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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://www.roswellpark.edu/shared-resources/gene-targeting-and-transgenic

Facility which provides researchers with transgenic mouse technologies, methods, and animal models. Knockout mice, transgenic mice, and mice on multiple strain backgrounds are provided.

Proper citation: RPCI Gene Targeting and Transgenic Shared Resource (RRID:SCR_001020) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

•   Source: SciCrunch Registry

http://cancergenome.nih.gov/

Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).

Proper citation: The Cancer Genome Atlas (RRID:SCR_003193) 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

http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=23686&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. The original aim of this study was to increase our understanding of the etiology of malignant lymphomas, especially in view of the increasing trend in incidence. Malignant lymphoma (including non-Hodgkin lymphoma, NHL, Hodgkin lymphoma, HL, and chronic lymphocytic leukemia, CLL) constitute a heterogeneous group of malignancies with regard to histology, molecular characteristics and clinical course. Etiological factors may also vary by lymphoma subtype. The incidence of NHL, the most common lymphoma group, has increased dramatically during the past decades in Sweden and in many other Western countries. The reasons for this increase as well as for the majority of all new cases is not well understood. Well established risk factors for lymphoma overall include hereditary and acquired disorders of strong immune dysfunction such as HIV/AIDS and organ transplantation, but they explain few new cases in the population. Approach: Population-based case-control study in Sweden and Denmark. The study includes in total 3740 patients and 3187 controls in both countries recruited during the period October 1999 to October 2002. Through a rapid case ascertainment system, the cases were identified shortly after diagnosis. The controls were randomly selected from national population registers and frequency-matched to the expected number of cases by sex and age group. Both cases and controls were interviewed by telephone based on a standardized questionnaire to obtain detailed information on potential risk factors for lymphoma such as medical history including infectious diseases, drug use and blood transfusions, socio-economic factors and life-style. Blood samples were also collected and stored as serum, plasma, DNA and live lymphocytes. In addition, written questionnaires about dietary habits or work exposures were sent out in Sweden. Tumor material from the cases was re-examined and uniformly classified according to the REAL classification. Status The data collection ended in 2002 and data analysis has been ongoing since then. We have primarily analyzed a range of environmental factors in relation risk of malignant lymphoma subgroups including sun exposure, body mass index, family history of hematopoietic cancer, allergy, autoimmune disorders and mononucleosis. We have also assessed specific genetic determinants in a subgroups of patients with follicular lymphoma and controls. Study results have so far been presented in 14 publications in peer-reviewed journals. In addition to new analyses on other environmental factors, we now also work to understand genetic susceptibility and gene-environmental interaction and risk of lymphoma. Also, prognostic studies have been initiated in collaboration with other research groups with regard to in CLL, HL and T-cell lymphoma.

Proper citation: SCALE - Scandinavian lymphoma etiology (RRID:SCR_006041) Copy   

•   Source: SciCrunch Registry

https://cibersort.stanford.edu/

Software tool to provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data. Used for characterizing cell composition of complex tissues from their gene expression profiles, large scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets.

Proper citation: CIBERSORT (RRID:SCR_016955) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/saint-apms/files/

Software tool for upgraded implementation of probabilistic scoring of affinity purification mass spectrometry data. Used for filtering high confidence interaction data from affinity purification mass spectrometry experiments. Used for assigning confidence scores to protein-protein interactions based on quantitative proteomics data in AP-MS experiments.

Proper citation: SAINTexpress (RRID:SCR_018562) Copy   

•   Source: SciCrunch Registry

https://cellrank.readthedocs.io/en/stable/

Software package for directed single cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease. Automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes.

Proper citation: CellRank (RRID:SCR_022827) Copy   

•   Source: SciCrunch Registry

https://mcmicro.org

Software tool as scalable, modular image processing pipeline for multiplexed tissue imaging. Transforms multi channel whole slide images into single cell data.

Proper citation: MCMICRO (RRID:SCR_022832) Copy   

•   Source: SciCrunch Registry

https://github.com/JonathanIrish/MEMv3

Software tool to calculate enrichment scores. Generates human and machine readable labels that quantify features enriched in sample. Used to identify multiple populations of cells and to compare each population to all of other remaining cells from original sample.

Proper citation: Marker Enrichment Modeling (RRID:SCR_022495) Copy   

•   Source: SciCrunch Registry

https://github.com/raphael-group/chisel

Software tool to infer allele and haplotype specific copy numbers in individual cells from low coverage single cell DNA sequencing data. Integrates weak allelic signals across individual cells, powering strength of single cell sequencing technologies to overcome weakness. Includes global clustering of RDRs and BAFs, and rigorous model selection procedure for inferring genome ploidy that improves both inference of allele specific and total copy numbers.

