genome location data and provides for efficient subsetting on genome location. CNSet and BAFSet extend GenoSet and require assayData matrices for Copy Number (cn) or Log-R Ratio (lrr) and B-Allele Frequency (baf) data. Implements and provides convenience functions for processing of copy number and B-Allele Frequency data.

Proper citation: GenoSet (RRID:SCR_001275) Copy   

•   Source: SciCrunch Registry

http://www.retinalmaps.com.au/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. A database of over 700 retinal topography maps of a wide variety of species published in a diversity of journals. It has been assembled to assist vision and neuroscience researchers to locate and compare the distribution of retinal neurons within and across species. The maps can be searched by taxonomic or common name classification, cell type sampled, type of retinal specialization and staining/visualization method. Maps can be compared by selecting multiple maps and clicking the Compare Selected button. An interactive spreadsheet can be also downloaded.

Proper citation: Retinal Topography Maps Database (RRID:SCR_001399) Copy   

•   Source: SciCrunch Registry