Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
http://www.eurasnet.info/tools/asdatabases
It has been established with the intention of assembling in a central, publicly accessible site information about alternatively spliced genes, their products and expression patterns. Version 2.1 of ASDB consists of two divisions, ASDB(proteins) , which contains amino acid sequences, and ASDB(nucleotides) with genomic sequences.
SWISS-PROT uses two formats for description of alternative splicing Thus the protein sequences were selected from SWISS-PROT using full text search for both the words alternative splicing (usually in the CC lines) and varsplic (in the FT lines). In order to group proteins that could arise by alternative splicing of the same gene, we developed the clustering procedure. Two proteins were linked if they had a common fragment of at least 20 amino acids, and clusters were initially defined as maximum connected groups of linked proteins. It turned out that some clusters were chimeric, in the sense that they contained members of multi-gene families, but not alternatively spliced variants of one gene. Therefore the multiple alignments were subject to additional analysis aimed at detection of chimeric clusters.
Each cluster is represented by multiple alignment of its members constructed using CLUSTALW. The distribution of cluster size, representation of species and other relevant statistics of ASDB(proteins) can be accessed through the links below.
This processing covers the cases when alternatively spliced variants are described in separate SWISS-PROT entries. The other kinds of ASDB records, originating from the SWISS-PROT entries with the varsplic field in the feature table, usually describe the proteins that are not part of any cluster. In these cases, the information on the variable fragments of the several proteins which result from the alternative splicing of a single gene is contained in the entry itself. ASDB(proteins) entries are marked with different symbols to allow for easy differentiation among the three types: those proteins which are part of the ASDB clusters and the corresponding multialignments, those which have the information on different variants in the associated SWISS-PROT entries, and those for which the information on the variants is not available at the present time. ASDB contains internal links between entries and/or clusters, as well as external links to Medline, GenBank and SWISS-PROT entries.
The ASDB(nucleotides) division was generated by collecting all GenBank entries containing the words alternative splicing and further selection of those entries that contain complete gene sequences (all CDS fields are complete, i.e. they do not have continuation signs).
Sponsors: This work was supported by the Director, Office of Energy Research, Office of Biological and Environmental Research, of the US Department of Energy under Contract No. DE-ACO3-76SF00098. Additional support came from grants from the Russian Fund of Basic Research (99-04-48347), the Russian State Scientific Program Human Genome (65/99), and the Merck Genome Research Institute (244).
Proper citation: Alternative Splicing Database (RRID:SCR_007555) Copy
http://genolist.pasteur.fr/Colibri/
Database dedicated to the analysis of the genome of Escherichia coli. Its purpose is to collate and integrate various aspects of the genomic information from E. coli, the paradigm of Gram-negative bacteria. Colibri provides a complete dataset of DNA and protein sequences derived from the paradigm strain E. coli K-12, linked to the relevant annotations and functional assignments. It allows one to easily browse through these data and retrieve information, using various criteria (gene names, location, keywords, etc.). The data contained in Colibri originates from two major sources of information, the reference genomic DNA sequence from the E. coli Genome Project and the feature annotations from the EcoGene data collection., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Colibri (RRID:SCR_007606) Copy
http://ppa.bcf.ku.edu/DB_PABP/
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. DB-PABP is an attempt to document the publicly available experimentally determined polyanion binding proteins (PABPs). The purpose of the database is to provide life scientists who are interested in PA/PABP interactions with a comprehensive data repository, as well as computer scientists with a publicly available dataset to perform knowledge discovery and datamining studies. The database is manually curated. It uses protein annotations from NCBI protein database and literature information is retrieved from PubMed. Whenever applicable, links to NCBI protein database and PubMed are provided so users may access additional information available in these public databases.
