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http://www.lipidmaps.org/data/structure/

Collection of structures and annotations of biologically relevant lipids that contains unique lipid structures. Structures of lipids from : LIPID MAPS Consortium's core laboratories and partners; lipids identified by LIPID MAPS experiments; biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources; novel lipids submitted to peer-reviewed journals; and computationally generated structures for appropriate classes. All the lipid structures adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet). All lipids have been classified using the LIPID MAPS Lipid Classification System. Each lipid structure has been assigned a LIPID MAPS ID (LM_ID) which reflects its position in the classification hierarchy. In addition to a classification-based retrieval of lipids, users can search using either text-based or structure-based search options.

Proper citation: LIPID MAPS Structure Database (RRID:SCR_003817) Copy   

•   Source: SciCrunch Registry

http://life.ccs.miami.edu/life/

LIFE search engine contains data generated from LINCS Pilot Phase, to integrate LINCS content leveraging semantic knowledge model and common LINCS metadata standards. LIFE makes LINCS content discoverable and includes aggregate results linked to Harvard Medical School and Broad Institute and other LINCS centers, who provide more information including experimental conditions and raw data. Please visit LINCS Data Portal.

Proper citation: LINCS Information Framework (RRID:SCR_003937) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC165503/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. Designed to capture protein function, defined at molecular level as set of other molecules with which protein interacts or reacts along with molecular outcome. Archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display.

Proper citation: BIND (RRID:SCR_003576) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/mmgp/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. Database providing access and limited analysis to the MMGP portal data sets. These include the MMRC funded reference array comparative genomic hybridization (aCGH) and gene expression data and additional public multiple myeloma datasets. The MMGP will be updated with new features such as additional data and analysis tools as they become available.

Proper citation: Multiple Myeloma Genomics Portal (RRID:SCR_003722) Copy   

•   Source: SciCrunch Registry

http://caps.ncbs.res.in/3dswap/index.html

Curated knowledegbase of protein structures that are reported to be involved in 3-dimensional domain swapping. 3DSwap provides literature curated information and structure related information about 3D domain swapping in proteins. Information about swapping, hinge region, swapped region, extent of swapping, etc. are extracted from original research publications after extensive literature curation.

Proper citation: 3DSwap (RRID:SCR_004133) Copy   

•   Source: SciCrunch Registry

http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

•   Source: SciCrunch Registry

http://www.cas.org/expertise/cascontent/registry/index.html

CAS REGISTRY, the gold standard for substance information, is the only integrated, comprehensive source of chemical information from a full range of disclosed material including patents, journals, and reputable web sources. When you need to positively identify a chemical substance, you can rely on the authoritative source for chemical names and structures of CAS REGISTRY. CAS databases are curated and quality-controlled by CAS scientists, and recognized by chemical and pharmaceutical companies, universities, government organizations, and patent offices around the world as authoritative. By combining these databases with advanced search and analysis technologies (SciFinder, STN, and Science IP products and services), CAS delivers the most current, complete, and cross-linked secure digital information environment for scientific discovery. You can identify your substance of interest by its CAS Registry Number, which is the best way to identify a substance, regardless of what name you have for it. You can also use CAS REGISTRY to locate * literature references to the substance * experimental and predicted property data (boiling and melting points, etc.) * commercial availability * preparative methods * spectra * regulatory information from international sources

Proper citation: CAS REGISTRY (RRID:SCR_004558) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/dbfetch/dbfetch

Dbfetch is an acronym for database fetch. Dbfetch provides an easy way to retrieve entries from various databases at the EBI in a consistent manner and allows you to retrieve up to 50 entries at a time from various up-to-date biological databases. It can be used from any browser as well as well as within a web-aware scripting tool that uses wget, lynx or similar. From the browser, follow these instructions... * Select a database: If you are using the first form to paste your search items: choose a database name from this form. If you are using the second form to upload your search items: the database name is included at the beginning of each line line of the upload file followed by a colon. * Enter search terms: These MUST BE in the appropriate database format, up to 200 search items can be queried in one run. If you are using the first form: separate search items with a comma or space. If you are using the second form: separate search items with a new line. * Choose an output format: Here you can choose the simpler fasta format, or the databases'''' default format for the chosen database. * Style: You can get your results as text or html. * Retrieve! - You are now ready to fetch your results, by pressing the Retrieve button.

Proper citation: EBI Dbfetch (RRID:SCR_004393) Copy   

•   Source: SciCrunch Registry

http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/sites/entrez?db=geo

The GEO Profiles database stores gene expression profiles derived from curated GEO DataSets. Each Profile is presented as a chart that displays the expression level of one gene across all Samples within a DataSet. Experimental context is provided in the bars along the bottom of the charts making it possible to see at a glance whether a gene is differentially expressed across different experimental conditions. Profiles have various types of links including internal links that connect genes that exhibit similar behaviour, and external links to relevant records in other NCBI databases. GEO Profiles can be searched using many different attributes including keywords, gene symbols, gene names, GenBank accession numbers, or Profiles flagged as being differentially expressed.

