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| Resource Name | Proper Citation | Abbreviations | Resource Type |
Description |
Keywords | Resource Relationships | |||||||||||||
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Natural Products Atlas Resource Report Resource Website 10+ mentions |
Natural Products Atlas (RRID:SCR_025107) | NP Atlas | data or information resource, atlas, knowledge base | Open access knowledge base for microbial natural products discovery. Database of microbially derived natural product structures. Provides coverage of bacterial and fungal natural products to visualize chemical diversity. Includes compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. Interactive web portal permits searching by structure, substructure, and physical properties. Provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. Atlas has been developed under FAIR principles. | FAIR principles, microbial natural products discovery, natural product structures, bacterial and fungal natural products, visualize chemical diversity, | has parent organization: Simon Fraser University; British Columbia; Canada | NSERC Discovery ; NCCIH U41 AT008718; NIGMS R01 GM125943; NCCIH F31 AT010098; NCI F31 CA236237; NCCIH T32 AT007533; NIH D43 TW010530; NSF ; BBSRC ; Carnegie Trust for the Universities of Scotland ; Netherlands eScience Center ; Sao Paulo Research Foundation ; NCCIH AT008718; NIGMS GM124461; Natural Sciences and Engineering Research Council of Canada ; Ministry of Science ; Technology and Telecommunications of Costa Rica |
PMID:31807684 DOI:10.1093/nar/gkab941 |
Free, Freely available, | SCR_025107 | , The Natural Products Atlas, The Natural Products Atlas 2.0 | 2026-02-12 09:48:12 | 19 | ||||||
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GONUTS Resource Report Resource Website 1+ mentions |
GONUTS (RRID:SCR_000653) | GONUTS | narrative resource, data or information resource, wiki, database | A wiki where users of the Gene Ontology can contribute and view notes about how specific GO terms are used. GONUTS can also be used as a GO term browser, or to search for GO annotations of specific genes from included organisms. The rationale for this wiki is based on helping new users of the gene ontology understand and use it. The GONUTS wiki is not an official product of the the Gene Ontology consortium. The GO consortium has a public wiki at their website, http://wiki.geneontology.org/. Maintaining the ontology involves many decisions to carefully choose terms and relationships. These decisions are currently made at GO meetings and via online discussion using the GO mailing lists and the Sourceforge curator request tracker. However, it is difficult for someone starting to use GO to understand these decisions. Some insight can be obtained by mining the tracker, the listservs and the minutes of GO meetings, but this is difficult, as these discussions are often dispersed and sometimes don't contain the GO accessions in the relevant messages. Wikis provide a way to create collaboratively written documentation for each GO term to explain how it should be used, how to satisfy the true path requirement, and whether an annotation should be placed at a different level. In addition, the wiki pages provide a discussion space, where users can post questions and discuss possible changes to the ontology. GONUTS is currently set up so anyone can view or search, but only registered users can edit or add pages. Currently registered users can create new users, and we are working to add at least one registered user for each participating database (So far we have registered users at EcoliHub, EcoCyc, GOA, BeeBase, SGD, dictyBase, FlyBase, WormBase, TAIR, Rat Genome Database, ZFIN, MGI, UCL and AgBase... | ontology or annotation browser, ontology or annotation search engine, ontology or annotation editor, protein |
is listed by: Gene Ontology Tools is listed by: OMICtools is related to: Gene Ontology has parent organization: EcoliHub |
NIGMS 1U24 GM077905-01; NIGMS U24 GM088849 |
PMID:22110029 | Free for academic use, The community can contribute to this resource | OMICS_02268, nlx_30164 | SCR_000653 | Gene Ontology Normal Usage Tracking System, GONUTS wiki | 2026-02-13 10:54:43 | 1 | |||||
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Cell Image Library (CIL) Resource Report Resource Website 10+ mentions |
