Tools   Select Another Resource Report Type

https://cansar.icr.ac.uk/

canSAR is an integrated database that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this data and make it accessible to cancer research scientists from multiple disciplines, in order to help with hypothesis generation in cancer research and support translational research. This cancer research and drug discovery resource was developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

Proper citation: canSAR (RRID:SCR_006794) Copy   

•   Source: SciCrunch Registry

http://www.psort.org

Portal to the PSORT family of computer programs for the prediction of protein localization sites in cells, as well as other datasets and resources relevant to localization prediction. The standalone versions are available for download for larger analyses.

Proper citation: Psort (RRID:SCR_007038) Copy   

•   Source: SciCrunch Registry

http://prodom.prabi.fr/

Comprehensive set of protein domain families automatically generated from UniProt Knowledge Database. Automated clustering of homologous domains generated from global comparison of all available protein sequences., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ProDom (RRID:SCR_006969) Copy   

•   Source: SciCrunch Registry

http://ardb.cbcb.umd.edu

The goals of Antibiotic Resistance Genes Database (ARGB) are to provide a centralized compendium of information on antibiotic resistance, to facilitate the consistent annotation of resistance information in newly sequenced organisms, and also to facilitate the identification and characterization of new genes. ARGB contains six types of database groups: - Resistance Type: This database contains information, such as resistance profile, mechanism, requirement, epidemiology for each type. - Resistance Gene: This database contains information, such as resistance profile, resistance type, requirement, protein and DNA sequence for each gene.This database only includes NON-REDUNDANT, NON-VECTOR, COMPLETE genes. - Antibiotic: This database contains information, such as producer, action mechanism, resistance type, for each gene. - Resistance Gene(NonRD): This database contains the same information as Resistance Gene. It does NOT include NON-REDUNDANT, NON-VECTOR genes, but includes INCOMPLETE genes. - Resistance Gene(ALL): This database contains the same information as Resistance Gene. It includes all REDUNDANT, VECTOR AND INCOMPLETE genes. - Resistance Species: This database contains resistance profile and corresponding resistance genes for each species. Furthermore, ARDB also contians three types BLAST database: - Resistance Genes Complete: Contains only NON-REDUNDANT, NON-VECTOR, COMPLETE genes sequences. - Resistance Genes Non-redundant: Contains NON-REDUNDANT, NON-VECTOR, COMPLETE, INCOMPLETE genes sequences. - Resistance Genes All: Contains all REDUNDANT, VECTOR, COMPLETE, INCOMPLETE genes sequences. Lastly, ARDB provides four types of Analytical tools: - Normal BLAST: This function allows an user to input a DNA or protein sequence, and find similar DNA (Nucleotide BLAST) or protein (Protein BLAST) sequences using blastn, blastp, blastx, tblastn, tblastx - RPS BLAST: A web RPSBLAST (RPS BLAST) interface is provided to align a query sequence against the Position Specific Scoring Matrix (PSSM) for each type. Normally, this will give the same annotation information as using regular BLAST mentioned above. - Multiple Sequences BLAST (Genome Annotation): This function allows an user to annotate multiple (less than 5000) query sequences in FASTA format. - Mutation Resistance Identification: This function allows an user to identify mutations that will cause potential antibiotic resistance, for 12 genes (16S rRNA, 23S rRNA, gyrA, gyrB, parC, parE, rpoB, katG, pncA, embB, folP, dfr). ������ :Sponsors: ARDB is funded by Uniformed Services University of the Health Sciences, administered by the Henry Jackson Foundation. :

Proper citation: Antibiotic Resistance Genes Database (RRID:SCR_007040) Copy   

•   Source: SciCrunch Registry

http://xin.cz3.nus.edu.sg/group/drt/dart.asp

Database that provides comprehensive information about adverse effect targets of drugs described in the literature, including information about known drug adverse reaction targets, functions and properties. Moreover, proteins involved in adverse effect targets of chemicals not yet confirmed as adverse drug reaction (ADR) targets are also included as potential targets. Associated references are also included. This database gives physiological function of each target, binding drugs / agonists / antagonists / activators / inhibitors, IC(50) values of the inhibitors, corresponding adverse effects, and type of ADR induced by drug binding to a target. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3-dimensional structure, function, and nomenclature of each target along with drug/ligand binding properties, and related literature. Each entry can be retrieved through multiple search methods including target name, target physiological function, adverse effect, ligand name, and biological pathways. A special page is provided for contribution of new or additional information. Function for ADR-target prediction by SVMDART: Submit protein primary sequence for ADR-related protein prediction.

