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https://icite.od.nih.gov/covid19/search/

NIH comprehensive, curated source for publications related to COVID-19. Includes articles from PubMed and pre-prints from arXiv, medRxiv, bioRxiv, and ChemRxiv. Updated daily. NIH Office of Portfolio Analysis has developed this resource to explore and analyze set of advances in COVID‑19 research as they accumulate in real time, and complements efforts by NLM to aggregate full text documents broadly related to COVID-19 and other outbreaks, and articles on COVID‑19 specific to PubMed database.

Proper citation: NIH COVID-19 Portfolio (RRID:SCR_018295) Copy   

•   Source: SciCrunch Registry

https://datascience.nih.gov/covid-19-open-access-resources

COVID-19 open access data and computational resources provided by federal agencies, including NIH, public consortia, and private entities. Continuously updated as more information becomes available. These resources are being shared for scientific and public health interests, and content is responsibility of resource organizers.

Proper citation: Data and Computational Resources to Address COVID-19 (RRID:SCR_018274) Copy   

•   Source: SciCrunch Registry

http://glycam.org/

Web provides tools for modeling 3D structures of molecules and complexes containing carbohydrates including oligosaccharide conformation modeling and glycoprotein 3D structure modeling. Used to simplify prediction of three dimensional structures of carbohydrates and macromolecular structures involving carbohydrates.

Proper citation: GLYCAM-Web (RRID:SCR_018260) 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

http://compbio.soe.ucsc.edu/yeast_introns.html

Database of information about the spliceosomal introns of the yeast Saccharomyces cerevisiae. Listed are known spliceosomal introns in the yeast genome and the splice sites actually used are documented. Through the use of microarrays designed to monitor splicing, they are beginning to identify and analyze splice site context in terms of the nature and activities of the trans-acting factors that mediate splice site recognition. In version 3.0, expression data that relates to the efficiency of splicing relative to other processes in strains of yeast lacking nonessential splicing factors is included. These data are displayed on each intron page for browsing and can be downloaded for other types of analysis.

Proper citation: Yeast Intron Database (RRID:SCR_007144) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/research-and-faculty/resources/knockout-mouse-project/high-throughput-production

Project is providing critical tools for understanding gene function and genetic causes of human diseases. Project KOMP is focused on generating targeted knockout mutations in mouse ES cells. Second phase, KOMP2, relies upon successful generation of strains of knockout mice from these ES cells. Information from JAX about their contributions to KOMP project.

Proper citation: Knockout Mouse Project (RRID:SCR_005571) Copy   

•   Source: SciCrunch Registry

http://www.nihclinicalcollection.com

A plated array of approximately 450 small molecules that have a history of use in human clinical trials. The collection was assembled by the National Institutes of Health (NIH) through the Molecular Libraries Roadmap Initiative as part of its mission to enable the use of compound screens in biomedical research. Similar collections of FDA approved drugs have proven to be rich sources of undiscovered bioactivity and therapeutic potential. The clinically tested compounds in the NCC are highly drug-like with known safety profiles. These compounds can provide excellent starting points for medicinal chemistry optimization and, for high-affinity targets, may even be appropriate for direct human use in new disease areas.

Proper citation: NIH Clinical Collection (RRID:SCR_007349) Copy   

•   Source: SciCrunch Registry

https://commonfund.nih.gov/hmp/

NIH Project to generate resources to characterize the human microbiota and to analyze its role in human health and disease at several different sites on the human body, including nasal passages, oral cavities, skin, gastrointestinal tract, and urogenital tract using metagenomic and traditional approach to genomic DNA sequencing studies.HMP was supported by the Common Fund from 2007 to 2016.

Proper citation: Human Microbiome Project (RRID:SCR_012956) Copy   

•   Source: SciCrunch Registry

https://github.com/YuanXue1993/SegAN

Image analysis software for medical image segmentation. The software is fueled by an end-to-end adversarial neural network that generates segmentation label maps.

Proper citation: SegAN (RRID:SCR_016215) Copy   

•   Source: SciCrunch Registry

http://nhprtr.org/

Nonhuman Primate reference transcriptome resource consisting of deep sequencing complete transcriptomes (RNA-seq) from multiple NHP species.

Proper citation: Nonhuman Primate Reference Transcriptome Resource (RRID:SCR_017534) Copy   

•   Source: SciCrunch Registry

https://cic.ini.usc.edu/

Project to create complete mesoscale connectivity atlas of the C57Black/6 mouse brain and to subsequently generate its global neural networks.

Proper citation: Mouse Connectome Project (RRID:SCR_017313) Copy   

•   Source: SciCrunch Registry

http://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html

A listing of NIH supported data sharing repositories that make data accessible for reuse. Most accept submissions of appropriate data from NIH-funded investigators (and others), but some restrict data submission to only those researchers involved in a specific research network. Also included are resources that aggregate information about biomedical data and information sharing systems. The table can be sorted according by name and by NIH Institute or Center and may be searched using keywords so that you can find repositories more relevant to your data. Links are provided to information about submitting data to and accessing data from the listed repositories. Additional information about the repositories and points-of-contact for further information or inquiries can be found on the websites of the individual repositories.