Cell Image Library (CIL) (RRID:SCR_003510) | CIL | data repository, storage service resource, data or information resource, service resource, image repository, database | Freely accessible, public repository of vetted and annotated microscopic images, videos, and animations of cells from a variety of organisms, showcasing cell architecture, intracellular functionalities, and both normal and abnormal processes. Explore by Cell Process, Cell Component, Cell Type or Organism. The Cell includes images acquired from historical and modern collections, publications, and by recruitment. | microscopic image repository, microscopic video repository, cell animation repository, bio.tools |
is used by: NIF Data Federation is recommended by: National Library of Medicine is recommended by: NIDDK Information Network (dkNET) is recommended by: NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases is listed by: re3data.org is listed by: bio.tools is listed by: Debian is related to: Cell Centered Database is related to: Cell Centered Database is related to: OME-TIFF Format is related to: Integrated Manually Extracted Annotation has parent organization: American Society for Cell Biology has parent organization: University of California; San Diego;National Center for Microscopy and Imaging Research - NCMIR has parent organization: University of California at San Diego; California; USA is parent organization of: Biological Imaging Methods Ontology |
NIGMS RC2 GM092708 | PMID:34218671 PMID:34218673 |
Free, Freely available | biotools:cellimagelibrary, nif-0000-37639, r3d100011601 | http://www.cellimagelibrary.org/pages/about https://bio.tools/cellimagelibrary https://doi.org/10.17616/R3N92J |
SCR_003510 | Cell Image Library. CIL, Cell Image Library (CIL) | 2026-02-13 10:55:16 | 19 | ||||
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Reactome Resource Report Resource Website 1000+ mentions |
Reactome (RRID:SCR_003485) | data analysis service, analysis service resource, data or information resource, production service resource, service resource, database | Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets. | pathway, interaction, reaction, nucleic acid, protein, complex, small molecule, signaling pathway, immune function, transcriptional regulation, translation, apoptosis, metabolism, ortholog, visualization, protein-protein interaction, web service, book, biomart, gold standard, bio.tools, FASEB list |
is used by: NIF Data Federation is used by: DisGeNET is used by: Pathway Analysis Tool for Integration and Knowledge Acquisition is listed by: re3data.org is listed by: bio.tools is listed by: Debian is related to: WikiPathways is related to: Pathway Commons is related to: ConsensusPathDB is related to: FlyMine is related to: AmiGO is related to: PSICQUIC Registry is related to: Integrated Molecular Interaction Database is related to: NCBI BioSystems Database is related to: MOPED - Model Organism Protein Expression Database is related to: KOBAS is related to: PSICQUIC Registry is related to: Pathway Interaction Database is related to: hiPathDB - human integrated Pathway DB with facile visualization is related to: Algal Functional Annotation Tool has parent organization: Ontario Institute for Cancer Research has parent organization: Cold Spring Harbor Laboratory has parent organization: European Bioinformatics Institute has parent organization: New York University School of Medicine; New York; USA works with: PathwayMatcher |
Ontario Research Fund ; European Molecular Biology Laboratory ; NHGRI P41 HG003751; European Union FP6 ENFIN LSHG-CT-2005-518254; NIGMS GM080223; NIGMS R01 GM100039 |
PMID:21082427 PMID:21067998 |
Open source, Public, Freely available | r3d100010285, nif-0000-03390, biotools:reactome | https://bio.tools/reactome https://doi.org/10.17616/R3V59P |
SCR_003485 | Reactome Functional Interaction Network | 2026-02-13 10:55:15 | 4282 | |||||
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miniTUBA Resource Report Resource Website |
miniTUBA (RRID:SCR_003447) | miniTUBA | production service resource, service resource, analysis service resource, storage service resource | 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. | analysis, analyze, bayesian, causation, clinical, linear, medical, structure, temporal, network analysis, network, molecule, information refining, gene expression regulation, bioinformatics, statistical package, interaction network, prediction, pathway, inference, biomedical, intervention |
is listed by: Biositemaps has parent organization: National Center for Integrative Biomedical Informatics has parent organization: University of Michigan; Ann Arbor; USA |