Proper citation: DART - Drug Adverse Reaction Targets (RRID:SCR_007041) Copy   

•   Source: SciCrunch Registry

http://www.genoscope.cns.fr/externe/tetraodon/

The initial objective of Genoscope was to compare the genomic sequences of this fish to that of humans to help in the annotation of human genes and to estimate their number. This strategy is based on the common genetic heritage of the vertebrates: from one species of vertebrate to another, even for those as far apart as a fish and a mammal, the same genes are present for the most part. In the case of the compact genome of Tetraodon, this common complement of genes is contained in a genome eight times smaller than that of humans. Although the length of the exons is similar in these two species, the size of the introns and the intergenic sequences is greatly reduced in this fish. Furthermore, these regions, in contrast to the exons, have diverged completely since the separation of the lineages leading to humans and Tetraodon. The Exofish method, developed at Genoscope, exploits this contrast such that the conserved regions which can be identified by comparing genomic sequences of the two species, correspond only to coding regions. Using preliminary sequencing results of the genome of Tetraodon in the year 2000, Genoscope evaluated the number of human genes at about 30,000, whereas much higher estimations were current. The progress of the annotation of the human genome has since supported the Genoscope hypothesis, with values as low as 22,000 genes and a consensus of around 25,000 genes. The sequencing of the Tetraodon genome at a depth of about 8X, carried out as a collaboration between Genoscope and the Whitehead Institute Center for Genome Research (now the Broad Institute), was finished in 2002, with the production of an assembly covering 90 of the euchromatic region of the genome of the fish. This has permitted the application of Exofish at a larger scale in comparisons with the genome of humans, but also with those of the two other vertebrates sequenced at the time (Takifugu, a fish closely related to Tetraodon, and the mouse). The conserved regions detected in this way have been integrated into the annotation procedure, along with other resources (cDNA sequences from Tetraodon and ab initio predictions). Of the 28,000 genes annotated, some families were examined in detail: selenoproteins, and Type 1 cytokines and their receptors. The comparison of the proteome of Tetraodon with those of mammals has revealed some interesting differences, such as a major diversification of some hormone systems and of the collagen molecules in the fish. A search for transposable elements in the genomic sequences of Tetraodon has also revealed a high diversity (75 types), which contrasts with their scarcity; the small size of the Tetraodon genome is due to the low abundance of these elements, of which some appear to still be active. Another factor in the compactness of the Tetraodon genome, which has been confirmed by annotation, is the reduction in intron size, which approaches a lower limit of 50-60 bp, and which preferentially affects certain genes. The availability of the sequences from the genomes of humans and mice on one hand, and Takifugu and Tetraodon on the other, provide new opportunities for the study of vertebrate evolution. We have shown that the level of neutral evolution is higher in fish than in mammals. The protein sequences of fish also diverge more quickly than those of mammals. A key mechanism in evolution is gene duplication, which we have studied by taking advantage of the anchoring of the majority of the sequences from the assembly on the chromosomes. The result of this study speaks strongly in favor of a whole genome duplication event, very early in the line of ray-finned fish (Actinopterygians). An even stronger evidence came from synteny studies between the genomes of humans and Tetraodon. Using a high-resolution synteny map, we have reconstituted the genome of the vertebrate which predates this duplication - that is, the last common ancestor to all bony vertebrates (most of the vertebrates apart from cartilaginous fish and agnaths like lamprey). This ancestral karyotype contains 12 chromosomes, and the 21 Tetraodon chromosomes derive from it by the whole genome duplication and a surprisingly small number of interchromosomal rearrangements. On the contrary, exchanges between chromosomes have been much more frequent in the lineage that leads to humans. Sponsors: The project was supported by the Consortium National de Recherche en Genomique and the National Human Genome Research Institute.