Proper citation: Entrez GEO Profiles (RRID:SCR_004584) Copy   

•   Source: SciCrunch Registry

http://data.linkedct.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023.The Linked Clinical Trials (LinkedCT) project aims at publishing the first open Semantic Web data source for clinical trials data. The data exposed by LinkedCT is generated by (1) transforming existing data sources of clinical trials into RDF, and (2) discovering links between the records in the trials data and several other data sources. You may download static data dumps. The LinkedCT data space is published according to the principles of publishing Linked Data. These principles greatly enhance adaptability and usability of data on the web. Each entity in LinkedCT is identified by a unique HTTP dereferenceable Uniform Resource Identifier (URI). When the URI is looked up, related RDF statements about the entity is returned in HTML or RDF/XML based on the user''s agent. Moreover, a SPARQL endpoint is provided as the standard access method for RDF data.

Proper citation: LinkedCT (RRID:SCR_004585) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://zfin.org/action/antibody/search

The ZFIN Antibody Database is a database of zebrafish gene expression antibodies.

Proper citation: ZFIN Antibody Database (RRID:SCR_004729) Copy   

•   Source: SciCrunch Registry

http://pfam.xfam.org/

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

Proper citation: Pfam (RRID:SCR_004726) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/mapview/map_search.cgi?taxid=7165

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. A database for the Anopheles gambiae str. PEST genome that was sequenced using a whole genome shotgun approach. The database aims to contribute to the understanding of mosquito genome structure and organization and will assist the development of malaria control strategies and improved anti-malarial drugs and vaccines. Sequences were generated and assembled into contigs for submission to GenBank.

Proper citation: Anopheles gambiae (African malaria mosquito) genome view (RRID:SCR_004402) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/taxonomy/

NEWT is the taxonomy database maintained by the UniProt group. It integrates taxonomy data compiled in the NCBI database and data specific to the UniProt Knowledgebase. Browse by hierarchy, List all, or Complete proteomes. Organisms are classified in a hierarchical tree structure. Our taxonomy database contains every node (taxon) of the tree. UniProtKB taxonomy data is manually curated: next to manually verified organism names, we provide a selection of external links, organism strains and viral host information. Species with protein sequences stored in the UniProt Knowledgebase are named according to UniProt nomenclature. We endeavour to maintain a list of manually curated species names for which protein sequence data is available. In particular, we have adopted a systematic convention for naming viral and bacterial strains and isolates. Links to external sites are chosen by the UniProt taxonomy team and show pictures and various scientific data of interest (taxonomy, biology, physiology,...).

Proper citation: NEWT (RRID:SCR_004477) Copy   

•   Source: SciCrunch Registry

http://selectome.unil.ch/

Database of positive selection based on a rigorous branch-site specific likelihood test. Positive selection is detected using CODEML on all branches of animal gene trees.

Proper citation: Selectome: a Database of Positive Selection (RRID:SCR_004542) Copy   

•   Source: SciCrunch Registry

http://dcv.uhnres.utoronto.ca/SCRIPDB/search/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2, 2025. Database of chemicals and reactions inside of US patents (2001 - 2011). SCRIPDB provides the full original patent text, reactions and relationships described within any individual patent, in addition to the molecular files common to structural databases. The patent literature is a rich catalog of biologically relevant chemicals; many public and commercial molecular databases contain the structures disclosed in patent claims. However, patents are an equally rich source of metadata about bioactive molecules, including mechanism of action, disease class, homologous experimental series, structural alternatives, or the synthetic pathways used to produce molecules of interest. Unfortunately, this metadata is discarded when chemical structures are deposited separately in databases. SCRIPDB is a chemical structure database designed to make this metadata accessible. The SCRIPDB information is valuable in medical text mining, chemical image analysis, reaction extraction and in silico pharmaceutical lead optimization. SCRIPDB may be searched by exact chemical structure, substructure or molecular similarity and the results may be restricted to patents describing synthetic routes.

Proper citation: SCRIPDB (RRID:SCR_008922) Copy   

•   Source: SciCrunch Registry

http://lsid.tdwg.org/

A web based life sciences identifier (LSID) resolution service allows you to view the data and metadata of an LSID with a web browser. This service will display the metadata, formatted as a standard webpage, for any LSID.

Proper citation: LSID Web Resolver (RRID:SCR_009737) Copy   

•   Source: SciCrunch Registry

http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry