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http://www.cidr.jhmi.edu/

Next generation sequencing and genotyping services provided to investigators working to discover genes that contribute to disease. On-site statistical geneticists provide insight into analysis issues as they relate to study design, data production and quality control. In addition, CIDR has a consulting agreement with the University of Washington Genetics Coordinating Center (GCC) to provide statistical and analytical support, most predominantly in the areas of GWAS data cleaning and methods development. Completed studies encompass over 175 phenotypes across 530 projects and 620,000 samples. The impact is evidenced by over 380 peer-reviewed papers published in 100 journals. Three pathways exist to access the CIDR genotyping facility: * NIH CIDR Program: The CIDR contract is funded by 14 NIH Institutes and provides genotyping and statistical genetic services to investigators approved for access through competitive peer review. An application is required for projects supported by the NIH CIDR Program. * The HTS Facility: The High Throughput Sequencing Facility, part of the Johns Hopkins Genetic Resources Core Facility, provides next generation sequencing services to internal JHU investigators and external scientists on a fee-for-service basis. * The JHU SNP Center: The SNP Center, part of the Johns Hopkins Genetic Resources Core Facility, provides genotyping to internal JHU investigators and external scientists on a fee-for-service basis. Data computation service is included to cover the statistical genetics services provided for investigators seeking to identify genes that contribute to human disease. Human Genotyping Services include SNP Genome Wide Association Studies, SNP Linkage Scans, Custom SNP Studies, Cancer Panel, MHC Panels, and Methylation Profiling. Mouse Genotyping Services include SNP Scans and Custom SNP Studies.

Proper citation: Center for Inherited Disease Research (RRID:SCR_007339) Copy   

•   Source: SciCrunch Registry

http://www.nia.nih.gov/research/dab/aged-rodent-tissue-bank-handbook/tissue-arrays

Offer high-throughput analysis of tissue histology and protein expression for the biogerontology research community. Each array is a 4 micron section that includes tissue cores from multiple tissues at multiple ages on one slide. The arrays are made from ethanol-fixed tissue and can be used for all techniques for which conventional tissue sections can be used. Ages are chosen to span the life from young adult to very old age. (available ages: 4, 12, 18, 24 and 28 months of age) Images of H&E stained punches are available for Liver, Cardiac Muscle, and Brain. The NIA aged rodent tissue arrays were developed with assistance from the National Cancer Institute (NCI) Tissue Array Research Program (TARP), led by Dr. Stephen Hewitt, Director. NCI TARP contains more information on tissue array construction, protocols for using arrays, and references. Preparation and Product Description Tissue arrays are prepared in parallel from different sets of animals so that experiments can be conducted in duplicate, with each array using unique animals with a unique product number. The product descriptions page describes each array, including: * Strain * Gender * Ages * Tissues * Animal Identification Numbers

Proper citation: Aged Rodent Tissue Arrays (RRID:SCR_007332) Copy   

•   Source: SciCrunch Registry

http://slicer.org/

A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X. Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.

Proper citation: 3D Slicer (RRID:SCR_005619) Copy   

•   Source: SciCrunch Registry

http://jjwanglab.org:8080/gwasdb/

Combines collections of genetic variants (GVs) from GWAS and their comprehensive functional annotations, as well as disease classifications. Used to maximize utilility of GWAS data to gain biological insights through integrative, multi-dimensional functional annotation portal. In addition to all GVs annotated in NHGRI GWAS Catalog, we manually curate GVs that are marginally significant (P value < 10-3) by looking into supplementary materials of each original publication and provide extensive functional annotations for these GVs. GVs are manually classified by diseases according to Disease Ontology Lite and HPO (Human Phenotype Ontology) for easy access. Database can also conduct gene based pathway enrichment and PPI network association analysis for those diseases with sufficient variants. SOAP services are available. You may Download GWASdb SNP. (This file contains all of the significant SNP in GWASdb. In the pvalue column, 0 means this P-value is not reported in the study but it is significant SNP. In the source column, GWAS:A represents the original data in GWAS catalog, while GWAS:B is our curation data which P-value < 10-3)

Proper citation: GWASdb (RRID:SCR_006015) Copy   

•   Source: SciCrunch Registry

https://proteomecommons.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A public resource for sharing general proteomics information including data (Tranche repository), tools, and news. Joining or creating a group/project provides tools and standards for collaboration, project management, data annotation, permissions, permanent storage, and publication.

Proper citation: Proteome Commons (RRID:SCR_006234) Copy   

•   Source: SciCrunch Registry

http://homer.ucsd.edu/

Software tools for Motif Discovery and next-gen sequencing analysis. Used for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets. Collection of command line programs for unix style operating systems written in Perl and C++.