Proper citation: Tetraodon Genome Browser (RRID:SCR_007079) Copy   

•   Source: SciCrunch Registry

http://weizhong-lab.ucsd.edu/cd-hit/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software program for clustering biological sequences with many applications in various fields such as making non-redundant databases, finding duplicates, identifying protein families, filtering sequence errors and improving sequence assembly etc. It is very fast and can handle extremely large databases. CD-HIT helps to significantly reduce the computational and manual efforts in many sequence analysis tasks and aids in understanding the data structure and correct the bias within a dataset. The CD-HIT package has CD-HIT, CD-HIT-2D, CD-HIT-EST, CD-HIT-EST-2D, CD-HIT-454, CD-HIT-PARA, PSI-CD-HIT, CD-HIT-OTU and over a dozen scripts. * CD-HIT (CD-HIT-EST) clusters similar proteins (DNAs) into clusters that meet a user-defined similarity threshold. * CD-HIT-2D (CD-HIT-EST-2D) compares 2 datasets and identifies the sequences in db2 that are similar to db1 above a threshold. * CD-HIT-454 identifies natural and artificial duplicates from pyrosequencing reads. * CD-HIT-OTU cluster rRNA tags into OTUs The usage of other programs and scripts can be found in CD-HIT user''s guide. CD-HIT was originally developed by Dr. Weizhong Li at Dr. Adam Godzik''s Lab at the Burnham Institute (now Sanford-Burnham Medical Research Institute)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CD-HIT (RRID:SCR_007105) Copy   

•   Source: SciCrunch Registry

http://hcv.lanl.gov/content/immuno/immuno-main.html

The HCV Immunology Database contains a curated inventory of immunological epitopes in HCV and their interaction with the immune system, with associated retrieval and analysis tools. The funding for the HCV database project has stopped, and this website and the HCV immunology database are no longer maintained. The site will stay up, but problems will not be fixed. The database was last updated in September 2007. The HIV immunology website contains the same tools, and may be usable for non-HCV-specific analyses. For new epitope information, users of this database can try the Immuno Epitope Database (http://www.immuneepitope.org).

Proper citation: HCV Immunology Database (RRID:SCR_007086) Copy   

•   Source: SciCrunch Registry

https://rostlab.org/owiki/index.php/PredictNLS

Software automated tool for analysis and determination of Nuclear Localization Signals (NLS). Predicts that your protein is nuclear or finds out whether your potential NLS is found in our database. The program also compiles statistics on the number of nuclear/non-nuclear proteins in which your potential NLS is found. Finally, proteins with similar NLS motifs are reported, and the experimental paper describing the particular NLS are given.

Proper citation: PredictNLS (RRID:SCR_003133) Copy   

•   Source: SciCrunch Registry

http://www.elsevier.com/online-tools/pathway-studio/biological-database

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023. MedScan is a fast and flexible biomedical information extraction technology. It uses dictionaries to identify individual biomedical terms (proteins, cellular processes, small molecules, diseases, etc) referred to in literature articles, and applies advanced natural language processing techniques to detect the relationships within the article and extract these terms and the relationships; the overall process of detection, identification, extraction and assembling, is termed Information Harvesting. Information extracted by MedScan represents the multiple aspects of protein function, including protein modification, cellular localization, protein-protein interactions, gene expression regulation, molecular transport and synthesis, as well as association with diseases, and regulation of various cellular processes. This scope can be broadened by modifying information extraction rules and the dictionaries. Dictionaries can be assembled on any topic or area that is represented in the literature you wish to harvest. High-throughput data generation methodologies like microarray gene expression require new approaches for gathering information for data analysis. For the best results, computational approaches used for high-throughput data analysis require that biological information from the literature be a coherent and integrated part of the analysis software itself. Pathway Studio meets this challenge through its MedScan Technology and underlying ResNet database. All editions of Pathway Studio contain MedScan Technology to harvest information from the literature and to save this information in the Pathway Studio ResNet database ready for data analysis. MedScan is more than a web search engine. Indeed, the output of a Google search can be channeled into MedScan for example. Web searches, like Google, are excellent at finding items as a result of a query. A quick look at the output list usually locates the item for which you are looking. This approach however, is not well suited for information and knowledge gathering. Also, once information is gathered, where do you put it for later computational use? MedScan meets this challenge for the area of biomedical literature and biomedical online information. PubMed meets the needs for a central repository of biomedical literature. Researchers can go to PubMed and search for any topic and articles of interest, much like a web type of search. However, just like a web type of search, PubMed also provides a list of all the hits with a link to the articles. If a single article, or even just a few, are sought, this search approach is useful. Alternatively, MedScan will list all the articles of interest but additionally scans the text for relationships, highlights these relationships in the articles and then lists these relationships and the biological molecules and processes involved in the relationships in separate tables. The tables of relationships can be viewed graphically in Pathway Studio and can be saved into the ResNet database for use in experimental data analysis.