Society of University Surgeons Foundation ; NIDA U54DA021519; NIAID 1R21AI057875-01; NIGMS K08 GM074678-01A1 |
PMID:17644819 | Free, Freely available | nif-0000-33272 | SCR_003447 | miniTUBA - Medical Inference by Network Integration of Temporal Data using Bayesian Analysis tool, Medical Inference by Network Integration of Temporal Data using Bayesian Analysis tool, Medical Inference by Network Integration of Temporal Data using Bayesian Analysis tool (miniTUBA), The Medical Inference by Network Integration of Temporal Data using Bayesian Analysis tool | 2026-02-13 10:55:14 | 0 | |||||
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Systems Biology Graphical Notation Resource Report Resource Website 1+ mentions |
Systems Biology Graphical Notation (RRID:SCR_004671) | SBGN | international standard specification, training resource, portal, data or information resource, software resource, narrative resource, meeting resource, topical portal, standard specification | The Systems Biology Graphical Notation (SBGN) project aims to develop high quality, standard graphical languages for representing biological processes and interactions. Each SBGN language is based on the consensus of the broad international SBGN community of biologists, curators and software developers. Over the course of its development many individuals, organizations and companies made invaluable contributions to the SBGN through participating in discussions and meetings, providing feedback on the documentation and worked examples, adopting the standard and spreading the word. Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. A list of software packages known to provide (or have started to develop) support for SBGN notations is available. | New Energy and Industrial Technology Development Organization ; Okinawa Institute of Science and Technology ; BBSRC ; National Institute of Advanced Industrial Science and Technology of Japan ; European Media Laboratory EML Research GmbH ; California Institute of Technology; California; USA ; NIGMS 1R01GM081070-01 |
PMID:19668183 | nlx_66628 | SCR_004671 | 2026-02-13 10:55:29 | 1 | |||||||||
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GeneNetwork Resource Report Resource Website 100+ mentions |
GeneNetwork (RRID:SCR_002388) | GeneNetwork, WebQTL | data repository, storage service resource, data or information resource, service resource, database | Web platform that provides access to data and tools to study complex networks of genes, molecules, and higher order gene function and phenotypes. Sequence data (SNPs) and transcriptome data sets (expression genetic or eQTL data sets). Quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. Used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Users are welcome to enter their own private data. | Variation, trait, vertebrate trait ontology, phenotype, systems genetics, quantitative trait, gene mapping, experimental precision medicinenetwork analysis, causal modeling, genomic location, genotype, inbred strain, sex, heterogeneous stock, phenome, phenotype, QTL, expression QTL, genetic reference population, single nucleotide polymorphism, RNA expression, protein expression, metabolite expression, metagenomics, epigenomics, gene-by-environmental interaction, epistasis, FAIR data standards, open source software, FASEB list |
is used by: NIF Data Federation is used by: Hypothesis Center is related to: NIH Data Sharing Repositories has parent organization: University of Tennessee Health Science Center; Tennessee; USA |
NIGMS R01 GM123489; NIAAA U01 AA016662; NIAAA U01 AA13499; NIAAA U24 AA13513; NIAAA U01 AA014425; NIA R01 AG043930; NIDA P20 DA21131; NCI U01 CA105417; NCRR U24 RR021760 |
PMID:8043953 PMID:11737945 PMID:15043217 PMID:15114364 PMID:15043220 PMID:15043219 PMID:15711545 PMID:18368372 PMID:27933521 |
Restricted | nif-0000-00380 | SCR_002388 | GeneNetwork and WebQTL, GeneNetwork / WebQTL, www.genenetwork.org, GeneNetwork WebQTL, The GeneNetwork / WebQTL | 2026-02-13 10:55:02 | 473 | |||||
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ConTrack Resource Report Resource Website 10+ mentions |
