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http://www.neuroepigenomics.org/methylomedb/

A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Proper citation: MethylomeDB (RRID:SCR_005583) Copy   

•   Source: SciCrunch Registry
  

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry
  

http://bard.nih.gov/

Database that allows scientists without specialized training to effectively utilize Molecular Libraries Program (MLP) data. It allows the research community to utilize and develop new chemical probes to explore biological functions by building a central, permanently accessible link to all aspects of chemical biology data and analyses. The project is split into two basic segments, the first segment delivering functionality for a data dictionary, as well as assay protocol and data entry tools. The second builds a data warehouse for analysis and visualization, accessible through a public RESTful API. They will initially deploy two clients that will use this API - a web-based interface and a desktop application. Advanced access to data and the platforms will also be available to support plug-in development and the repackaging of data by others. Initially the project will focus on small molecule assays. Features: * allow scientists to annotate assay data using a common, shared language * provide facile access to data, integrating existing chemical biology and computational resources * enable meaningful analysis and interpretation of discovery data by the research community * support hypothesis generation for iterative probe- and drug-discovery projects * inform the entire small molecule discovery and development process, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BARD (RRID:SCR_006283) 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
  

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://bioinformaticshome.com/tools/rna-seq/descriptions/LIGER.html

Software R package for integrating and analyzing multiple single-cell datasets. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors. Used for analysis of multiple scRNA-seq data sets.

Proper citation: LIGER (RRID:SCR_018100) Copy   

•   Source: SciCrunch Registry
  

https://github.com/galaxyproteomics/mvpapplication-git.git

Software tool as plugin to enable viewing of results produced from workflows integrating genomic sequencing data and mass spectrometry proteomics data. Plugin to Galaxy bioinformatics workbench which enables visualization of mass spectrometry-based proteomics data integrated with genomic and/or transcriptomic sequencing data. Useful for verifying quality of results and characterizing novel peptide sequences identified using multi-omic proteogenomic approach.

Proper citation: Multi-omics Visualization Platform (RRID:SCR_018077) Copy   

•   Source: SciCrunch Registry
  

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
  

https://ncats.nih.gov/n3c/about

Portal for centralized national data to study COVID-19 and identify potential treatments.Centralized, secure analytics platform where patient privacy is protected. Enables collection and analysis of clinical, laboratory and diagnostic data from hospitals and health care plans. Data are provided after executing data transfer agreement with National Center for Advancing Translational Sciences. N3C is partnership among NCATS supported Clinical and Translational Science Awards Program hubs and National Center for Data to Health with overall stewardship by NCATS.

Proper citation: National COVID Cohort Collaborative (RRID:SCR_018757) Copy   

•   Source: SciCrunch Registry
  

https://bivi.co/visualisation/apinatomy

Software toolkit for visualizing multiscale anatomy schematics with phenotype related information. Used for visualisation of multiscale physiology circuitboards and to support clinical and scientific graphical user interfaces and dashboards for biomedical resource management and data analytics. Creates FAIR models of vascular and neural connectivity information for molecular, subcellular, cellular and tissue conduits across multiple scales. Provides interface between physiology knowledge and data relevant to physiology through intuitive graphical interface for managing semantic metadata and ontologies relevant to physiology. Brings together expertise in computer science, image processing, bioengineering and medicine to manage knowledge in physiology and pathology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ApiNATOMY (RRID:SCR_018998) Copy   

•   Source: SciCrunch Registry
  

https://portal.imaging.datacommons.cancer.gov

Portal for finding and analyzing cancer imaging data. Part of Cancer Research Data Commons to support cancer imaging research. Provides cloud based access to medical imaging data and library of analytical tools and workflows to share, analyze, and visualize multi modal imaging data from both clinical and basic cancer research studies.

Proper citation: NCI Imaging Data Commons (RRID:SCR_019127) Copy   

•   Source: SciCrunch Registry
  

https://datacommons.cancer.gov

Cloud based data science infrastructure that provides secure access to cancer research data from NCI programs and key external cancer programs. Serves as coordinated resource for public data sharing of NCI funded programs. Users can explore and use analytical and visualization tools for data analysis. Enables to search and aggregate data across repositories including Cancer Data Service, Clinical Trial Data Commons, Genomic Data Commons, Imaging Data Commons, Integrated Canine Data Commons, Proteomic Data Commons.

Proper citation: Cancer Research Data Commons (RRID:SCR_019128) Copy   

•   Source: SciCrunch Registry
  

https://www.fdilab.org

UCSD based bioinformatics lab composed of several projects in different biomedical disciplines. Established in 2008 as Neuroscience Information Framework and has since expanded to include broader field of biomedical research. Leader in developing and providing novel informatics infrastructure and tools for making data FAIR: Findable, Accessible, Interoperable and Reusable. FAIR Data informatics laboratory develops SciCrunch.org platform.

Proper citation: FAIR Data Informatics Laboratory (RRID:SCR_019235) Copy   

•   Source: SciCrunch Registry
  

http://www.sb.cs.cmu.edu/drem

The Dynamic Regulatory Events Miner (DREM) allows one to model, analyze, and visualize transcriptional gene regulation dynamics. The method of DREM takes as input time series gene expression data and static transcription factor-gene interaction data (e.g. ChIP-chip data), and produces as output a dynamic regulatory map. The dynamic regulatory map highlights major bifurcation events in the time series expression data and transcription factors potentially responsible for them. DREM 2.0 was released and supports a number of new features including: * new static binding data for mouse, human, D. melanogaster, A. thaliana * a new and more flexible implementation of the IOHMM supports dynamic binding data for each time point or as a mix of static/dynamic TF input * expression levels of TFs can be used to improve the models learned by DREM * the motif finder DECOD can be used in conjuction with DREM and help find DNA motifs for unannotated splits * new features for the visualization of expressed TFs, dragging boxes in the model view, and switching between representations

Proper citation: Dynamic Regulatory Events Miner (RRID:SCR_003080) Copy   

•   Source: SciCrunch Registry
  

http://stemcells.nih.gov/research/registry/

A listing of human embryonic cell lines that are eligible for use in NIH funded research. Those lines that carry disease-specific mutations are noted as such under the line name. Total Eligible Lines = 200. The purpose of the Registry is to provide investigators with: # a unique NIH Code for each cell line that must be used when applying for NIH funding and # contact information to facilitate investigators' acquisition of stem cells. Before submitting a new grant application and supporting materials for consideration of a human embryonic stem cell line, scientists may wish to see what lines are already under consideration: * Human embryonic stem cell lines submitted to NIH that are being reviewed to determine if they may be used in NIH-supported research, http://grants.nih.gov/stem_cells/registry/pending.htm President George W. Bush required that the name of the registry be changed in his Executive Order #13435, issued on June 20, 2007. As a result of this Executive Order, the former National Institutes of Health Human Embryonic Stem Cell Registry will now be called the National Institutes of Health Human Pluripotent Stem Cell Registry. The registry will now include both human embryonic stem cells that were derived consistent with the President's policy of August 9, 2001 and human pluripotent stem cells derived from non-embryonic sources.

Proper citation: NIH Human Pluripotent Stem Cell Registry (RRID:SCR_003149) 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