Proper citation: MedScan (RRID:SCR_003314) Copy   

•   Source: SciCrunch Registry

http://rostlab.org/services/nlsdb/

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

Proper citation: NLSdb: a database of nuclear localization signals (RRID:SCR_003273) Copy   

•   Source: SciCrunch Registry

http://abi.inf.uni-tuebingen.de/Services/MultiLoc2

An extensive high-performance subcellular protein localization prediction system that incorporates phylogenetic profiles and Gene Ontology terms to yield higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. A downloadable version of MultiLoc2 for local use is also available.

Proper citation: MultiLoc (RRID:SCR_003151) Copy   

•   Source: SciCrunch Registry

http://dip.doe-mbi.ucla.edu/

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.

Proper citation: Database of Interacting Proteins (DIP) (RRID:SCR_003167) Copy   

•   Source: SciCrunch Registry

http://bbid.irp.nia.nih.gov/

Database of images of putative biological pathways, macromolecular structures, gene families, and cellular relationships. It is of use to those who are working with large sets of genes or proteins using cDNA arrays, functional genomics, or proteomics. The rationale for this collection is that: # Except in a few cases, information on most biological pathways in higher eukaryotes is non-existent, incomplete, or conflicting. # Similar biological pathways differ by tissue context, developmental stages, stimulatory events, or for other complex reasons. This database allows comparisons of different variations of pathways that can be tested empirically. # The goal of this database is to use images created directly by biomedical scientists who are specialists in a particular biological system. It is specifically designed to NOT use average, idealized or redrawn pathways. It does NOT use pathways defined by computer algorithm or information search approaches. # Information on biological pathways in higher eukaryotes generally resides in the images and text of review papers. Much of this information is not easily accessible by current medical reference search engines. # All images are attributable to the original authors. All pathways or other biological systems described are graphic representations of natural systems. Each pathway is to be considered a work in progress. Each carries some degree of error or incompleteness. The end user has the ultimate responsibility to determine the scientific correctness and validity in their particular biological system. Image/pathway submissions are welcome.

Proper citation: Biological Biochemical Image Database (RRID:SCR_003474) Copy   

•   Source: SciCrunch Registry

http://pir.georgetown.edu/pirwww/dbinfo/pirsf.shtml

A SuperFamily classification system, with rules for functional site and protein name, to facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors. The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships. The PIRSF classification system is based on whole proteins rather than on the component domains; therefore, it allows annotation of generic biochemical and specific biological functions, as well as classification of proteins without well-defined domains. There are different PIRSF classification levels. The primary level is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). At a lower level are the subfamilies which are clusters representing functional specialization and/or domain architecture variation within the family. Above the homeomorphic level there may be parent superfamilies that connect distantly related families and orphan proteins based on common domains. Because proteins can belong to more than one domain superfamily, the PIRSF structure is formally a network. The FTP site provides free download for PIRSF.

Proper citation: PIRSF (RRID:SCR_003352) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pride/

Centralized, standards compliant, public data repository for proteomics data, including protein and peptide identifications, post-translational modifications and supporting spectral evidence. Originally it was developed to provide a common data exchange format and repository to support proteomics literature publications. This remit has grown with PRIDE, with the hope that PRIDE will provide a reference set of tissue-based identifications for use by the community. The future development of PRIDE has become closely linked to HUPO PSI. PRIDE encourages and welcomes direct user submissions of protein and peptide identification data to be published in peer-reviewed publications. Users may Browse public datasets, use PRIDE BioMart for custom queries, or download the data directly from the FTP site. PRIDE has been developed through a collaboration of the EMBL-EBI, Ghent University in Belgium, and the University of Manchester.