ConTrack (RRID:SCR_002681) | ConTrack | image processing software, software resource, data processing software, software application | An algorithm for identifying pathways that are known to exist between two regions within DTI data of anisotropic tissue, e.g., muscle, brain, spinal cord. The ConTrack algorithms use knowledge of DTI scanning physics and apriori information about tissue architecture to identify the location of connections between two regions within the DTI data. Assuming a course of connection or pathway between these two regions is known to exist within the measured tissue, ConTrack can be used to estimate properties of these connections in-vivo. | diffusion tensor imaging, tractography, brain connectivity, mri, software, source code, pathway, fiber tractography, tissue analysis |
is listed by: Biositemaps has parent organization: Simtk.org |
NIH Roadmap for Medical Research ; NIGMS U54 GM072970; NEI EY015000 |
PMID:18831651 | Free, Available for download, Freely available | nif-0000-23303 | SCR_002681 | Connectivity Tracking, Connectivity Tracking (ConTrack) | 2026-02-13 10:55:05 | 13 | |||||
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Diseasome Resource Report Resource Website 1+ mentions |
Diseasome (RRID:SCR_002792) | Diseasome | map, data set, data or information resource, book, service resource, image, narrative resource | A disease / disorder relationships explorer and a sample of a map-oriented scientific work. It uses the Human Disease Network dataset and allows intuitive knowledge discovery by mapping its complexity. The Human Disease Network (official) dataset, a poster of the data and related book (Biology - The digital era, ISBN: 978-2-271-06779-1) are available. This kind of data has a network-like organization, and relations between elements are at least as important as the elements themselves. More data could be integrated to this prototype and could eventually bring closer phenotype and genotype. Results should be visual, but also printable. Creating posters can enhance collaborative work. It facilitates discussion and sharing of ideas about the data. This website initiative is an invitation to think about the benefits of networks exploration but above all it tries to outline future designs of scientific information systems. | disease, disorder, genotype, phenotype, poster, network |
is related to: Allen Institute Neurowiki has parent organization: Gephi |
Dana-Farber Cancer Institute ; W. M. Keck Foundation ; NHGRI ; NIGMS |
PMID:17502601 | Free, Freely Available | nif-0000-24580 | SCR_002792 | Diseaseome | 2026-02-13 10:55:07 | 1 | |||||
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PharmGKB Resource Report Resource Website 1000+ mentions |
PharmGKB (RRID:SCR_002689) | PharmGKB | data repository, storage service resource, web service, data set, data or information resource, service resource, data access protocol, software resource, database | Database and central repository for genetic, genomic, molecular and cellular phenotype data and clinical information about people who have participated in pharmacogenomics research studies. The data includes, but is not limited to, clinical and basic pharmacokinetic and pharmacogenomic research in the cardiovascular, pulmonary, cancer, pathways, metabolic and transporter domains. PharmGKB welcomes submissions of primary data from all research into genes and genetic variation and their effects on drug and disease phenotypes. PharmGKB collects, encodes, and disseminates knowledge about the impact of human genetic variations on drug response. They curate primary genotype and phenotype data, annotate gene variants and gene-drug-disease relationships via literature review, and summarize important PGx genes and drug pathways. PharmGKB is part of the NIH Pharmacogenomics Research Network (PGRN), a nationwide collaborative research consortium. Its aim is to aid researchers in understanding how genetic variation among individuals contributes to differences in reactions to drugs. A selected subset of data from PharmGKB is accessible via a SOAP interface. Downloaded data is available for individual research purposes only. Drugs with pharmacogenomic information in the context of FDA-approved drug labels are cataloged and drugs with mounting pharmacogenomic evidence are listed. | pharmacogenomics, microarray, pathway, phenotype, snp array, genotype, clinical, genetic variation, drug, gene, genetic variation, disease, cardiovascular, pulmonary, cancer, metabolic, transporter, drug response, small molecule, research, drug response, FASEB list |
is used by: NIF Data Federation is listed by: OMICtools is related to: WikiPathways is related to: ConsensusPathDB is related to: Integrated Molecular Interaction Database is related to: MalaCards is related to: phenomeNET has parent organization: Stanford University; Stanford; California is parent organization of: PharmGKB Ontology |
NIGMS R24 GM61374 | PMID:11908751 | Free, Freely available | nif-0000-00414, OMICS_01586, r3d100012325 | https://doi.org/10.17616/R31H1N | SCR_002689 | Pharmacogenomics Knowledge Base | 2026-02-13 10:55:06 | 1152 | ||||
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Patterns of Gene Expression in Drosophila Embryogenesis Resource Report Resource Website 50+ mentions |
Patterns of Gene Expression in Drosophila Embryogenesis (RRID:SCR_002868) | BDGP insitu | image collection, data or information resource, source code, software resource, database | Database of embryonic expression patterns using a high throughput RNA in situ hybridization of the protein-coding genes identified in the Drosophila melanogaster genome with images and controlled vocabulary annotations. At the end of production pipeline gene expression patterns are documented by taking a large number of digital images of individual embryos. The quality and identity of the captured image data are verified by independently derived microarray time-course analysis of gene expression using Affymetrix GeneChip technology. Gene expression patterns are annotated with controlled vocabulary for developmental anatomy of Drosophila embryogenesis. Image, microarray and annotation data are stored in a modified version of Gene Ontology database and the entire dataset is available on the web in browsable and searchable form or MySQL dump can be downloaded. So far, they have examined expression of 7507 genes and documented them with 111184 digital photographs. | embryo, embryogenesis, gene, anatomy, microarray, pattern, protocol, rna, gene expression, expression pattern, embryonic drosophila, in situ hybridization, annotation, est, FASEB list |
is related to: Gene Ontology has parent organization: Berkeley Drosophila Genome Project |
Howard Hughes Medical Institute ; NIH ; NIGMS R01 GM076655; NHGRI HG00750; NHGRI P41 HG00739 |
PMID:17645804 PMID:12537577 |
Free, Freely available, Available for download | nif-0000-25550, r3d100011327 | https://doi.org/10.17616/R32H0K | http://www.fruitfly.org/cgi-bin/ex/insitu.pl | SCR_002868 | BDGP Embryonic Expression Patterns | 2026-02-13 10:55:08 | 64 | |||
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I2D Resource Report Resource Website 10+ mentions |
I2D (RRID:SCR_002957) | I2D | data analysis service, analysis service resource, data or information resource, production service resource, service resource, database | Database of known and predicted mammalian and eukaryotic protein-protein interactions, it is designed to be both a resource for the laboratory scientist to explore known and predicted protein-protein interactions, and to facilitate bioinformatics initiatives exploring protein interaction networks. It has been built by mapping high-throughput (HTP) data between species. Thus, until experimentally verified, these interactions should be considered predictions. It remains one of the most comprehensive sources of known and predicted eukaryotic PPI. It contains 490,600 Source Interactions, 370,002 Predicted Interactions, for a total of 846,116 interactions, and continues to expand as new protein-protein interaction data becomes available. | interaction, prediction, protein-protein interaction, high-throughput, model organism, mammal, eukaryote, visualization, interolog, protein |
is related to: Interaction Reference Index is related to: IMEx - The International Molecular Exchange Consortium is related to: PSICQUIC Registry is related to: IntAct has parent organization: University of Toronto; Ontario; Canada |
National Science and Engineering Research Council RGPIN 203833-02; NIGMS P50-GM62413 |
PMID:17535438 PMID:15657099 |
Free, Available for download, Freely available | nif-0000-03005, r3d100010675 | https://doi.org/10.17616/R3BG8R | SCR_002957 | Interologous Interaction Database, OPHID, I2D - Interologous Interaction Database | 2026-02-13 10:55:09 | 23 | ||||
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Database of Interacting Proteins (DIP) Resource Report Resource Website 100+ mentions |
Database of Interacting Proteins (DIP) (RRID:SCR_003167) | DIP | data repository, storage service resource, data analysis service, analysis service resource, data or information resource, production service resource, service resource, database | Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available. | blast, cellular network, ligand-receptor complex, ligand, network, protein, protein interaction, protein ligand, protein-protein interaction, protein receptor, receptor, sequence, interaction, regulatory pathway, signaling pathway, protein binding, bio.tools, FASEB list |
