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http://www.t1diabetes.nih.gov/t1d-raid/index.shtml

NOTE: The T1D-RAID program is not currently accepting applications. Cooperative program that makes available, on a competitive basis, NCI resources for the pre-clinical development of drugs, natural products, and biologics to facilitate translation to the clinic of novel, scientifically meritorious therapeutic interventions for type 1 diabetes and its complications. A partial listing of those services includes: high-throughput screening, studies in animal models, formulation, pharmacology and toxicology studies, and bulk substances acquisition. Requests to T1D-RAID are brief (20 pages or less), and should clearly outline the resources required to ready the proposed therapeutic agent for clinical trials. T1D-RAID should enable entry into the clinic of promising molecules that are not otherwise likely to receive an adequate and timely clinical test. T1D-RAID is designed to accomplish the tasks that are rate-limiting in bringing discoveries from the laboratory to the clinic. Once a project has been approved, NIDDKstaff interact directly with the Principal Investigator (PI). NCI contractors perform the T1D-RAID-approved tasks under the direction of NIDDKand NCI staff. The required tasks will vary from project to project. In some cases T1D-RAID will support only one or two key missing steps necessary to bring a compound to the clinic; in other cases it may be necessary to supply the entire portfolio of development requirements needed to file an IND. Examples of tasks that can be supported by T1D-RAID include, but are not limited to: * Definition or optimization of dose and schedule for in vivo activity * Development of pharmacology assays * Conduct of pharmacology studies with a pre-determined assay * Acquisition of bulk substance (GMP and non-GMP) * Scale-up production from lab-scale to clinical-trials lot scale * Development of suitable formulations * Development of analytical methods for bulk substances * Production of dosage forms * Stability assurance of dosage forms * Range-finding initial toxicology * IND-directed toxicology, with correlative pharmacology and histopathology * Planning of clinical trials * Regulatory affairs, so that FDA requirements are likely to be satisfied by participating investigators seeking to test new molecular entities in the clinic * IND filing advice The output of T1D-RAID activities will be both products and information that will be made fully available to the originating investigator for support of an IND application and clinical trials. T1D-RAID does not sponsor clinical trials.

Proper citation: Type 1 Diabetes - Rapid Access to Intervention Development (RRID:SCR_000203) Copy   

•   Source: SciCrunch Registry

https://pharos.nih.gov/idg/index#

Database of ligands and diseases. Its goal is to develop a knowledge-base for the Druggable Genome (DG) in order to illuminate the uncharacterized and/or poorly annotated portion of the genome. DG, focusing on four of the most commonly drug-targeted protein families: G-protein-coupled receptors (GPCRs); nuclear receptors (NRs); ion channels (ICs); and kinases.

Proper citation: PHAROS (RRID:SCR_016258) Copy   

•   Source: SciCrunch Registry

http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

•   Source: SciCrunch Registry

https://signalingpathways.org

Web multi omics knowledgebase based upon public, manually curated transcriptomic and cistromic datasets involving genetic and small molecule manipulations of cellular receptors, enzymes and transcription factors. Integrated omics knowledgebase for mammalian cellular signaling pathways. Web browser interface was designed to accommodate numerous routine data mining strategies. Datasets are biocurated versions of publically archived datasets and are formatted according to recommendations of the FORCE11 Joint Declaration on Data Citation Principles73, and are made available under Creative Commons CC 3.0 BY license. Original datasets are available.

Proper citation: Signaling Pathways Project (RRID:SCR_018412) Copy   

•   Source: SciCrunch Registry

http://software.broadinstitute.org/gsea/msigdb/index.jsp

Collection of annotated gene sets for use with Gene Set Enrichment Analysis (GSEA) software.

Proper citation: Molecular Signatures Database (RRID:SCR_016863) Copy   

•   Source: SciCrunch Registry

https://wan-bioinfo.shinyapps.io/GESS/

Database of global evaluation of SARS-CoV-2/hCoV-19 sequences.Provides comprehensive analysis results based on tens of thousands of high-coverage and high-quality SARS-CoV-2 complete genomes.

