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http://rulai.cshl.edu/cgi-bin/tools/ESE3/esefinder.cgi?process=home

A web-based resource that facilitates rapid analysis of exon sequences to identify putative exonic splicing enhancers (ESEs) responsive to the human SR proteins SF2/ASF, SC35, SRp40 and SRp55, and to predict whether exonic mutations disrupt such elements.

Proper citation: ESEfinder 3.0 (RRID:SCR_007088) Copy   

•   Source: SciCrunch Registry

https://www.cshl.edu/research/cancer/flow-cytometry/

Resource provides equipment, training, and operating assistance for cell sorting and analysis. Facility staff oversees equipment maintenance, trains new users, and assists with assay development and operation of equipment.

Proper citation: Cold Spring Harbor Laboratory Flow Cytometry Shared Resource Core Facility (RRID:SCR_022164) Copy   

•   Source: SciCrunch Registry

https://pathbio.med.upenn.edu/pbr/portal/flowcyto/

Flow cytometry shared resource laboratory at the University of Pennsylvania. Facility has instruments, which include analyzers, cell sorters, small particle detectors, dual fluorescence cell counter/viability instrument, tissue dissociator for cell preparation. Provides on-site and off-site support to instrument users, including analyzer and cell sorter training. Core's Research and Development team collaborates/consults with principal investigators in developing high-dimensional panels, as well as staining, acquisition, and analysis.

Proper citation: University of Pennsylvania Perelman School of Medicine Cytomics and Cell Sorting Resource Laboratory Core Facility (RRID:SCR_022376) Copy   

•   Source: SciCrunch Registry

http://www.sylvester.org/OGSR

Provides access to technologies and services for study of genomics and epigenomics of cancer, in addition to providing technical expertise for project design, trouble shooting and pre and post award support. Services include next generation sequencing, single cell genomics, spatial genomics, gene expression assays and molecular quantitation, services for sample extraction and QC.

Proper citation: University of Miami Sylvester Onco Genomics Shared Resource Core Facility (RRID:SCR_022502) Copy   

•   Source: SciCrunch Registry

http://igm.ucsd.edu/genomics/

Provides services including sequencing library preparation and sequencing on Illumina MiSeq and NovaSeq 6000 platforms. Supports single cell sequencing on 10X Genomics Chromium Controller. Provides Illumina Infinium Beadchips, which includes variety of whole genome genotyping arrays as well as Infinium MethylationEPIC BeadChip.

Proper citation: University of California at San Diego Institute for Genomic Medicine Genomics Center Core Facilitiy (RRID:SCR_022740) Copy   

•   Source: SciCrunch Registry

https://health.ucdavis.edu/cancer/research/sharedresources/im.html

Clinical Laboratory Improvement Amendments certified laboratory established to provide scientific support to investigators conducting clinical studies in humans, preclinical studies in animals and/or studies in veterinary medicine. Conducts immunology based assays needed to monitor patients in clinical immunotherapy trials. Offers molecular and cellular assays for investigators in different fields, including custom assays designes.

Proper citation: University of California Davis Health Immune Modeling, Analysis and Diagnostics Shared Resource Core Facility (RRID:SCR_023587) Copy   

•   Source: SciCrunch Registry

https://health.ucdavis.edu/cancer/research/sharedresources/ger.html

Provides expertise and comprehensive services to accomplish virtually every application of next generation sequencing based genomics research, including gene expression profiling, mutation/variant analyses, copy number analysis, epigenomics, metagenomics, single cell sequencing and spatial transcriptomics services. GSR also specializes in development of custom protocols and extensive data analysis and integrative bioinformatics support. The shared resource provides wide range of additional services for translational and clinical genomics research.

Proper citation: University of California Davis Health Genomics Shared Resource Core Facility (RRID:SCR_023586) Copy   

•   Source: SciCrunch Registry

https://health.ucdavis.edu/cancer/research/sharedresources/specimen.html

Provides specimens with annotated data for clinical and basic science research purposes.Biorepository functions as centralized tissue bank to provide researchers access to cancer- and non-cancer-related specimens including fresh or frozen tissue, paraffin blocks,sections and fluids, procured and stored using international standards of best practices and protocols compliant with Office for Human Research Protection. Specimens can be obtained prospectively as part of clinical trials or accessed through UC Davis Health Clinical Laboratories, where over 5.5 million blood specimens are processed annually.