is recommended by: NIDDK Information Network (dkNET) is recommended by: National Library of Medicine is recommended by: NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases is listed by: OMICtools is listed by: re3data.org is listed by: NIH Data Sharing Repositories is listed by: bio.tools is listed by: Debian is related to: IMEx - The International Molecular Exchange Consortium is related to: IMEx - The International Molecular Exchange Consortium is related to: MPIDB is related to: TissueNet - The Database of Human Tissue Protein-Protein Interactions is related to: InteroPorc is related to: Interaction Reference Index is related to: ConsensusPathDB is related to: NIH Data Sharing Repositories is related to: PSICQUIC Registry is related to: Agile Protein Interactomes DataServer has parent organization: University of California at Los Angeles; California; USA |
NIGMS | PMID:14681454 | Free, Available for download, Freely available | OMICS_01905, nif-0000-00569, r3d100010882, biotools:dip | https://dip.doe-mbi.ucla.edu/dip/Main.cgi https://bio.tools/dip https://doi.org/10.17616/R3431F |
SCR_003167 | , Database of Interacting Proteins, DIP, Database of Interacting Proteins (DIP) | 2026-02-13 10:55:11 | 153 | ||||
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MouseNET Resource Report Resource Website 1+ mentions |
MouseNET (RRID:SCR_003357) | mouseNet | data analysis service, analysis service resource, data or information resource, production service resource, service resource, database | A functional network for laboratory mouse based on integration of diverse genetic and genomic data. It allows the users to accurately predict novel functional assignments and network components. MouseNET uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the mouseNET algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. The graph may be explored further. As you move the mouse over genes in the network, interactions involving these genes are highlighted.If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. | gene, network, mouse, protein function, visualization, open reading frame, graph |
is listed by: OMICtools is related to: Gene Ontology is related to: mouseMAP has parent organization: Princeton University; New Jersey; USA |
NSF DBI-0546275; NIGMS R01 GM071966; NSF IIS-0513552; NIGMS P50 GM071508 |
PMID:18818725 | Free, Freely available | OMICS_01550, nif-0000-32003 | SCR_003357 | MouseNET | 2026-02-13 10:55:14 | 3 | |||||
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GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool Resource Report Resource Website 1+ mentions |
GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool (RRID:SCR_003191) | GOLEM | data analysis service, analysis service resource, production service resource, source code, service resource, software resource | THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Welcome to the home of GOLEM: An interactive, graphical gene-ontology visualization, navigation,and analysis tool on the web. GOLEM is a useful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy. Users can also load annotations for various organisms into the ontology in order to search for particular genes, or to limit the display to show only GO terms relevant to a particular organism, or to quickly search for GO terms enriched in a set of query genes. GOLEM is implemented in Java, and is available both for use on the web as an applet, and for download as a JAR package. A brief tutorial on how to use GOLEM is available both online and in the instructions included in the program. We also have a list of links to libraries used to make GOLEM, as well as the various organizations that curate organism annotations to the ontology. GOLEM is available as a .jar package and a macintosh .app for use on- or off- line as a stand-alone package. You will need to have Java (v.1.5 or greater) installed on your system to run GOLEM. Source code (including Eclipse project files) are also available. GOLEM (Gene Ontology Local Exploration Map)is a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet. | gene ontology, ontology visualization, ontology analysis |
is related to: Gene Ontology has parent organization: Princeton University; New Jersey; USA |
