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http://cancer.case.edu/sharedresources/tissue/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Case Comprehensive Cancer Center''s Biorepository and Tissue Processing Core Facility (BTPC) serves two primary functions: 1. To build an inventory of remnant human tissues, blood and other body fluids (collectively termed biospecimens) targeted towards cancer and other medical research, for later assignment to investigators; and 2. To provide long term, controlled storage of biospecimens for specific researchers. These samples are for research purposes only and may not be used for clinical diagnosis or implantation into humans. Clinical information relating to the samples and donors are collected and maintained in a secure database. Samples and data are de-identified or de-linked before release to the researcher unless he/she has specific IRB approval to gain access to this information. Remnant biospecimens are prospectively collected from surgical procedures, autopsies and clinical laboratories for the BTPC by the Human Tissue Procurement Facility (HTPF), which operates under UH-IRB Protocol 01-02-45. Blood and bone marrow specimens are collected for the BTPC by the Hematopoietic Stem Cell Core Facility (HSCC), which operates under UH-IRB Protocol 09-90-195. The Division of Surgical Pathology at University Hospitals Case Medical Center (UHCMC) has clinical archives of paraffin blocks that can be made available through the BTPC for retrospective research studies under the approval of the Vice Chair for Clinical Affairs at UHCMC. Surgical Pathologists associated with the BTPC are responsible for determining which blocks can be made available and how much material can be removed from the blocks. Types of Tissue Available * Malignant, benign, diseased, normal and normal human tissues * Normal adjacent tissues available paired with tumor specimens in many cases * Tissues are collected from over 50 anatomic sites * Frozen specimens, OCT-embedded and paraffin-embedded tissues * Large array of paraffin-embedded specimens from clinical archives of paraffin blocks and QC research blocks maintained by the HTPF * Peripheral blood and bone marrow samples from initial visits and follow-up procedures are processed to obtain serum and cell fractions for storage * No samples are collected from individuals with known infectious illnesses * Fetal biospecimens are not collected due to state and local statutes

Proper citation: Case Comprehensive Cancer Center Biorepository and Tissue Processing Core Facility (RRID:SCR_004382) Copy   

•   Source: SciCrunch Registry

http://www.uclaaidsinstitute.org/researchareas/clinical_malignancy.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 27, 2012. The National Cancer Institute established centers in the United States and its territories for the collection and distribution of tissues, blood and secretions from patients with clinically-characterized AIDS related malignancies in 1994. The AIDS Malignancy Bank makes these tissues available to qualified investigators in the United States for research on AIDS malignancies. It is hoped that by providing access to these high-quality specimens, research in AIDS-related malignancies will be encouraged and expanded. The AMB contains formalin-fixed paraffin-embedded tissues, fresh-frozen tissues, malignant-cell suspensions, fine-needle aspirates, and cell lines from AIDS-related malignancies. The bank also contains serum, plasma, urine, bone marrow, cervical secretions, anal swabs, saliva semen and multi-site autopsy tissues from patients with AIDS-related malignancies who have participated in clinical trials. The bank has an associated database that contains prognostic, staging, outcome and treatment data on patients from whom tissues were obtained. Researchers pay for preparation and shipping of specimens.

Proper citation: AIDS Malignancy Bank (RRID:SCR_004417) Copy   

•   Source: SciCrunch Registry

http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

•   Source: SciCrunch Registry

http://www.scandb.org/newinterface/about.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A large-scale database of genetics and genomics data associated to a web-interface and a set of methods and algorithms that can be used for mining the data in it. The database contains two categories of single nucleotide polymorphism (SNP) annotations: # Physical-based annotation where SNPs are categorized according to their position relative to genes (intronic, inter-genic, etc.) and according to linkage disequilibrium (LD) patterns (an inter-genic SNP can be annotated to a gene if it is in LD with variation in the gene). # Functional annotation where SNPs are classified according to their effects on expression levels, i.e. whether they are expression quantitative trait loci (eQTLs) for that gene. SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms. . eQTL files and reported GWAS from NHGRI may be downloaded., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SCAN (RRID:SCR_005185) Copy   

•   Source: SciCrunch Registry

https://www.nature.com/articles/s41467-018-03367-w

Nanodroplet processing platform for deep and quantitative proteome profiling of 10 to 100 mammalian cells. It enhances efficiency and recovery of sample processing by downscaling processing volumes.