Proper citation: University of California Davis Health Biorepository Shared Resource Core Facility (RRID:SCR_023583) Copy   

•   Source: SciCrunch Registry

https://health.ucdavis.edu/cancer/research/sharedresources/animalimaging.html

Provides access to in vivo imaging technologies including molecular imaging, optical imaging, quantitative physiologic and anatomic imaging, and whole body PET/CT scanning in humans and animals. Provides targeted imaging probes and tracers as well as expertise in planning, executing and analyzing in vivo imaging studies. Supports imaging studies in small animals, large animals and humans.This resource is located in three adjacent buildings in the Health Sciences district of the Davis campus:Center for Molecular and Genomic Imaging (CMGI), Genome and Biomedical Sciences Facility (small-animal imaging);Nuclear Magnetic Resonance (NMR) Facility, Tupper Hall (small-animal MRI);Center for Imaging Sciences (CIS), Veterinary School (large-animal imaging).

Proper citation: University of California Davis Health In Vivo Translational Imaging Shared Resource Core Facility (RRID:SCR_023589) Copy   

•   Source: SciCrunch Registry

https://ctl.cornell.edu/industry/mrdetect-license-request/

Software application to estimate presence of MRD in plasma cfDNA WGS through evaluation of matched tumour-derived mutations (SNVs or CNVs).

Proper citation: MRDetect (RRID:SCR_024766) Copy   

•   Source: SciCrunch Registry

https://www.minerva.im/

Software tools for interactive viewing and fast sharing of large image data. Comprises Minerva Author, a tool to create and annotate images, and Minerva Story, a narrative image viewer for web hosting. Used for interpreting and interacting with complex images, organized around guided analysis approach. Enables fast sharing of large image data that is stored on Amazon S3 and viewed using zoomable image viewer implemented using OpenSeadragon, making it ideal for integration into multi-omic browsers for data dissemination of tissue atlases.

Proper citation: Minerva (RRID:SCR_024750) Copy   

•   Source: SciCrunch Registry

https://scimap.xyz

Software toolkit for analyzing spatial molecular data. Underlying framework is generalizable to spatial datasets mapped to XY coordinates. Package uses anndata framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. Python based implementation efficiently deals with large datasets of millions of cells.

Proper citation: scimap (RRID:SCR_024751) Copy   

•   Source: SciCrunch Registry

https://reprint-apms.org/?q=chooseworkflow

Database of Mass Spectrometry contaminants and pipeline for Affinity Purification coupled with Mass Spectrometry analysis. Contaminant repository for affinity purification mass spectrometry data. Database of standardized negative controls. Used to identify protein-protein interactions.

Proper citation: CRAPome (RRID:SCR_025008) Copy   

•   Source: SciCrunch Registry

https://redu.ucsd.edu/

Software framework to find and re-analyze public Mass Spectrometry data. Used to find uniformly formatted public MS/MS data in the Global Natural Product Social Molecular Networking Platform (GNPS) via formatted metadata. New or previously collected data can be added provided they adhere to the ReDU metadata standards (the implemented drag-and-drop validator is applicable to any scientific data) and data are available in GNPS/MassIVE.

Proper citation: ReDU (RRID:SCR_025105) Copy   

•   Source: SciCrunch Registry

https://github.com/GregorySchwartz/too-many-cells

Software suite of tools, algorithms, and visualizations focusing on relationships between cell clades. This includes new ways of clustering, plotting, choosing differential expression comparisons. Identifies and visualizes relationships of single-cell clades.

Proper citation: TooManyCells (RRID:SCR_025328) Copy   

•   Source: SciCrunch Registry

https://github.com/willtownes/glmpca

Software R package for dimension reduction of non-normally distributed data. Generalized PCA for non-normally distributed data.

Proper citation: glmpca (RRID:SCR_025517) 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.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://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