NIGMS R01 GM071966; NSF IIS-0513552; NIGMS P50 GM071508 |
PMID:17032457 | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-30620 | https://lsi.princeton.edu/golem-interactive-graph-based-gene-ontology-navigation-and-analysis-tool | SCR_003191 | GOLEM An interactive graphical gene-ontology visualization navigation and analysis tool, GOLEM An interactive graphical gene-ontology visualization navigation analysis tool | 2026-02-13 10:55:11 | 3 | ||||
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NLSdb: a database of nuclear localization signals Resource Report Resource Website 1+ mentions |
NLSdb: a database of nuclear localization signals (RRID:SCR_003273) | NLSdb | data analysis service, analysis service resource, data or information resource, production service resource, service resource, database | A database of nuclear localization signals (NLSs) and of nuclear proteins targeted to the nucleus by NLS motifs. NLSs are short stretches of residues mediating transport of nuclear proteins into the nucleus. The database contains 114 experimentally determined NLSs that were obtained through an extensive literature search. Using "in silico mutagenesis" this set was extended to 308 experimental and potential NLSs. This final set matched over 43% of all known nuclear proteins and matches no currently known non-nuclear protein. NLSdb contains over 6000 predicted nuclear proteins and their targeting signals from the PDB and SWISS-PROT/TrEMBL databases. The database also contains over 12 500 predicted nuclear proteins from six entirely sequenced eukaryotic proteomes (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana and Saccharomyces cerevisiae). NLS motifs often co-localize with DNA-binding regions. This observation was used to also annotate over 1500 DNA-binding proteins. From this site you can: * Query NLSdb * Find out how to use NLSdb * Browse the entries in NLSdb * Find out if your protein has an NLS using PredictNLS * Predict subcellular localization of your protein using LOCtree | nuclear localization signal, nuclear protein, nucleus, motif, predict, protein | has parent organization: Columbia University; New York; USA | NIGMS 1-P50-GM62413-01; NSF DBI-0131168 |
PMID:12520032 | Free for academic use, Acknowledgement requested, All others should inquire about a commercial license | nif-0000-03191 | http://cubic.bioc.columbia.edu/db/NLSdb/ | SCR_003273 | NLSdb - a database of nuclear localization signals | 2026-02-13 10:55:13 | 4 | ||||
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EMDataResource.org Resource Report Resource Website 100+ mentions |
EMDataResource.org (RRID:SCR_003207) | EMDB, EMDataResource | data repository, storage service resource, portal, data or information resource, service resource, project portal | Portal for deposition and retrieval of cryo electron microscopy (3DEM) density maps, atomic models, and associated metadata. Global resource for 3 Dimensional Electron Microscopy structure data archiving and retrieval, news, events, software tools, data standards, validation methods. | deposition, retrival, cryo, electron, microscopy, 3DEM, density, maps, atomic, model, metadata, structure |
is recommended by: NIDDK Information Network (dkNET) is recommended by: NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases is listed by: 3DVC is listed by: re3data.org is affiliated with: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is related to: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is related to: Electron Microscopy Data Bank at PDBe (MSD-EBI) is related to: PDBe - Protein Data Bank in Europe is related to: National Center for Macromolecular Imaging has parent organization: Rutgers University; New Jersey; USA has parent organization: European Bioinformatics Institute has parent organization: Baylor University; Texas; USA |
NIGMS R01 GM079429; BBSRC BBG022577 |
PMID:20935055 PMID:20888470 |
Free, Freely available | r3d100010552, nif-0000-30776 | https://doi.org/10.17616/R3T61P | EMDataBank.org | SCR_003207 | EMDataResource, EMDResource, EMDB, EMDataBank.org, EMDataBank - Unified Data Resource for 3DEM, EMDataBank | 2026-02-13 10:55:12 | 168 | |||
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GenePattern Resource Report Resource Website 1000+ mentions |
GenePattern (RRID:SCR_003201) | GenePattern | data analysis software, software resource, data processing software, software application | A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. | gene expression, analysis, genomic, pattern, proteomics, silico, snp, workflow, analysis pipeline, flow cytometry, rna-seq, data processing, bio.tools |