Proper citation: DB-PABP: a database of polyanion binding proteins (RRID:SCR_007603) Copy
http://mips.gsf.de/genre/proj/ustilago/
The MIPS Ustilago maydis Genome Database aims to present information on the molecular structure and functional network of the entirely sequenced, filamentous fungus Ustilago maydis. The underlying sequence is the initial release of the high quality draft sequence of the Broad Institute. The goal of the MIPS database is to provide a comprehensive genome database in the Genome Research Environment in parallel with other fungal genomes to enable in depth fungal comparative analysis. The specific aims are to: 1. Generate and assemble Whole Genome Shotgun sequence reads yielding 10X coverage of the U. maydis genome 2. Integrate the genomic sequence assembly with physical maps generated by Bayer CropScience 3. Perform automated annotation of the sequence assembly 4. Align the strain 521 assembly with the FB1 assembly provided by Exelixis 5. Release the sequence assembly and results of our annotation and analysis to public Ustilago maydis is a basidiomycete fungal pathogen of maize and teosinte. The genome size is approximately 20 Mb. The fungus induces tumors on host plants and forms masses of diploid teliospores. These spores germinate and form haploid meiotic products that can be propagated in culture as yeast-like cells. Haploid strains of opposite mating type fuse and form a filamentous, dikaryotic cell type that invades plant tissue to reinitiate infection. Ustilago maydis is an important model system for studying pathogen-host interactions and has been studied for more than 100 years by plant pathologists. Molecular genetic research with U. maydis focuses on recombination, the role of mating in pathogenesis, and signaling pathways that influence virulence. Recently, the fungus has emerged as an excellent experimental model for the molecular genetic analysis of phytopathogenesis, particularly in the characterization of infection-specific morphogenesis in response to signals from host plants. Ustilago maydis also serves as an important model for other basidiomycete plant pathogens that are more difficult to work with in the laboratory, such as the rust and bunt fungi. Genomic sequence of U. maydis will also be valuable for comparative analysis of other fungal genomes, especially with respect to understanding the host range of fungal phytopathogens. The analysis of U. maydis would provide a framework for studying the hundreds of other Ustilago species that attack important crops, such as barley, wheat, sorghum, and sugarcane. Comparisons would also be possible with other basidiomycete fungi, such as the important human pathogen C. neoformans. Commercially, U. maydis is an excellent model for the discovery of antifungal drugs. In addition, maize tumors caused by U. maydis are prized in Hispanic cuisine and there is interest in improving commercial production. The complete putative gene set of the Broad Institute''s second release is loaded into the database and in addition all deviating putative genes from a putative gene set produced by MIPS with different gene prediction parameters are also loaded. The complete dataset will then be analysed, gene predictions will be manually corrected due to combined information derived from different gene prediction algorithms and, more important, protein and EST comparisons. Gene prediction will be restricted to ORFs larger than 50 codons; smaller ORFs will be included only if similarities to other proteins or EST matches confirm their existence or if a coding region was postulated by all prediction programs used. The resulting proteins will be annotated. They will be classified according to the MIPS classification catalogue receiving appropriate descriptions. All proteins with a known, characterized homolog will be automatically assigned to functional categories using the MIPS functional catalog. All extracted proteins are in addition automatically analysed and annotated by the PEDANT suite.
Proper citation: MIPS Ustilago maydis Database (RRID:SCR_007563) Copy
http://source.rcsb.org/jfatcatserver/ceHome.jsp
CE is a databases of alignments for all polypeptide chains. A representative set of proteins is available and kept current with the PDB, a method for calculating pairwise structure alignments. CE aligns two polypeptide chains using characteristics of their local geometry as defined by vectors between C alpha positions. Matches are termed aligned fragment pairs (AFPs). Heuristics are used in defining a set of optimal paths joining AFPs with gaps as needed. The path with the best RMSD is subject to dynamic programming to achieve an optimal alignment. For specific families of proteins additional characteristics are used to weight the alignment. Complete details are described in the paper (PDF format). Databases of alignments for all polypeptide chains and a representative set of proteins is available and kept current with the PDB
Proper citation: Combinatorial Extension (CE) (RRID:SCR_007585) Copy
http://caps.ncbs.res.in/gendis/home.html
Genomic Distribution of structural Superfamilies identifies and classifies evolutionary related proteins at the superfamily level in whole genome databases. GenDiS has been curated in direct correspondence with SCOP and represents 4001 highly resolved domains in 1194 structural superfamilies across protein sequence databases. Sequences showing reliable homology to entries in SCOP and PASS2 databases have been obtained from the non-redundant protein sequence database and aligned. Similar alignments of the superfamily members are provided in the genome level. GenDiS provides a platform for cross genome comparison at the superfamily level. GenDis relates proteins sequence information across all strata of taxonomy. One may navigate through the database to obtain structural homologues across different levels in taxonomic classification. The nomenclature of the various genomes and their hierarchy is in direct correspondence with the taxonomy database maintained at the NCBI. Sequence homologues for the various structural members are obtained from the non-redundant protein sequence database employing sensitive sequence search methods. Multiple approaches such as PSI-BLAST, HMMsearch of the HMMer suite and an interacting motif constrained PHI-BLAST have been employed to identify homologues in the sequence databases.