Proper citation: CHISEL (RRID:SCR_023220) Copy   

•   Source: SciCrunch Registry

https://github.com/mhammell-laboratory/TEtranscripts

Software package for including transposable elements in differential enrichment analysis of sequencing datasets. Used for including transposable elements in differential expression analysis of RNA-seq datasets. RNAseq TE quantification tool.

Proper citation: TEtranscripts (RRID:SCR_023208) Copy   

•   Source: SciCrunch Registry

http://mass-spec.stanford.edu

Core mass spec and proteomic services include open access lab for trained users with GC/MS, LC/MS, high resolution LC/MS, and MALDI-TOF instruments, help with intact protein analysis, targeted quantitation, drug discovery support, pathway analysis, protein interactions, FFPE tissue analysis, both labeled and label-free proteomics, and more. Please contact SUMS to discuss these and other custom projects including new application development.

Proper citation: Stanford University Vincent Coates Foundation Mass Spectrometry Laboratory Core Facility (RRID:SCR_017801) Copy   

•   Source: SciCrunch Registry

http://rhlccflow.facilities.northwestern.edu

Provides 6 cell sorters and 5 benchtop analyzers. Helps investigators to define their projects in the early stages of development to make optimal and efficient use of flow cytometry. Educates ALL users (faculty and staff) in the science and technology of flow cytometry.

Proper citation: Northwestern University Cancer Center Flow Cytometry Core Facility (RRID:SCR_017766) Copy   

•   Source: SciCrunch Registry

http://cancer.northwestern.edu/research/shared-resources/pathology-core-facility.html

Centralized, comprehensive, core laboratory providing histology, immunohistochemistry, molecular analysis and extraction and microscopic evaluation services for human tissue-based studies. Serves integral marker studies that require biomarker-based treatment arm assignment. Performs procurement of fresh biospecimens for clinical trials and biobanking.

Proper citation: Northwestern University School of Medicine Lurie Cancer Center Pathology Core Facility (RRID:SCR_017769) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/csb/sbi

Core provides consultations and collaborations on research studies requiring computational structural biology methods. Analyses available are to study of static structures, molecular dynamics studies for analyzing contribution of dynamic and collective motions to macromolecular functionality. Trains researchers in computational structural biology techniques, or works in collaborative manner with researchers.Provides access to software tools for protein and DNA sequence analysis, protein fold determination, homology modeling, active site identification, and analysis of effects of various mutations on structure and function of protein, along with additional computational analyses.

Proper citation: North Carolina University at Chapel Hill R.L. Juliano Structural Bioinformatics Core Facility (RRID:SCR_017836) Copy   

•   Source: SciCrunch Registry

https://www.med.unc.edu/csb/nmr/

Core to support academic and industrial users. Spectrometer time is available to trained users for an hourly fee. Lab manager trains new users, consults to determine whether NMR will be useful in their research, and helps design experimental plan to obtain information they need. Manager also operates spectrometer for users whose experiments do not justify time and expense of individual training.

Proper citation: North Carolina University at Chapel Hill School of Medicine Biomolecular NMR Laboratory Core Facility (RRID:SCR_017841) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/csb/pep

Core specializes in production of pure, functional proteins for structural, biophysical, and biochemical studies. Facility offers three categories of service:Protein Expression,Protein Purification,Scientific Consultation, Mentoring, and Training; Offers Isotope labeled proteins for NMR;High production scales for immunizations, drug discovery, structural biology;Endotoxin-free protein production;Stable cell line generation;Expert baculovirus expression;Custom packages to efficiently suit your needs.

Proper citation: North Carolina University at Chapel Hill School of Medicine Protein Expression and Purification Core Facility (RRID:SCR_017843) Copy   

•   Source: SciCrunch Registry

http://www.ucdmc.ucdavis.edu/cancer/research/sharedresources/flowcytometry.html

Core provides access to expertise and instrumentation for analytical flow cytometry, cell sorting and laser scanning cytometry.Cell sorting and analytical cytometers are located at three sites in Davis and Sacramento for research use on recharge basis. Provides cell sorters for assisted sorting on appointment basis and provides training to investigators in acquiring data on analytic cytometers.Can advise on experiment design for flow cytometry, provides training to enable independent use of analytical cytometers, and familiarize investigators with popular data analysis software tools such as BD Diva and CellQuest, FlowJo, ModFit and iBrowser.

Proper citation: University of California Davis Flow Cytometry Shared Resource Laboratory Core Facility (RRID:SCR_017826) Copy   

•   Source: SciCrunch Registry