Proper citation: NIH Data Sharing Repositories (RRID:SCR_003551) Copy   

•   Source: SciCrunch Registry

http://dockground.bioinformatics.ku.edu/

Data sets, tools and computational techniques for modeling of protein interactions, including docking benchmarks, docking decoys and docking templates. Adequate computational techniques for modeling of protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The first release of the DOCKGROUND resource (Douguet et al., Bioinformatics 2006; 22:2612-2618) implemented a comprehensive database of cocrystallized (bound) protein-protein complexes in a relational database of annotated structures. Additional releases added features to the set of bound structures, such as regularly updated downloadable datasets: automatically generated nonredundant set, built according to most common criteria, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Also included are unbound (experimental and simulated) protein-protein complexes. Complexes from the bound dataset are used to identify crystallized unbound analogs. If such analogs do not exist, the unbound structures are simulated by rotamer library optimization. Thus, the database contains comprehensive sets of complexes suitable for large scale benchmarking of docking algorithms. Advanced methodologies for simulating unbound conformations are being explored for the next release. The Dockground project is developed by the Vakser lab at the Center for Bioinformatics at the University of Kansas. Parts of Dockground were co-developed by Dominique Douguet from the Center of Structural Biochemistry (INSERM U554 - CNRS UMR5048), Montpellier, France.

Proper citation: Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING (RRID:SCR_007412) Copy   

•   Source: SciCrunch Registry

https://nationalmaglab.org/user-facilities/icr

Facility provides service operations for sample analysis that requires ultrahigh resolution and high mass accuracy of Fourier Transform Ion Cyclotron Resonance. Used for research in biomolecular analysis, hydrogen-deuterium exchange and environmental and petrochemical analysis. Four FT-ICR mass spectrometers feature high magnetic fields including the world-record 21 tesla and are compatible with multiple ionization and fragmentation techniques.

Proper citation: National High Magnetic Field Laboratory Ion Cyclotron Resonance Core Facility (RRID:SCR_017361) Copy   

•   Source: SciCrunch Registry

https://nationalmaglab.org/user-facilities/emr/

EMR Facility offers home-built, high-frequency and high-field continuous-wave instruments providing frequency coverage from 9 GHz to 1 THz, with additional frequencies available up to 2.5 THz using molecular gas laser. EMR covers variety of magnetic resonance techniques associated with electron like Electron Paramagnetic/Spin Resonance (EPR/ESR). EPR/ESR can be performed on any sample that has unpaired electron spins and used in applications in physics, materials science, chemistry and biology, including studies of impurity states, molecular clusters, antiferromagnetic, ferromagnetic and thin film compounds, natural or induced radicals, optically excited paramagnetic states, electron spin-based quantum information devices, transition-metal based catalysts; and for structural and dynamical studies of metallo-proteins, spin-labeled proteins and other complex bio-molecules and their synthetic models.

Proper citation: National High Magnetic Field Laboratory Electron Magnetic Resonance Core Facility (RRID:SCR_017359) Copy   

•   Source: SciCrunch Registry

https://nationalmaglab.org/user-facilities/dc-field

Facility located at MagLab headquarters near Florida State University in Tallahassee. Contains 14 resistive magnet cells connected to 56 megawatt DC power supply and 15,000 square feet of cooling equipment to remove heat generated by magnets. Includes several superconducting magnets operating at millikelvin temperatures. Among these instruments is 45-tesla hybrid magnet, which offers scientists strongest continuous magnetic field in world. Research is supported by magnet plant and cryogenic system operators. Technicians design, build and repair instruments for user research.

Proper citation: National High Magnetic Field Lab DC Field Core Facility (RRID:SCR_017358) Copy   

•   Source: SciCrunch Registry

http://mass-spec.stanford.edu

Core mass spec and proteomic services include open access lab for trained users with GC/MS, LC/MS, high resolution LC/MS, and MALDI-TOF instruments, help with intact protein analysis, targeted quantitation, drug discovery support, pathway analysis, protein interactions, FFPE tissue analysis, both labeled and label-free proteomics, and more. Please contact SUMS to discuss these and other custom projects including new application development.

Proper citation: Stanford University Vincent Coates Foundation Mass Spectrometry Laboratory Core Facility (RRID:SCR_017801) Copy   

•   Source: SciCrunch Registry

https://www.unmc.edu/vcr/cores/vcr-cores/flow-cytometry/index.html

Provides central location for flow cytometry instrumentation and education. Services include Flow Cytometry,Cell sorting, data analysis, training. Software packages to analyze data include ModFit LT, BD FACSDiva v6, Cell Quest Pro, and FlowJo vX, from facility workstations.

Proper citation: Nebraska University Medical Center Flow Cytometry Research Core Facility (RRID:SCR_017736) Copy   

•   Source: SciCrunch Registry

http://www.columbia.edu/cu/biology/resources/proteomics/

Core provides identification of proteins and metabolites with differential quantitative expression in cells, tissues or in protein affinity purifications. Particular emphasis is on quantitative analysis of posttranslational modifications such as phosphorylation.

Proper citation: Columbia University Quantitative Proteomics and Metabolomics Core Facility (RRID:SCR_017747) Copy   

•   Source: SciCrunch Registry