is listed by: bio.tools is listed by: Debian is listed by: SoftCite is affiliated with: GenePattern Notebook is related to: TIGRESS has parent organization: Broad Institute |
NCI ; NIGMS |
PMID:16642009 | Free, Freely available | biotools:genepattern, OMICS_01855, nif-0000-30654 | https://bio.tools/genepattern | SCR_003201 | 2026-02-13 10:55:12 | 1078 | |||||
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MCell Resource Report Resource Website 50+ mentions |
MCell (RRID:SCR_007307) | software resource, simulation software, software application | Software modeling tool for realistic simulation of cellular signaling in complex 3-D subcellular microenvironment in and around living cells. Program that uses spatially realistic 3D cellular models and specialized Monte Carlo algorithms to simulate movements and reactions of molecules within and between cells. | Monte Carlo, simulator, cellular, microphysiology, living, cell, BRAIN Initiative |
is recommended by: BRAIN Initiative is related to: CellOrganizer |
NIGMS P41 GM103712 | Free, Available for download, Freely available | nif-0000-00160 | https://github.com/mcellteam/mcell | SCR_007307 | Monte Carlo simulator of cellular microphysiology | 2026-02-13 10:56:04 | 59 | ||||||
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SimTKCore Resource Report Resource Website |
SimTKCore (RRID:SCR_008268) | SimTKCore | software resource, simulation software, software application | SimTK Core is one of the two packages that together constitute SimTK, the biosimulation toolkit from the Simbios Center. The other major component of SimTK is OpenMM which is packaged separately. This SimTK Core project collects together all the binaries needed for the various SimTK Core subprojects. These include Simbody, Molmodel, Simmath (including Ipopt), Simmatrix, CPodes, SimTKcommon, and Lapack. See the individual projects for descriptions. SimTK brings together in a robust, convenient, open source form the collection of highly-specialized technologies necessary to building successful physics-based simulations of biological structures. These include: strict adherence to an important set of abstractions and guiding principles, robust, high-performance numerical methods, support for developing and sharing physics-based models, and careful software engineering. Accessible High Performance Computing We believe that a primary concern of simulation scientists is performance, that is, speed of computation. We seek to build valid, approximate models using classical physics in order to achieve reasonable run times for our computational studies, so that we can hope to learn something interesting before retirement. In the choice of SimTK technologies, we are focused on achieving the best possible performance on hardware that most researchers actually have. In today''s practice, that means commodity multiprocessors and small clusters. The difference in performance between the best methods and the do-it-yourself techniques most people use can be astoundingeasily an order of magnitude or more. The growing set of SimTK Core libraries seeks to provide the best implementation of the best-known methods for widely used computations such as: Linear algebra, numerical integration and Monte Carlo sampling, multibody (internal coordinate) dynamics, molecular force field evaluation, nonlinear root finding and optimization. All SimTK Core software is in the form of C++ APIs, is thread-safe, and quietly exploits multiple CPUs when they are present. The resulting pre-built binaries are available for download and immediate use. Audience: Biosimulation application programmers interested in including robust, high-performance physics-based simulation in their domain-specific applications. | computational algorithm, high-performance, linear algebra, numerical integration, numerical method, optimization, monte carlo sampling, multibody dynamics, molecular force field evaluation, nonlinear root finding, optimizing, cpodes, simbody, ipopt, molmodel, mit license, linux, mac os x, windows |
is listed by: Biositemaps has parent organization: Stanford University; Stanford; California has parent organization: Simtk.org |
NIGMS U54 GM072970 | PMID:20107615 | nif-0000-23310 | SCR_008268 | 2026-02-13 10:56:12 | 0 |
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