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https://guolab.shinyapps.io/app-mimivirus-publication/
Web interactive and searchable genome wide comparison tool for browsing human and mimivirus homologous proteins. User friendly Shinny app helps users browse protein sequence homology between humans and mimivirus at genome wide level for querying new homologs and generating new hypotheses.
Proper citation: App for searching human and mimivirus homologous proteins (RRID:SCR_022140) Copy
https://github.com/immunogenomics/harmony
Software R package to project cells into shared embedding in which cells group by cell type rather than dataset specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. Used for integration of single cell data.
Proper citation: Harmony (RRID:SCR_022206) Copy
Open source Java based image processing software program designed for scientific multidimensional images. ImageJ has been transformed to ImageJ2 application to improve data engine to be sufficient to analyze modern datasets.
Proper citation: ImageJ (RRID:SCR_003070) Copy
http://www.type2diabetesgenetics.org/
Portal and database of DNA sequence, functional and epigenomic information, and clinical data from studies on type 2 diabetes and analytic tools to analyze these data. .Provides data and tools to promote understanding and treatment of type 2 diabetes and its complications. Used for identifying genetic biomarkers correlated to Type 2 diabetes and development of novel drugs for this disease.
Proper citation: Accelerating Medicines Partnership Type 2 Diabetes Knowledge Portal (AMP-T2D) (RRID:SCR_003743) Copy
https://simtk.org/home/rna-viz-proto
A software application for animating and visualising RNA and other macromolecular structures. Users are able to use their intuition to interactively refold RNA structures and produce morphs from one structure to another. It allow researchers to explore and manipulate molecular structures Imported from BiositeMaps registry, to better understand structure:function relationships, folding pathways, and molecular motion.
Proper citation: ToRNADo (RRID:SCR_002706) Copy
https://obofoundry.org/ontology/cl.html
Ontology designed as a structured controlled vocabulary for cell types. It was constructed for use by the model organism and other bioinformatics databases. It includes cell types from prokaryotes, mammals, and fungi. The ontology is available in the formats adopted by the Open Biological Ontologies umbrella and is designed to be used in the context of model organism genome and other biological databases.
Proper citation: Cell Type Ontology (RRID:SCR_004251) Copy
http://www.project-redcap.org/
Web application that allows users to build and manage online surveys and databases. Using REDCap's stream-lined process for rapidly developing projects, you may create and design projects using 1) the online method from your web browser using the Online Designer; and/or 2) the offline method by constructing a "data dictionary" template file in Microsoft Excel, which can be later uploaded into REDCap. Both surveys and databases (or a mixture of the two) can be built using these methods. REDCap provides audit trails for tracking data manipulation and user activity, as well as automated export procedures for seamless data downloads to Excel, PDF, and common statistical packages (SPSS, SAS, Stata, R). Also included are a built-in project calendar, a scheduling module, ad hoc reporting tools, and advanced features, such as branching logic, file uploading, and calculated fields. REDCap has a quick and easy software installation process, so that you can get REDCap running and fully functional in a matter of minutes. Several language translations have already been compiled for REDCap (e.g. Chinese, French, German, Portuguese), and it is anticipated that other languages will be available in full versions of REDCap soon. The REDCap Shared Library is a repository for REDCap data collection instruments and forms that can be downloaded and used by researchers at REDCap partner institutions.
Proper citation: REDCap (RRID:SCR_003445) Copy
Web based collaborative text annotation tool. Used for managing multi-user, multi-label document annotation. Project managers can specify annotation schema for entities and relations and select annotators and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. Displays figures from full text.
Proper citation: TeamTat (RRID:SCR_023439) Copy
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
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
http://udn.nichd.nih.gov/brainatlas_home.html
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 1, 2019. The first brain atlas for the common marmoset to be made available since a printed atlas by Stephan, Baron and Schwerdtfeger published in 1980. It is a combined histological and magnetic resonance imaging (MRI) atlas constructed from the brains of two adult female marmosets. Histological sections were processed from Nissl staining and digitized to produce an atlas in a large format that facilitates visualization of structures with significant detail. Naming of identifiable brain structures was performed utilizing current terminology. For the present atlas, an adult female was perfused through the heart with PBS followed by 10% formalin. The brain was then sent to Neuroscience Associates of Knoxville, TN, who prepared the brain for histological analysis. The brain was cut in the coronal (frontal) plane at 40 microns, every sixth section stained for Nissl granules with thionine and every seventh section stained for myelinated fibers with the Weil technique. The mounted sections were photographed at the NIH (Medical Arts and Photography Branch). The equipment used was a Nikon Multiphot optical bench with Zeiss Luminar 100 mm lens, and scanned with a Better Light 6100 scan back driven by Better Light Viewfinder 5.3 software. The final images were saved as arrays of 6000x8000 pixels in Adobe Photoshop 6.0. A scale in mm provided with these images permitted construction of the final Nissl atlas files with a horizontal and vertical scale. Some additional re-touching (brightness and contrast) was done with Adobe Photoshop Elements 2.0. The schematic (labeled) atlas plates were created from the Nissl images. The nomenclature came almost exclusively from brainmaps.org, where a rhesus monkey brain with structures labeled can be found. The labels for the MRI images were placed by M. R. Zametkin, under supervision from Dr. Newman.
Proper citation: Brain atlas of the common marmoset (RRID:SCR_005135) Copy
http://science.education.nih.gov/SciEdBlog
A blog put out by the NIH Office of Science Education.
Proper citation: NIH SciEd Blog (RRID:SCR_005499) Copy
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
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
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
Repository to make datasets resulting from NIH funded research more accessible, citable, shareable, and discoverable. Data submitted will be reviewed to ensure there is no personally identifiable information in data and metadata prior to being published and in line with FAIR -Findable, Accessible, Interoperable, and Reusable principles. Data published on Figshare is assigned persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.Complited on July,2020. Researches can continue to share NIH funded data and other research product on figshare.com.
Proper citation: NIH Figshare Archive (RRID:SCR_017580) Copy
Integrated genomic and functional genomic database for Entamoeba and Acanthamoeba parasites. Contains genomes of three Entamoeba species and microarray expression data for E. histolytica. Integrates whole genome sequence and annotation and includes experimental data and environmental isolate sequences provided by community researchers.
Proper citation: AmoebaDB (RRID:SCR_017592) Copy
Free, cloud-based platform for publishing, sharing, and processing standardized neurophysiology data, primarily using the Neurodata Without Borders (NWB) format. Supported by the BRAIN Initiative, it enables researchers to collaborate, reuse datasets, and adhere to FAIR data principles.
Proper citation: Distributed Archives for Neurophysiology Data Integration (RRID:SCR_017571) Copy
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
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
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