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http://ceolas.org/VL/mo/

Catalog of internet resources relating to biological model organisms, and is part of the Biosciences area of the Virtual Library project. The main Model Organisms Library discussed in this website are: * E. coli (bacterium) * Yeasts (Saccharomyces cerevisiae, and other species) * Dictyostelium discoideum (slime mold) * Drosophila melanogaster (fruit fly) * Xenopus laevis (African clawed frog) Many aspects of biology are similar in most or all organisms, but it is frequently much easier to study particular aspects in particular organisms - for instance, genetics is easier in small organisms that breed quickly, and very difficult in humans! The most popular model organisms have strong advantages for experimental research, and become even more useful when other scientists have already worked on them, discovering techniques, genes and other useful information.

Proper citation: The WWW Virtual Library: Model Organisms (RRID:SCR_007007) Copy   

•   Source: SciCrunch Registry

https://plantcyc.org/databases/aracyc/15.0

Curated species-specific database present at the Plant Metabolic Network. It has a large number of experimentally supported enzymes and metabolic pathways, but it also houses a substantial number of computationally predicted enzymes and pathways.

Proper citation: AraCyc (RRID:SCR_008109) Copy   

•   Source: SciCrunch Registry

http://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

•   Source: SciCrunch Registry

http://www.genenetwork.org/

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.

Proper citation: GeneNetwork (RRID:SCR_002388) Copy   

•   Source: SciCrunch Registry

http://uwaging.org/genesdb/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 17,2023. A database of genes and interventions connected with aging phenotypes including those with respect to their effects on life-span or age-related neurological diseases. Information includes: organism, aging phenotype, allele type, strain, gene function, phenotypes, mutant, and homologs. If you know of published data (or your own unpublished data that you'd like to share) not currently in the database, please use the Submit a Gene/Intervention link.

Proper citation: Aging Genes and Interventions Database (RRID:SCR_002701) 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://clipserve.clip.ubc.ca/topfind

An integrated knowledgebase focused on protein termini, their formation by proteases and functional implications. It contains information about the processing and the processing state of proteins and functional implications thereof derived from research literature, contributions by the scientific community and biological databases. It lists more than 120,000 N- and C-termini and almost 10,000 cleavages. TopFIND is a resource for comprehensive coverage of protein N- and C-termini discovered by all available in silico, in vitro as well as in vivo methodologies. It makes use of existing knowledge by seamless integration of data from UniProt and MEROPS and provides access to new data from community submission and manual literature curating. It renders modifications of protein termini, such as acetylation and citrulination, easily accessible and searchable and provides the means to identify and analyse extend and distribution of terminal modifications across a protein. The data is presented to the user with a strong emphasis on the relation to curated background information and underlying evidence that led to the observation of a terminus, its modification or proteolytic cleavage. In brief the protein information, its domain structure, protein termini, terminus modifications and proteolytic processing of and by other proteins is listed. All information is accompanied by metadata like its original source, method of identification, confidence measurement or related publication. A positional cross correlation evaluation matches termini and cleavage sites with protein features (such as amino acid variants) and domains to highlight potential effects and dependencies in a unique way. Also, a network view of all proteins showing their functional dependency as protease, substrate or protease inhibitor tied in with protein interactions is provided for the easy evaluation of network wide effects. A powerful yet user friendly filtering mechanism allows the presented data to be filtered based on parameters like methodology used, in vivo relevance, confidence or data source (e.g. limited to a single laboratory or publication). This provides means to assess physiological relevant data and to deduce functional information and hypotheses relevant to the bench scientist. TopFIND PROVIDES: * Integration of protein termini with proteolytic processing and protein features * Displays proteases and substrates within their protease web including detailed evidence information * Fully supports the Human Proteome Project through search by chromosome location CONTRIBUTE * Submit your N- or C-termini datasets * Contribute information on protein cleavages * Provide detailed experimental description, sample information and raw data

Proper citation: TopFIND (RRID:SCR_008918) Copy   

•   Source: SciCrunch Registry

http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

•   Source: SciCrunch Registry

https://www.cpib.ac.uk/tools-resources/software/roottrace/

Software tool which allows the automatic and high throughput measure of root length, as well as extra associated measures such as curvature. The user must supply start points for each root, and exemplar patches of nearby background. The software will then trace the main root to the tip, in every image in a timeseries, and record the results.