Proper citation: Genomic Distribution of structural Superfamilies (RRID:SCR_007670) Copy
http://caps.ncbs.res.in/imotdb/
Comprehensive collection of spatially interacting motifs in proteins. Interacting motif database lists interacting motifs that are identified for all structural entries in PDB. Conserved patterns or finger prints are identified for individual structural entries and also grouped together for reporting common motifs shared among all superfamily members.
Proper citation: Database of Spatially Interacting Motifs in Proteins (RRID:SCR_007735) Copy
http://mendel.gene.cwru.edu/adamslab/cgi-bin/paml/pbrowser.py
THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It provides access to the results of tests for positive selection in 14,000 human genes. Multiple alignments of protein-coding regions of genes from human and other mammals were extracted from whole-genome alignments available from UC-Santa Cruz. Each gene was analyzed using the maximum likelihood tests of selection using PAML. Branch, site, and branch+site tests were performed, each with at least one matching null model.
Proper citation: Human PAML Browser (RRID:SCR_007715) Copy
dbPTM is a database that compiles information on protein post-translational modifications (PTM) such as the modified sites, solvent accessibility of surrounding amino acids, protein secondary and tertiary structures, protein domains, and protein variations. The version 2.0 of dbPTM integrates the experimentally validated PTM sites with referable literatures from Swiss-Prot, Phospho.ELM, O-GLYCBASE, and UbiProt. In all of the collected PTM information, about 25 types of PTM with enough experimentally validated sites are trained the profile hidden Markov models (HMMs) to detect the potential PTM sites with 100% specificity against Swiss-Prot proteins. To help users investigating more detail in each type of PTM, the substrate peptide specificity such as positional amino acid frequency, solvent accessibility and secondary structure surrounding the modified sites are also provided. Moreover, the information of orthologous protein clusters is provided to users for analyzing whether the PTM sites located in the evolutionary conserved regions or not., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: dbPTM: An informational repository of proteins and post-translational modifications (RRID:SCR_007619) Copy
http://firedb.bioinfo.cnio.es/
A database of Protein Data Bank structures, ligands and annotated functional site residues. The database can be accessed by PDB codes or UniProt accession numbers as well as keywords. FireDB contains information on every chemical compound in the PDB, including their descriptions, the PDB structures in which the compounds are found and the amino acids that are in contact with the ligand.
Proper citation: FireDB (RRID:SCR_007655) Copy
FCP is a publicly accessible web tool dedicated to analyzing the current state and trends of available proteome structures along the classification schemes of enzymes and nuclear receptors. It offers both graphical and quantitative data on the degree of functional coverage in that portion of the proteome by existing structures and on the bias observed in the distribution of those structures among proteins. Users can choose to search the website based on structures or ligands, and can also sort by enzyme or receptor. Users can also view data based on structural and population (species) filters.
Proper citation: Functional Coverage of the Proteome (RRID:SCR_007654) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 15,2025. Human protein knowledge platform. Knowledge platform for human proteins selects and filters high throughput data pertinent to human proteins from UniProtKB. Extends UniProtKB/Swiss-Prot annotations for human proteins to include several new data types.
Proper citation: neXtProt (RRID:SCR_008911) Copy
http://harvester.fzk.de/harvester/
Harvester is a Web-based tool that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. It is a meta search engine for gene and protein information. It searches 16 major databases and prediction servers and combines the results on pregenerated HTML pages. In this way Harvester can provide comprehensive gene-protein information from different servers in a convenient and fast manner. As full text meta search engine, similar to Google trade mark, Harvester allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. Sponsors: This work has been supported by the BMBF with grants 01GR0101 and 01KW0013.