Proper citation: GESS (RRID:SCR_021847) Copy   

•   Source: SciCrunch Registry

http://www.nihpromis.org/

Repository of person centered measures that evaluates and monitors physical, mental, and social health in adults and children.

Proper citation: Patient-Reported Outcomes Measurement Information System (RRID:SCR_004718) Copy   

•   Source: SciCrunch Registry

https://brd.nci.nih.gov/brd/

Free and publicly accessible literature database for peer-reviewed primary and review articles in the field of human Biospecimen Science. Each entry has been created by a Ph.D. level scientist to capture relevant parameters, pre-analytical factors, and original summaries of relevant results.

Proper citation: Biospecimen Research Database (RRID:SCR_001944) Copy   

•   Source: SciCrunch Registry

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

A set of specialist databases related to the study of polymorphic genes in the immune system. The IPD project works with specialist groups or nomenclature committees who provide and curate individual sections before they are submitted to IPD for online publication. The IPD project stores all the data in a set of related databases. IPD currently consists of four databases: * IPD-KIR, contains the allelic sequences of Killer-cell Immunoglobulin-like Receptors, * IPD-MHC, is a database of sequences of the Major Histocompatibility Complex of different species; * IPD-human platelet antigens, alloantigens expressed only on platelets and * IPD-ESTDAB, which provides access to the European Searchable Tumour cell-line database, a cell bank of immunologically characterized melanoma cell lines.

Proper citation: IPD - Immuno Polymorphism Database (RRID:SCR_003004) Copy   

•   Source: SciCrunch Registry

https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/

Open, global repository as central resource for reagents, protocols, panels, publications, software, and datasets. In addition to IBEX, we support standard, single cycle multiplexed imaging (Multiplexed 2D imaging), volume imaging of cleared tissues with clearing enhanced 3D (Ce3D), highly multiplexed 3D imaging (Ce3D-IBEX), and extension of the IBEX dye inactivation protocol to the Leica Cell DIVE (Cell DIVE-IBEX). Committed to sharing knowledge related to multiplexed imaging. Antibody validation community knowledgebase.

Proper citation: IBEX Knowledge Base (RRID:SCR_025296) Copy   

•   Source: SciCrunch Registry

http://biospecimens.cancer.gov/

The NCI Office of Biorepositories and Biospecimen Research (OBBR) was established in 2005 in recognition of the critical role that biospecimens play in cancer research. The OBBR is responsible for developing a common biorepository infrastructure that promotes resource sharing and team science, in order to facilitate multi-institutional, high throughput genomic and proteomic studies. OBBR is focused on the following objectives: * Establish biobanking as a new area of research, in order to determine the impact of various collection and processing protocols on the usefulness of biospecimens in genomic and proteomic studies * Disseminate first-generation Best Practices in order to harmonize policies and procedures of NCI-supported biorepositories * Develop future generations of biorepository best practices, based on the data generated in the biobanking research programs above * Promote professional oversight of biospecimen standards development by standards organizations * Develop new technologies for biorepository operations * Develop a biorepository accreditation program * Coordinate with the international biobanking community to harmonize policies and procedures to facilitate multi-national research

Proper citation: NCI Office of Biospecimens (RRID:SCR_007076) Copy   

•   Source: SciCrunch Registry

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

Curated database of published models so that they can be openly accessed, downloaded, and tested to support computational neuroscience. Provides accessible location for storing and efficiently retrieving computational neuroscience models.Coupled with NeuronDB. Models can be coded in any language for any environment. Model code can be viewed before downloading and browsers can be set to auto-launch the models. The model source code has to be available from publicly accessible online repository or WWW site. Original source code is used to generate simulation results from which authors derived their published insights and conclusions.

