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https://www.delaneycare.org/index.php

The Collaboratory of AIDS Researchers for Eradication (CARE) is a consortium of scientific experts in the field of HIV latency from several U.S. and European academic research institutions as well as Merck Research Laboratories working together to find a cure for HIV.

Proper citation: Collaboratory of AIDS Researchers for Eradciation (CARE) (RRID:SCR_013681) Copy   

•   Source: SciCrunch Registry

https://www.itntrialshare.org/

Immune tolerance data management and visualization portal for studies sponsored by Immune Tolerance Network (ITN) and collaborating investigators. Data from published studies are accessible to any user; data from current in-progress studies are accessible to study investigators and collaborators. Includes links to published Figures, tools for visualization and analysis of data, and ability to query study data by subject, group, or any other study parameter.

Proper citation: Immune Tolerance Network TrialShare (RRID:SCR_013699) Copy   

•   Source: SciCrunch Registry

https://vdjserver.org/

A web application immune repertoire management, analysis, and archiving. Users can collaborate and share data either privately or publicly. Users can perform a variety of tasks, such as create and share projects with other users, conduct pre-processing tasks on single end reads, run IgBlast, and obtain basic repertoire characterization results for B cell receptor and T cell receptor repertoires.

Proper citation: VDJ Server (RRID:SCR_014356) Copy   

•   Source: SciCrunch Registry

http://www.patricbrc.org/portal/portal/patric/Home

A Bioinformatics Resource Center bacterial bioinformatics database and analysis resource that provides researchers with an online resource that stores and integrates a variety of data types (e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data) and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. The PATRIC project includes three primary collaborators: the University of Chicago, the University of Manchester, and New City Media. The University of Chicago is providing genome annotations and a PATRIC end-user genome annotation service using their Rapid Annotation using Subsystem Technology (RAST) system. The National Centre for Text Mining (NaCTeM) at the University of Manchester is providing literature-based text mining capability and service. New City Media is providing assistance in website interface development. An FTP server and download tool are available.

Proper citation: Pathosystems Resource Integration Center (RRID:SCR_004154) Copy   

•   Source: SciCrunch Registry

http://hcv.lanl.gov/

The Hepatitis C Virus (HCV) Database Project strives to present HCV-associated genetic and immunologic data in a user-friendly way, by providing access to the central database via web-accessible search interfaces and supplying a number of analysis tools.

Proper citation: HCV Databases (RRID:SCR_002863) Copy   

•   Source: SciCrunch Registry

https://pave.niaid.nih.gov

Collection of curated papillomavirus genomic sequences, accompanied by web-based sequence analysis tools. Database and web applications support the storage, annotation, analysis, and exchange of information.

Proper citation: PaVE (RRID:SCR_016599) Copy   

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https://bioinformatics.niaid.nih.gov/hasp

Web server to visualize phylogenetic, biochemical, and immunological hemagglutinin data in the three-dimensional context of homology models. Database and structural visualization platform for comparative models of influenza A hemagglutinin proteins.

Proper citation: HASP (RRID:SCR_016615) 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://csgid.org/csgid/metal_sites

Metal binding site validation server. Used for systematic inspection of the metal-binding architectures in macromolecular structures. The validation parameters that CMM examines cover the entire binding environment of the metal ion, including the position, charge and type of atoms and residues surrounding the metal.

Proper citation: CheckMyMetal (RRID:SCR_016887) 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.immunexpresso.org

Software tool as text-mining engine that structures and standardizes knowledge of immune intercellular communication. Knowledgebase contains interactions and separate mentions of cells or cytokines in context of thousands of diseases. Intercellular interactions were text-mined from all available PubMed abstracts across disease conditions.

Proper citation: immuneXpresso (RRID:SCR_017578) Copy   

•   Source: SciCrunch Registry

https://dice-database.org

Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics. Collection of identified cis-eQTLs for 12,254 unique genes, which represent 61% of all protein-coding genes expressed in human cell types. Datasets to help reveal effects of disease risk associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis.

Proper citation: Database of Immune Cell Epigenomes (RRID:SCR_018259) Copy   

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https://outbreak.info/

Resource to aggregate all outbreak information into single location during outbreaks of emerging diseases, such as COVID-19.

Proper citation: outbreak.info (RRID:SCR_018282) Copy   

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https://sourceforge.net/projects/timezone1/

Software package to detect footprints of positive selection for functionally adaptive point mutations in microbial genomes.

Proper citation: TimeZone (RRID:SCR_018564) 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

http://tools.dice-database.org/GOnet/)

Web tool for interactive Gene Ontology analysis of any biological data sources resulting in gene or protein lists.

Proper citation: GOnet (RRID:SCR_018977) Copy   

•   Source: SciCrunch Registry

http://www.nmpdr.org/FIG/wiki/view.cgi

The National Microbial Pathogen Data Resource provides curated annotations in an environment for comparative analysis of genomes and biological subsystems, with an emphasis on the food-borne pathogens Campylobacter, Listeria, Staphylococcus, Streptococcus, and Vibrio; as well as the STD pathogens Chlamydiaceae, Haemophilus, Mycoplasma, Neisseria, Treponema, and Ureaplasma. This edition of the NMPDR includes 47 archaeal, 725 bacterial, and 29 eukaryal genomes with 3,257,100 genetic features, of which 1,338,895 are in FIGfams curated using 616 active subsystems. ''''''Notice to NMPDR Users'''''' - The NMPDR BRC contract ended in December 2009. At that time we ceased maintenance of the NMPDR web resource and data. Bacterial data from NMPDR has been transferred to PATRIC (http://www.patricbrc.org), a new consolidated BRC for all NIAID category A-C priority pathogenic bacteria. NMPDR was a collaboration among researchers from the Computation Institute of the University of Chicago, the Fellowship for Interpretation of Genomes (FIG), Argonne National Laboratory, and the National Center for Supercomputing Applications (NCSA) at the University of Illinois.

Proper citation: NMPDR (RRID:SCR_007821) Copy   

•   Source: SciCrunch Registry

http://patricbrc.vbi.vt.edu/portal/portal/patric/IncumbentBRCs?page=eric

ERIC is a resource of annotated enterobacterial genomes. Information is available and accessed through a open web portal uniting biological data and analysis tools. ERIC contains information on Escherichia, Shigella, Salmonella, Yersinia, and other microorgansims. ERIC has recently been moved over to PATRIC: The PATRIC BRC is now responsible for all bacterial species in the NIAID Category A-C Priority Pathogen lists for biodefense research, and pathogens causing emerging/reemerging infectious diseases. For ERIC users, we understand that the resource was valuable to your work. As such, we will be doing our very best to create a useful PATRIC resource to continue supporting your work. We realize that the transition will cause disruptions. However, it is a priority for us to work with established BRC users and communities to identify and prioritize our transition efforts. We have concentrated on the transfer of genomic data for this initial release. We anticipate adding new data, tools, and website features over the next several months. We look forward to working with you during the next 5 years., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ERIC (RRID:SCR_007644) Copy   

•   Source: SciCrunch Registry

http://www.macaque.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on June 8, 2020.Macaque genomic and proteomic resources and how they are providing important new dimensions to research using macaque models of infectious disease. The research encompasses a number of viruses that pose global threats to human health, including influenza, HIV, and SARS-associated coronavirus. By combining macaque infection models with gene expression and protein abundance profiling, they are uncovering exciting new insights into the multitude of molecular and cellular events that occur in response to virus infection. A better understanding of these events may provide the basis for innovative antiviral therapies and improvements to vaccine development strategies.

Proper citation: Macaque.org (RRID:SCR_002767) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry