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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

http://www.med.upenn.edu/genetics/dnaseq/index.shtml

Core facility that provides the following services: Large sequencing project support, Sanger sequencing service, High throughput DNA sequencing, Ion Torrent Personal Genome Machine sequencing, Template preparation and purification, Roche 454 sequencing, Sequence analysis and database search support, Construction of targeting vector for gene targeting, Genotyping and Fragment Analysis service, Molecular biology services, Mouse genotyping, and Ion Personal Genome Machine sequencing data analysis. The DNA Sequencing Facility provides long read, automated Sanger sequencing; microsatellite-based genotyping and fragment analysis; plasmid and BAC DNA preparation and purification; and related molecular biological services including PCR, cloning, sub-cloning, site-directed mutagenesis, and preparation of targeting vectors for gene targeting in mice. Core also provides services and support for analysis and interpretation of sequence data as well as the design of approaches to complex sequencing projects. For the last four years the facility has been providing Roche 454 sequencing service that includes library preparation, emulsion PCR and pyrosequencing for both genomic DNA and amplicons.

Proper citation: University of Pennsylvania Genomics Analysis Core (RRID:SCR_011061) Copy   

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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://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

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.midasplatform.org/

Open-source toolkit that enables the rapid creation of tailored, web-enabled data storage and provides a cohesive system for data management, visualization, and processing. At its core, Midas Platform is implemented as a PHP modular framework with a backend database (PostGreSQL, MySQL and non-relational databases). While the Midas Platform system can be installed and deployed without any customization, the framework has been designed with customization in mind. As building one system to fit all is not optimal, the framework has been extended to support plugins and layouts. Through integration with a range of other open-source toolkits, applications, or internal proprietary workflows, Midas Platform offers a solid foundation to meet the needs of data-centric computing. Midas Platform provides a variety of data access methods, including web, file system and DICOM server interfaces, and facilitates extending the methods in which data is stored to other relational and non-relational databases.

Proper citation: Midas Platform (RRID:SCR_002186) 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.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://www.census.gov/did/www/nlms/

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

Proper citation: National Longitudinal Mortality Study (RRID:SCR_008946) Copy   

•   Source: SciCrunch Registry

http://www.chernobyltissuebank.com/

The CTB (Chernobyl Tissue Bank) is an international cooperation that collects, stores and disseminates biological samples from tumors and normal tissues from patients for whom the aetiology of their disease is known - exposure to radioiodine in childhood following the accident at the Chernobyl power plant. The main objective of this project is to provide a research resource for both ongoing and future studies of the health consequences of the Chernobyl accident. It seeks to maximize the amount of information obtained from small pieces of tumor by providing multiple aliquots of RNA and DNA extracted from well documented pathological specimens to a number of researchers world-wide and to conserve this valuable material for future generations of scientists. It exists to promote collaborative, rather than competitive, research on a limited biological resource. Tissue is collected to an approved standard operating procedure (SOP) and is snap frozen; the presence or absence of tumor is verified by frozen section. A representative paraffin block is also obtained for each case. Where appropriate, we also collect fresh and paraffin-embedded tissue from loco-regional metastases. Currently we do not issue tissue but provide extracted nucleic acid, paraffin sections and sections from tissue microarrays from this material. The project is coordinated from Imperial College, London and works with Institutes in the Russian Federation (the Medical Radiological Research Centre in Obninsk) and Ukraine (the Institute of Endocrinology and Metabolism in Kiev) to support local scientists and clinicians to manage and run a tissue bank for those patients who have developed thyroid tumors following exposure to radiation from the Chernobyl accident. Belarus was also initially included in the project, but is currently suspended for political reasons.

Proper citation: Chernobyl Tissue Bank (RRID:SCR_010662) 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://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