Proper citation: ModelDB (RRID:SCR_007271) Copy   

•   Source: SciCrunch Registry

http://www.mouseatlas.org/

A portal to the Mouse Atlas of Gene Expression Project and Dissecting Gene Expression Networks in Mammalian Organogenesis Project. This Atlas will define the normal state for many tissues by determining, in a comprehensive and quantitative fashion, the number and identity of genes expressed throughout development. The resource will be comprehensive, quantitative, and publicly accessible, containing data on essentially all genes expressed throughout select stages of mouse development. Serial Analysis of Gene Expression (SAGE) is the gene expression methodology of choice for this work. Unlike expressed sequence tags (ESTs) and gene chip data, SAGE data are independent of prior gene discovery and are quantitative. Furthermore, SAGE data are digital, easily exchanged between laboratories for comparison and can be added to by scientists for years to come. Thus, this Atlas will include a data structure and data curation strategy that will facilitate the ongoing collection of gene expression data, even after the completion of this project. The Mouse Atlas project compromises 202 SAGE Libraries from 198 tissues. The list of libraries is available in a number of different groupings, including groups of libraries taken from specific tissue locations and libraries taken from specific developmental stages. Furthermore, this atlas will assemble gene expression profiles for a few focused experiments that will test hypotheses related to the techniques employed, tumor models and models of abnormal development. This will test the resource and provide quality control, validation and demonstrate applicability. Additionally, The Mammalian Organogenesis - Regulation by Gene Expression Networks (MORGEN) project will provide a complete, permanent, and accurate picture of mouse gene expression in the heart (atrioventricular canal and outflow tract), pancreas, and liver; new techniques to understand the interplay of proteins governing the expression of genes key to the development of these organ systems; and the identification of the master regulatory switches that control development of the tissues.

Proper citation: Mouse Gene Expression at the BC Cancer Agency (RRID:SCR_008091) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/GSVA.html

Open source software R package for assaying variation of gene set enrichment over sample population.Used for microarray and RNA-seq data analysis. Gene set enrichment method that estimates variation of pathway activity over sample population in unsupervised manner.

Proper citation: GSVA (RRID:SCR_021058) Copy   

•   Source: SciCrunch Registry

https://github.com/labsyspharm/cylinter

Open source software tool as interactive image segmentation filter for multiplex microscopy that aids in identification and removal of cell segmentation instances corrupted by optical and image processing artifacts.

Proper citation: CyLinter (RRID:SCR_021157) Copy   

•   Source: SciCrunch Registry

https://www.archrproject.com/

Software R package for processing and analyzing single-cell ATAC-seq data. Used for integrative single cell chromatin accessibility analysis.Provides intuitive, user focused interface for complex single cell analysis, including doublet removal, single cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing.

Proper citation: ArchR (RRID:SCR_020982) Copy   

•   Source: SciCrunch Registry

https://mcmicro.org/

Open source software tool as multiple choice microscopy pipeline for multiplexed whole slide imaging and tissue microarrays. Scalable, modular image processing pipeline for multiplexed tissue imaging. Used for performing sequential steps needed to transform large, multi channel whole slide images into single cell data.

Proper citation: MCMICRO (RRID:SCR_021048) Copy   

•   Source: SciCrunch Registry

https://github.com/JamieHeather/stitchr

Software Python tool for stitching coding T cell receptors nucleotide sequences from V,J,CDR3 information. Produces complete coding sequences representing fully spliced TCR cDNA given minimal V,J,CDR3 information.

Proper citation: Stitchr (RRID:SCR_022139) Copy   

•   Source: SciCrunch Registry

https://seer.cancer.gov/siterecode/icdo3_dwhoheme/index.html

Website describing International Classification of Diseases-Oncology codes that corresponds to different cancer sites in the Surveillance, Epidemiology, and End Results (SEER) registry.

Proper citation: NCI Site Recode ICD-O-3/WHO 2008 Definition (RRID:SCR_024687) Copy   

•   Source: SciCrunch Registry

http://cabig.cancer.gov

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 19, 2016. It has been integrated into the National Cancer Informatics Program (NCIP). The National Cancer Institute launched the cancer Biomedical Informatics Grid (caBIG) to create a virtual network of interconnected data, individuals, and organizations that worked together to redefine how cancer research is conducted. caBIG capabilities allowed researchers and clinicians to collaborate more effectively so that complex research questions might be asked and answered faster and more effectively. The mission of caBIG was to develop a truly collaborative information network that accelerated the discovery of new approaches for the detection, diagnosis, treatment, and prevention of cancer, ultimately improving patient outcomes.

Proper citation: Cancer Biomedical Informatics Grid (RRID:SCR_003328) Copy   

•   Source: SciCrunch Registry