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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
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
A SEED-quality automated service that annotates complete or nearly complete bacterial and archaeal genomes across the entire phylogenetic tree. RAST can also be used to analyze draft genomes.
Proper citation: RAST Server (RRID:SCR_014606) Copy
https://github.com/taborlab/FlowCal
Open source software tool for automatically converting flow cytometry data from arbitrary to calibrated units. Can be run using intuitive Microsoft Excel interface, or customizable Python scripts. Software accepts Flow Cytometry Standard (FCS) files as inputs and is compatible with different calibration particles, fluorescent probes, and cell types. Automatically gates data, calculates common statistics, and produces plots.
Proper citation: FlowCal (RRID:SCR_018140) Copy
https://evidencemodeler.github.io/
Software tool for automated eukaryotic gene structure annotation that reports eukaryotic gene structures as weighted consensus of all available evidence. Used to combine ab intio gene predictions and protein and transcript alignments into weighted consensus gene structures. Inputs include genome sequence, gene predictions, and alignment data (in GFF3 format).
Proper citation: EVidenceModeler (RRID:SCR_014659) Copy
https://www.niaid.nih.gov/diseases-conditions/coronaviruses
Information about coronaviruses, including COVID-19. NIAID provides research funding and resources for scientific community to facilitate development of vaccines, therapeutics, and diagnostics for infectious diseases, including those caused by coronaviruses.
Proper citation: NIAID Overview of Coronaviruses (RRID:SCR_018290) Copy
https://gitlab.com/gernerlab/cytomap/-/wikis/home
Software tool as spatial analysis software for whole tissue sections.Utilizes information on cell type and position to phenotype local neighborhoods and reveal how their spatial distribution leads to generation of global tissue architecture.Used to make advanced data analytic techniques accessible for single cell data with position information.
Proper citation: CytoMAP (RRID:SCR_021227) Copy
https://github.com/zdk123/SpiecEasi
Software R package for microbiome network analysis. Used for inference of microbial ecological networks from amplicon sequencing datasets. Combines data transformations developed for compositional data analysis with graphical model inference framework that assumes underlying ecological association network is sparse.
Proper citation: SpiecEasi (RRID:SCR_022712) Copy
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
Develops information technologies that make authoring complete metadata more manageable. Its products aim to facilitate using the metadata in further research.Center to improve metadata and its use throughout biomedical sciences. Develops information technologies that make authoring complete metadata more manageable through better interfaces, terminology, metadata practices, and analytics. Optimizes metadata pathway from provider to end user. Provides way for funders to specify what metadata they want to collect as part of research life cycle.
Proper citation: Center for Expanded Data Annotation and Retrieval (RRID:SCR_016269) Copy
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
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
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
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
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
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
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
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
Project portal for a cooperative research program to improve short and long-term graft and patient survival. CTOT is an investigative consortium for conducting clinical and associated mechanistic studies that will lead to improved outcomes for transplant recipients.
Proper citation: Clinical Trials in Organ Transplantation (CTOT) (RRID:SCR_015859) Copy
Project portal for a cooperative research program sponsored by the National Institute of Allergy and Infectious Diseases (NIAID). CTOT-C is an investigative consortium for conducting clinical and associated mechanistic studies that will lead to improved outcomes for pediatric heart, lung, or kidney transplant recipients.
Proper citation: Clinical Trials in Organ Transplantation in Children (CTOT-C) (RRID:SCR_015860) Copy
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