Proper citation: Proteomics Identifications (PRIDE) (RRID:SCR_003411) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Main_Page

Laboratory portal, including software, web-based tools, databases and data sets, related to their research that focuses on the development and application of biophysical and bioinformatics methods aimed at understanding the structural and energetic origins of protein-protein, protein-nucleic acid, and protein-membrane interactions. Their work includes fundamental theoretical research, the development of software tools, and applications to problems of biological importance. In this regard they maintain an active collaborative computational and experimental research program on the molecular basis of cell-cell adhesion. Other problems of current interest include protein structure prediction, the organization of protein sequence/structure space, the prediction of protein function based on protein structure, the structural origins of specificity in protein-DNA interactions, RNA function and, more generally, the electrostatic properties of biological macromolecules.

Proper citation: Honig Lab (RRID:SCR_003410) Copy   

•   Source: SciCrunch Registry

http://www.reactome.org

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.

Proper citation: Reactome (RRID:SCR_003485) Copy   

•   Source: SciCrunch Registry

http://mimi.ncibi.org/MimiWeb/main-page.jsp

MiMi Web gives you an easy to use interface to a rich NCIBI data repository for conducting your systems biology analyses. This repository includes the MiMI database, PubMed resources updated nightly, and text mined from biomedical research literature. The MiMI database comprehensively includes protein interaction information that has been integrated and merged from diverse protein interaction databases and other biological sources. With MiMI, you get one point of entry for querying, exploring, and analyzing all these data. MiMI provides access to the knowledge and data merged and integrated from numerous protein interactions databases and augments this information from many other biological sources. MiMI merges data from these sources with deep integration into its single database with one point of entry for querying, exploring, and analyzing all these data. MiMI allows you to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI displays results of your queries in easy-to-browse interfaces and provides you with workspaces to explore and analyze the results. Among these workspaces is an interactive network of protein-protein interactions displayed in Cytoscape and accessed through MiMI via a MiMI Cytoscape plug-in. MiMI gives you access to more information than you can get from any one protein interaction source such as: * Vetted data on genes, attributes, interactions, literature citations, compounds, and annotated text extracts through natural language processing (NLP) * Linkouts to integrated NCIBI tools to: analyze overrepresented MeSH terms for genes of interest, read additional NLP-mined text passages, and explore interactive graphics of networks of interactions * Linkouts to PubMed and NCIBI's MiSearch interface to PubMed for better relevance rankings * Querying by keywords, genes, lists or interactions * Provenance tracking * Quick views of missing information across databases. Data Sources include: BIND, BioGRID, CCSB at Harvard, cPath, DIP, GO (Gene Ontology), HPRD, IntAct, InterPro, IPI, KEGG, Max Delbreuck Center, MiBLAST, NCBI Gene, Organelle DB, OrthoMCL DB, PFam, ProtoNet, PubMed, PubMed NLP Mining, Reactome, MINT, and Finley Lab. The data integration service is supplied under the conditions of the original data sources and the specific terms of use for MiMI. Access to this website is provided free of charge. The MiMI data is queryable through a web services api. The MiMI data is available in PSI-MITAB Format. These files represent a subset of the data available in MiMI. Only UniProt and RefSeq identifiers are included for each interactor, pathways and metabolomics data is not included, and provenance is not included for each interaction. If you need access to the full MiMI dataset please send an email to mimi-help (at) umich.edu.

Proper citation: Michigan Molecular Interactions (RRID:SCR_003521) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/mimiplugin.html

The Cytoscape MiMI Plugin is an open source interactive visualization tool that you can use for analyzing protein interactions and their biological effects. The Cytoscape MiMI Plugin couples Cytoscape, a widely used software tool for analyzing bimolecular networks, with the MiMI database, a database that uses an intelligent deep-merging approach to integrate data from multiple well-known protein interaction databases. The MiMI database has data on 119,880 molecules, 330,153 interactions, and 579 complexes. By querying the MiMI database through Cytoscape you can access the integrated molecular data assembled in MiMI and retrieve interactive graphics that display protein interactions and details on related attributes and biological concepts. You can interact with the visualization by expanding networks to the next nearest neighbors and zooming and panning to relationships of interest. You also can perceptually encode nodes and links to show additional attributes through color, size and the visual cues. You can edit networks, link out to other resources and tools, and access information associated with interactions that has been mined and summarized from the research literature information through a biology natural language processing database (BioNLP) and a multi-document summarization system, MEAD. Additionally, you can choose sub-networks of interest and use SAGA, a graph matching tool, to match these sub-networks to biological pathways.

Proper citation: MiMI Plugin for Cytoscape (RRID:SCR_003424) Copy   

•   Source: SciCrunch Registry