Proper citation: HOMER (RRID:SCR_010881) Copy   

•   Source: SciCrunch Registry

http://www.xiphophorus.txstate.edu/

Supplier of xiphophorus (platyfish or swordtails) from pedigreed parental lines, representing variety of species. In addition to supplying strains and providing consultation on husbandry and genetic questions, the XGSC produces custom interspecies hybrids (both first generation F1, and backcross hybrid generation BC1) for a variety of projects.

Proper citation: Xiphophorus Genetic Stock Center (RRID:SCR_008340) Copy   

•   Source: SciCrunch Registry

http://itools.loni.usc.edu/

An infrastructure for managing of diverse computational biology resources - data, software tools and web-services. The iTools design, implementation and meta-data content reflect the broad NCBC needs and expertise (www.NCBCs.org).

Proper citation: iTools (RRID:SCR_009626) Copy   

•   Source: SciCrunch Registry

http://discover.nci.nih.gov/gominer/GoCommandWebInterface.jsp

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A web program that organizes lists of genes of interest (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology and automates the analysis of multiple microarrays then integrates the results across all of them in exportable output files and visualizations. High-Throughput GoMiner is an enhancement of GoMiner and is implemented with both a command line interface and a web interface. The program can also: efficiently perform automated batch processing of an arbitrary number of microarrays; produce a human- or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories; integrate the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories; provide a fast form of false discovery rate multiple comparisons calculation; and provide annotations and visualizations for relating transcription factor binding sites to genes and GO categories.

Proper citation: High-Throughput GoMiner (RRID:SCR_000173) Copy   

•   Source: SciCrunch Registry

http://www.pbtc.org/

The PEDIATRIC BRAIN TUMOR CONSORTIUM (PBTC) is a multidisciplinary cooperative research organization devoted to the study of correlative tumor biology and new therapies for primary CNS tumors of childhood. PBTC's mission is to contribute rapidly and effectively to the understanding and cure of these tumors through the conduct of multi-center, multidisciplinary, innovative studies with designs and analyses based on uniformly high quality statistical science. While the primary mission of the PBTC is to identify through laboratory and clinical science superior treatment strategies for children with brain cancers, the PBTC investigators recognize their profound responsibility to meet the special needs of the children and families as they face this enormous challenge. Members are committed to working within their institutions and communities to improve support services and follow up care for these patients and their families. The PBTC's primary objective is to rapidly conduct novel phase I and II clinical evaluations of new therapeutic drugs, new biological therapies, treatment delivery technologies and radiation treatment strategies in children from infancy to 21 years of age with primary central nervous system (CNS) tumors. A second objective is to characterize reliable markers and predictors (direct or surrogate) of brain tumors' responses to new therapies. The Consortium conducts research on brain tumor specimens in the laboratory to further understand the biology of pediatric brain tumors. A third objective is to develop and coordinate innovative neuro-imaging techniques. Through the PBTC's Neuro-Imaging Center, formed in May 2000, research to evaluate new treatment response criteria and neuro-imaging methods to understand regional brain effects is in progress. These imaging techniques can also advance understanding of significant neuro-toxicity in a developing child's central nervous system. The Neuro-Imaging Center is supported in part by private sources - grants from foundations and non-profit organizations - in addition to the NCI. As an NCI funded Consortium, the Pediatric Brain Tumor Consortium (PBTC) is required to make research data available to other investigators for use in research projects. An investigator who wishes to use individual patient data from one or more of the Consortium's completed and published studies must submit in writing a description of the research project, the PBTC studies from which data are requested, the specific data requested, and a list of investigators involved with the project and their affiliated research institutions. A copy of the requesting investigator's CV must also be provided. Participating Institutions: Children's Hospital of Philadelphia, Children's National Medical Center (Washington, DC), Children's Memorial Hospital (Chicago), Duke University, National Cancer Institute, St. Jude Children's Research Hospital, Texas Children's Cancer Center, University of California at San Francisco, and University of Pittsburgh.

Proper citation: Pediatric Brain Tumor Consortium (RRID:SCR_000658) Copy   

•   Source: SciCrunch Registry

http://www.px.nsls.bnl.gov/

Biomedical technology research center that creates optimal facilities and environments and support for macromolecular structure determination by synchrotron X-ray diffraction at the National Synchrotron Light Source for the benefit of outside and in-house investigators. The PXRR innovates new access modes such as Mail-in crystallography, builds new facilities, currently on the X25 undulator, advances automation, develops remote participation software, collaborates with outside groups, teaches novice users, and supports vising investigators with 7-day, 20-hours staff coverage.

Proper citation: Macromolecular Crystallography Research Resource (RRID:SCR_001442) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/LJP/

Interactive on line tool where signatures are tagged with user selected metadata and external transcript signatures are projected onto network. Browser to visualize signatures from breast cancer cell lines treated with single molecule perturbations.

