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https://exrna-atlas.org

Software tool as data and metadata repository of Extracellular RNA Communication Consortium. Atlas includes small RNA sequencing and qPCR derived exRNA profiles from human and mouse biofluids. All RNAseq datasets are processed using version 4 of exceRpt small RNAseq pipeline. Atlas accepts submissions for RNAseq or qPCR data.

Proper citation: exRNA Atlas (RRID:SCR_017221) Copy   

•   Source: SciCrunch Registry

https://www.epimodel.org/

Software R package for mathematical modeling of infectious disease over networks. Provides tools for simulating and analyzing mathematical models of infectious disease dynamics. Mathematical Modeling of Infectious Disease Dynamics.

Proper citation: EpiModel (RRID:SCR_018539) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/omicsGAN

Software generative adversarial network to integrate two omics data and their interaction network to generate one synthetic data corresponding to each omics profile that can result in better phenotype prediction. Used to capture information from interaction network as well as two omics datasets and fuse them to generate synthetic data with better predictive signals.

Proper citation: OmicsGAN (RRID:SCR_022976) Copy   

•   Source: SciCrunch Registry

http://www.rhesusbase.org/drugDisc/CAM.jsp

OKCAM (Ontology-based Knowledgebase for Cell Adhesion Molecules) is an online resource for human genes known or predicted to be related to the processes of cell adhesion. These genes include members of the cadherin, immunoglobulin/FibronectinIII (IgFn), integrin, neurexin, neuroligin, and catenin families. Totally 496 human CAM genes were compiled and annotated. We have mapped these genes onto a novel cell adhesion molecule ontology (CAMO) that provides a hierarchical description of cell adhesion molecules and their functions. It is intended to provide a means to facilitate better and better understanding of the global and specific properties of CAMs through their genomic features, regulatory modes, expression patterns and disease associations become clearer. You may browse by CAM ontology, Chromosomes and Full Gene list.

Proper citation: OKCAM: Ontology-based Knowledgebase for Cell Adhesion Molecules (RRID:SCR_010696) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/saga.html

SAGA (Substructure Index-based Approximate Graph Alignment) is a tool for querying a biological graph database to retrieve matches between subgraphs of molecular interactions and biological networks. SAGA implements an efficient approximate subgraph matching algorithm that can be used for a variety of biological graph matching problems such as the pathway matching SAGA uses to compare pathways in KEGG and Reactome. You can also use SAGA to find matches in literature databases that have been parsed into semantic graphs. In this use of SAGA, portions of PubMed have been parsed into graphs that have nodes representing gene names. A link is drawn between two genes if they are discussed in the same sentence (indicating there is potential association between the two genes). SAGA lets you match graphs between different databases even though the content is distinct and the databases organize pathways in different ways. This cross-database matching is achieved by SAGA's flexible approximate subgraph matching model that computes graph similarity, and allows for node gaps, node mismatches, and graph structural differences. Comparing pathways from different databases can be a useful precursor to pathway data integration. SAGA is very efficient for querying relatively small graphs, but becomes prohibitory expensive for querying large graphs. Large graph data sets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of information encoded in graphs, effective and efficient graph matching tools are critical. Due to the noisy and incomplete nature of real graph datasets, approximate, rather than exact, graph matching is required. Furthermore, many modern applications need to query large graphs, each of which has hundreds to thousands of nodes and edges. TALE is an approximate subgraph matching tool for matching graph queries with a large number of nodes and edges. TALE employs a novel indexing technique that achieves a high pruning power and scales linearly with the database size.

Proper citation: Substructure Index-based Approximate Graph Alignment (RRID:SCR_003434) Copy   

•   Source: SciCrunch Registry

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

Software Python package for performing gene set enrichment analysis. Used for characterizing gene expression changes by analysis of large single-cell datasets.

Proper citation: GSEApy (RRID:SCR_025803) Copy   

•   Source: SciCrunch Registry

https://painseq.shinyapps.io/harmonized_painseq_v1/

Harmonized cell atlases using sc/snRNA-seq data obtained from dorsal root ganglia and trigeminal ganglio mammalian datasets.

Proper citation: Harmonized DRG and TG Reference Atlas (RRID:SCR_025720) Copy   

•   Source: SciCrunch Registry

https://github.com/smorabit/hdWGCNA

Software R package for performing weighted gene co-expression network analysis in high dimensional transcriptomics data such as single-cell RNA-seq or spatial transcriptomics.

Proper citation: hdWGCNA (RRID:SCR_027496) Copy   

•   Source: SciCrunch Registry

http://courses.jax.org/2012/addiction.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This course emphasizes genetic applications and approaches to drug addiction research through methodological instruction based on literature, data sets and informatics resources drawn from studies of addiction related phenotypes. The course includes plenary sessions on major progress in addiction genetics, and discussion sessions in which students present their work for discussion on applications of genetic methods. Students will leave the course able to design and interpret genetic and genomic studies of addiction as they relate to their specific research question, and will be able to make use of current bioinformatics resources to identify research resources and make use of public data sources in their own research.

Proper citation: Short Course on the Genetics of Addiction (RRID:SCR_005560) Copy   

•   Source: SciCrunch Registry

http://www.exocarta.org/

Manually curated database of exosomal proteins, RNA and lipids. Web based compendium of exosomal cargo. Database catalogs information from both published and unpublished exosomal studies. Mode of exosomal purification and characterization, biophysical and molecular properties are listed.