Proper citation: Bioinformatic Harvester IV (beta) at Karlsruhe Institute of Technology (RRID:SCR_008017) Copy
Database of ascidian embryonic development at the level of the genome (cis-regulatory sequences, gene expression, protein annotation), of the cell (morphology, fate, induction, lineage) or of the whole embryo (anatomy, morphogenesis). Currently, four organism models are described in Aniseed: Ciona intestinalis, Ciona savignyi, Halocynthia roretzi and Phallusia mammillata.
This version supports four sets of Ciona intestinalis transcript models: JGI v1.0, KyotoGrail 2005, KH and ENSEMBL, all functionally annotated, and grouped into Aniseedv3.0 gene models. Users can explore their expression profiles during normal or manipulated development, access validated cis-regulatory regions, get the molecular tools used to assay gene function, or all articles related to the function, or regulation of a given gene. Known transcriptional regulators and targets are listed for each gene, as are the gene regulatory networks acting in individual anatomical territories.
ANISEED is a community tool, and the direct involvement of external contributors is important to optimize the quality of the submitted data. Virtual embryo: The 3D Virtual embryo is available to download in the download section of the website.
Proper citation: Ascidian Network for InSitu Expression and Embryological Data (RRID:SCR_013030) Copy
https://github.com/davidemms/OrthoFinder
Software Python application for comparative genomics analysis. Finds orthogroups and orthologs, infers rooted gene trees for all orthogroups and identifies all of gene duplcation events in those gene trees, infers rooted species tree for species being analysed and maps gene duplication events from gene trees to branches in species tree, improves orthogroup inference accuracy. Runs set of protein sequence files, one per species, in FASTA format.
Proper citation: OrthoFinder (RRID:SCR_017118) Copy
Software application to organize and store in structured format signaling information published in scientific literature. Information is stored as binary causative relationships between biological entities and can be represented graphically as activity flow. Each relationship is linked to literature reporting experimental evidence. Each node is annotated with chemical inhibitors that modulate its activity. Signaling information is mapped to human proteome. SIGNOR 2.0 stores manually annotated causal relationships between proteins and other biologically relevant entities including chemicals, phenotypes, complexes, etc with compliance to FAIR data principles.
Proper citation: SIGNOR (RRID:SCR_018485) Copy
The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.
Proper citation: NBDC - National Bioscience Database Center (RRID:SCR_000814) Copy
Project portal's database of protein-ligand data sets provided by pharmaceutical partners that provide atomic details of drug mechanisms that will be used to improve computer-aided drug-design methods and thus accelerate drug discovery. The project aims to help companies release the high-quality data they have generated, which has incredible value to researchers working to improve methods of computer-aided drug discovery. Everyone stands to benefit from the ability to develop new medications more quickly and inexpensively. What computational chemists globally are trying to do is to make faster, more accurate, more predictive programs to speed up the process. Part of their mission is to engage the community in these challenges to test newly developed predictive algorithms.
Proper citation: Drug Design Data Resource (RRID:SCR_000497) Copy
http://www.ncbi.nlm.nih.gov/guide/sitemap/
The National Center for Biotechnology Information''s listing of resources. Sort by alphabetical character, Databases, Downloads, Submissions, Tools and How-To; or by Topic: Chemicals & Bioassays; Data & Software; DNA & RNA; Domains & Structures; Genes & Expression; Genetics & Medicine; Genomes & Maps; Homology; Literature; Proteins; Sequence Analysis; Taxonomy; Training & Tutorials; Variation.
Proper citation: NCBI Resource List (RRID:SCR_005628) Copy
Institute to advance genomics in support of the DOE missions related to clean energy generation and environmental characterization and cleanup. Supported by the DOE Office of Science, the DOE JGI unites the expertise at Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, and the HudsonAlpha Institute for Biotechnology. The facility provides integrated high-throughput sequencing and computational analysis that enable systems-based scientific approaches to these challenges.
Proper citation: DOE Joint Genome Institute (RRID:SCR_003045) Copy
Can't find your Tool?
We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the RRID Resources search. From here you can search through a compilation of resources used by RRID and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that RRID has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on RRID then you can log in from here to get additional features in RRID such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into RRID you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the sources that were queried against in your search that you can investigate further.
Here are the categories present within RRID that you can filter your data on
Here are the subcategories present within this category that you can filter your data on
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.