Proper citation: LINCS Joint Project - Breast Cancer Network Browser (RRID:SCR_016181) Copy   

•   Source: SciCrunch Registry

https://github.com/jbelyeu/SV-plaudit

Software for rapidly curating structural variant (SVs) predictions. SV-plaudit provides a pipeline for creating image views of genomic intervals, automatically storing them in the cloud, deploying a website to view/score them, and retrieving scores for analysis.

Proper citation: SV-plaudit (RRID:SCR_016285) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/DGB/

Web based application to assist researchers with identifying drugs and small molecules that are predicted to maximally influence expression of mammalian gene of interest. Used to identify drugs and small molecules to regulate expression of target genes for research purpose only. Application for ranking drugs to modulate specific gene based on transcriptomic signatures.

Proper citation: Drug Gene Budger (RRID:SCR_016489) Copy   

•   Source: SciCrunch Registry

https://github.com/dpeerlab/phenograph

Software tool as clustering method designed for high dimensional single cell data. Algorithmically defines phenotypes in high dimensional single cell data. Used for large scale analysis of single cell heterogeneity.

Proper citation: Phenograph (RRID:SCR_016919) Copy   

•   Source: SciCrunch Registry

https://immunedb.readthedocs.io/en/latest/

Software system for storing and analyzing high throughput B and T cell immune receptor sequencing data. Comprised of web interface and of Python analysis tools to process raw reads for gene usage, infer clones, aggregate data, and run downstream analyses, or in conjunction with other AIRR tools using its import and export features.

Proper citation: ImmuneDB (RRID:SCR_017125) Copy   

•   Source: SciCrunch Registry

http://www.genesigdb.org

Database of traceable, standardized, annotated gene signatures which have been manually curated from publications that are indexed in PubMed. The Advanced Gene Search will perform a One-tailed Fisher Exact Test (which is equivalent to Hypergeometric Distribution) to test if your gene list is over-represented in any gene signature in GeneSigDB. Gene expression studies typically result in a list of genes (gene signature) which reflect the many biological pathways that are concurrently active. We have created a Gene Signature Data Base (GeneSigDB) of published gene expression signatures or gene sets which we have manually extracted from published literature. GeneSigDB was creating following a thorough search of PubMed using defined set of cancer gene signature search terms. We would be delighted to accept or update your gene signature. Please fill out the form as best you can. We will contact you when we get it and will be happy to work with you to ensure we accurately report your signature. GeneSigDB is capable of providing its functionality through a Java RESTful web service.

Proper citation: GeneSigDB (RRID:SCR_013275) Copy   

•   Source: SciCrunch Registry

http://www.genepattern-notebook.org/

Interactive analysis notebook environment that streamlines genomics research by interleaving text, multimedia, and executable code into unified, sharable, reproducible “research narratives.” It integrates the dynamic capabilities of notebook systems with an investigator-focused, simple interface that provides access to hundreds of genomic tools without the need to write code.

Proper citation: GenePattern Notebook (RRID:SCR_015699) Copy   

•   Source: SciCrunch Registry

http://tissues.jensenlab.org

Database that integrates evidence on tissue expression from manually curated literature, proteomics and transcriptomics screens, and automatic text mining. It maps all evidence to common protein identifiers and Brenda Tissue Ontology terms, and further unifies it by assigning confidence scores that facilitate comparison of the different types and sources of evidence.

Proper citation: TISSUES (RRID:SCR_015665) Copy   

•   Source: SciCrunch Registry

https://cabig.nci.nih.gov/tools/caTRIP

THIS RESOURCE IS NO LONGER IN SERVICE documented June 4, 2013. Allows users to query across a number of caBIG data services, join on common data elements (CDEs), and view results in a user-friendly interface. With an initial focus on enabling outcomes analysis, caTRIP allows clinicians to query across data from existing patients with similar characteristics to find treatments that were administered with success. In doing so, caTRIP can help inform treatment and improve patient care, as well as enable the searching of available tumor tissue, enable locating patients for clinical trials, and enable investigating the association between multiple predictors and their corresponding outcomes such as survival caTRIP relies on the vast array of open source caBIG applications, including: * Tumor Registry, a clinical system that is used to collect endpoint data * cancer Text Information Extraction System (caTIES), a locator of tissue resources that works via the extraction of clinical information from free text surgical pathology reports. while using controlled terminologies to populate caBIG-compliant data structures * caTissue CORE, a tissue bank repository tool for biospecimen inventory, tracking, and basic annotation * Cancer Annotation Engine (CAE), a system for storing and searching pathology annotations * caIntegrator, a tool for storing, querying, and analyzing translational data, including SNP data Requires Java installation and network connectivity.

Proper citation: caTRIP (RRID:SCR_003409) Copy   

•   Source: SciCrunch Registry