Proper citation: ExoCarta (RRID:SCR_021960) Copy   

•   Source: SciCrunch Registry

http://biositemaps.ncbcs.org/rds/search.html

Resource Discovery System is a web-accessible and searchable inventory of biomedical research resources. Powered by the Resource Discovery System (RDS) that includes a standards-based informatics infrastructure * Biositemaps Information Model * Biomedical Resource Ontology Extensions * Web Services distributed web-accessible inventory framework * Biositemap Resource Editor * Resource Discovery System Source code and project documentation to be made available on an open-source basis. Contributing institutions: University of Pittsburgh, University of Michigan, Stanford University, Oregon Health & Science University, University of Texas Houston. Duke University, Emory University, University of California Davis, University of California San Diego, National Institutes of Health, Inventory Resources Working Group Members

Proper citation: Resource Discovery System (RRID:SCR_005554) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-7/search?q=*

Virtual database currently indexing interaction between genes and diseases from Online Mendelian Inheritance in Man (OMIM) and Comparative Toxicogenomics Database (CTD).

Proper citation: Integrated Gene-Disease Interaction (RRID:SCR_006173) Copy   

•   Source: SciCrunch Registry

http://www.cpc.unc.edu/projects/addhealth

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/DRG

Gene expression data from published journal articles that test hypotheses relevant to neuroscience of addiction and addictive behavior. Data types include effects of particular drug, strain, or knock out on particular gene, in particular anatomical region. Focuses on gene expression data and exposes data from investigations using DNA microarrays, polymerase chain reaction, immunohistochemistry and in-situ hybridizations. Data are available for query through NIF interface.Data submissions are welcome.

Proper citation: Drug Related Gene Database (RRID:SCR_003330) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/about/sources/nlx_144509-1

Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.

Proper citation: SciCrunch Registry (RRID:SCR_005400) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dti_rat_atlas/

3D DTI anatomical rat brain atlases have been created by the UNC- Chapel Hill Department of Psychiatry and the CAMID research collaboration. There are three age groups, postnatal day 5, postnatal day 14, and postnatal day 72. The subjects were Sprague-Dawley rats that were controls in a study on cocaine abuse and development. The P5 and P14 templates were made from scans of twenty rats each (ten female, ten male); the P72, from six females. The individual cases have been resampled to isotropic resolution, manually skull-stripped, and deformably registered via an unbiased atlas building method to create a template for each age group. Each template was then manually segmented using itk-SNAP software. Each atlas is made up of 3 files, a template image, a segmentation, and a label file.

Proper citation: 3D DTI Atlas of the Rat Brain In Postnatal Day 5 14 and Adulthood (RRID:SCR_009437) Copy   

•   Source: SciCrunch Registry

http://www.emouseatlas.org

Detailed multidimensional digital multimodal atlas of C57BL/6J mouse nervous system with data and informatics pipeline that can automatically register, annotate, and visualize large scale neuroanatomical and connectivity data produced in histology, neuronal tract tracing, MR imaging, and genetic labeling. MAP2.0 interoperates with commonly used publicly available databases to bring together brain architecture, gene expression, and imaging information into single, simple interface.Resource to visualise mouse development, identify anatomical structures, determine developmental stage, and investigate gene expression in mouse embryo. eMouseAtlas portal page allows access to EMA Anatomy Atlas of Mouse Development and EMAGE database of gene expression.EMAGE is freely available, curated database of gene expression patterns generated by in situ techniques in developing mouse embryo. EMA, e-Mouse Atlas, is 3-D anatomical atlas of mouse embryo development including histology and includes EMAP ontology of anatomical structure, provides information about shape, gross anatomy and detailed histological structure of mouse, and framework into which information about gene function can be mapped.

Proper citation: eMouseAtlas (RRID:SCR_002981) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

http://neuinfo.org

Framework for identifying, locating, relating, accessing, integrating, and analyzing information from neuroscience research. Users can search for and add neuroscience-related resources at NIF portal and receive and RRID to track and cite resources within scientific manuscripts.

Proper citation: Neuroscience Information Framework (RRID:SCR_002894) Copy   

•   Source: SciCrunch Registry

http://www.drugabuse.gov/about/organization/CEWG/

A network composed of researchers from major metropolitan areas of the United States and selected foreign countries which meet semiannually to discuss the current epidemiology of drug abuse. The primary mission of the Work Group is to provide ongoing community-level surveillance of drug abuse through analysis of quantitative and qualitative research data. Through this program the CEWG provides current descriptive and analytical information regarding the nature and patterns of drug abuse, emerging trends, characteristics of vulnerable populations and social and health consequences. Reports Reports are available from the biannual meetings at which the network members discuss current and emerging problems of substance abuse. At the meetings, CEWG members present data on drug abuse from a variety of city, State, Federal, and other sources. These data are enhanced with information gathered through ethnographic research, focus groups, interviews, and other qualitative methods. This integration of quantitative with qualitative data provides invaluable insight into emerging drug use trends. Book In 1998, the National Institute on Drug Abuse (NIDA) published the first edition of Assessing Drug Abuse Within and Across Communities: Community Epidemiology Surveillance Networks on Drug Abuse to share information on establishing drug abuse epidemiology networks at community and State levels. Its purpose is to provide guidelines for establishing epidemiology networks to monitor and assess drug abuse patterns and trends and emerging drug problems at community and State levels to provide a foundation of information for public health response. The second edition differs from the first in format. For each data source, there is a description of the source and database, followed by guidelines on how to access the data (including Web sites) and what to request, and examples of how the data have been used by epidemiology work groups or Federal agencies. NIDA hopes that this revised guide is helpful to agencies, organizations, and researchers that are involved in or wish to establish epidemiology networks in their communities or States.

Proper citation: Community Epidemiology Work Group (RRID:SCR_002751) Copy   

•   Source: SciCrunch Registry