Proper citation: nanoPOTS (RRID:SCR_017129) Copy   

•   Source: SciCrunch Registry

https://amp.pharm.mssm.edu/geneshot/

Software tool as search engine for ranking genes from arbitrary text queries. Enables to enter arbitrary search terms, to receive ranked lists of genes relevant to search terms. Returned ranked gene lists contain genes that were previously published in association with search terms, as well as genes predicted to be associated with terms based on data integration from multiple sources. Search results are presented with interactive visualizations.

Proper citation: Geneshot (RRID:SCR_017582) Copy   

•   Source: SciCrunch Registry

http://taylor0.biology.ucla.edu/structureHarvester/

Web based program for collating results generated by program STRUCTURE. Provides assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of number of genetic groups that best fit data. Reformats data for use in downstream programs, such as CLUMPP.It is complement for using software Structure in genetics population. Website and program for visualizing STRUCTURE output and implementing Evanno method., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Structure Harvester (RRID:SCR_017636) 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://maayanlab.cloud/chea3/

Web based transcription factor enrichment analysis. Web server ranks TFs associated with user-submitted gene sets. ChEA3 background database contains collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate composite rank that improves prediction of correct upstream TF compared to ranks produced by individual libraries.

Proper citation: ChIP-X Enrichment Analysis 3 (RRID:SCR_023159) Copy   

•   Source: SciCrunch Registry

https://kleintools.hms.harvard.edu/tools/spring.html

Interactive web tool to visualize single cell data using force directed graph layouts. Kinetic interface for visualizing high dimensional single cell expression data. Collection of pre-processing scripts and web browser based tool for visualizing and interacting with high dimensional data.

Proper citation: SPRING (RRID:SCR_023578) Copy   

•   Source: SciCrunch Registry

http://www.hemonc.org

Medical wiki of interventions, regimens, and general information relevant to fields of hematology and oncology. Knowledge base for hematology and oncology providers, containing details about hematology/oncology drugs and treatment regimens. Any healthcare professional can sign up to contribute. Acuracy and completeness of content is overseen by Editorial Board.

Proper citation: HemOnc Knowledgebase (RRID:SCR_023436) Copy   

•   Source: SciCrunch Registry

https://het.io/search/

Web app that allows users to search for the most important paths connecting any two nodes in Hetionet.

Proper citation: Hetnet Connectivity Search (RRID:SCR_023630) Copy   

•   Source: SciCrunch Registry

https://maayanlab.cloud/kea3

Web server application that infers overrepresentation of upstream kinases whose putative substrates are in user inputted list of proteins. Used to analyze data from phosphoproteomics and proteomics studies to predict upstream kinases responsible for observed differential phosphorylations.

Proper citation: Kinase Enrichment Analysis 3 (RRID:SCR_023623) Copy   

•   Source: SciCrunch Registry

https://generanger.maayanlab.cloud/gene/A2M?database=ARCHS4

Web server application that provides access to processed data about expression of human genes and proteins across human cell types, tissues, and cell lines from several atlases. Used to explore single gene expression across tissues and cell types.

Proper citation: GeneRanger (RRID:SCR_023622) Copy   

•   Source: SciCrunch Registry

https://targetranger.maayanlab.cloud/

Web server application that identifies targets from user inputted RNA-seq samples collected from cells we wish to target. By comparing inputted samples with processed RNA-seq and proteomics data from several atlases, TargetRanger identifies genes that are highly expressed in target cells while lowly expressed across normal human cell types, tissues, and cell lines.

Proper citation: TargetRanger (RRID:SCR_023621) 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://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://statistika.mfub.bg.ac.rs/interactive-linegraph/

Interactive web based tool for creating line graphs for scientific publications. Users can view different summary statistics, examine lines for any individual in data, focus on time points or groups of interest, and view changes between any two time points and conditions.

Proper citation: Interactive Line Graph (RRID:SCR_018334) Copy   

•   Source: SciCrunch Registry

https://cadd.gs.washington.edu/

Web tool for predicting deleteriousness of variants throughout human genome. Software tool for scoring deleteriousness of single nucleotide variants as well as insertion and deletions variants in human genome.

Proper citation: Combined Annotation Dependent Depletion (RRID:SCR_018393) Copy   

•   Source: SciCrunch Registry