Proper citation: RootTrace (RRID:SCR_015585) Copy   

•   Source: SciCrunch Registry

https://github.com/lucventurini/mikado/

Mikado is a lightweight Python3 pipeline whose purpose is to facilitate the identification of expressed loci from RNA-Seq data * and to select the best models in each locus.

Proper citation: Mikado (RRID:SCR_016159) Copy   

•   Source: SciCrunch Registry

http://www.arabidopsis.org

Database of genetic and molecular biology data for the model higher plant Arabidopsis thaliana. Data available includes the complete genome sequence along with gene structure, gene product information, metabolism, gene expression, DNA and seed stocks, genome maps, genetic and physical markers, publications, and information about the Arabidopsis research community. Gene product function data is updated every two weeks from the latest published research literature and community data submissions. Gene structures are updated 1-2 times per year using computational and manual methods as well as community submissions of new and updated genes. TAIR also provides extensive linkouts from data pages to other Arabidopsis resources. The data can be searched, viewed and analyzed. Datasets can also be downloaded. Pages on news, job postings, conference announcements, Arabidopsis lab protocols, and useful links are provided.

Proper citation: TAIR (RRID:SCR_004618) Copy   

•   Source: SciCrunch Registry

http://mitominer.mrc-mbu.cam.ac.uk/

A database of mitochondrial proteomics data. It includes two sets of proteins: the MitoMiner Reference Set, which has 10477 proteins from 12 species; and MitoCarta, which has 2909 proteins from mouse and human mitochondrial proteins. MitoMiner provides annotation from the Gene Ontology (GO) and UniProt databases. This reference set contains all proteins that are annotated by either of these resources as mitochondrial in any of the species included in MitoMiner. MitoMiner data via is available via Application Programming Interface (API). The client libraries are provided in Perl, Python, Ruby and Java.

Proper citation: MitoMiner (RRID:SCR_001368) Copy   

•   Source: SciCrunch Registry

http://biogps.org/

An extensible and customizable gene annotation portal that emphasizes community extensibility and user customizability. It is a complete resource for learning about gene and protein function. Community extensibility reflects a belief that any BioGPS user should be able to add new content to BioGPS using the simple plugin interface, completely independently of the core developer team. User customizability recognizes that not all users are interested in the same set of gene annotation data, so the gene report layouts enable each user to define the information that is most relevant to them. Currently, BioGPS supports eight species: Human (Homo sapiens), Mouse (Mus musculus), Rat (Rattus norvegicus), Fruitfly (Drosophila melanogaster), Nematode (Caenorhabditis elegans), Zebrafish (Danio rerio), Thale-cress (Arabidopsis thaliana), Frog (Xenopus tropicalis), and Pig (Sus scrofa). BioGPS presents data in an ortholog-centric format, which allows users to display mouse plugins next to human ones. Our data for defining orthologs comes from NCBI's HomoloGene database.

Proper citation: BioGPS: The Gene Portal Hub (RRID:SCR_006433) Copy   

•   Source: SciCrunch Registry

http://datf.cbi.pku.edu.cn/

Database that collects all arabidopsis transcription factors (totally 1922 Loci; 2290 Gene Models) and classifies them into 64 families. It uses not only locus (gene), but also gene model (transcript, protein) and the detail information is for each gene model not for locus. It adds multiple alignment of the DNA-binding domain of each family, Neighbor-Joining phylogenetic tree of each family, the GO annotation, homolog with the Database of Rice Transcription Factors (DRTF). It also keeps old information items such as the unique cloned and sequenced information of about 1200 transcription factors, protein domains, 3D structure information with BLAST hits against PDB, predicted Nuclear Location Signals, UniGene information, as well as links to literature reference.

Proper citation: Database of Arabidopsis Transcription Factors (RRID:SCR_007101) Copy   

•   Source: SciCrunch Registry

http://spliceosomedb.ucsc.edu/

A database of proteins and RNAs that have been identified in various purified splicing complexes. Various names, orthologs and gene identifiers of spliceosome proteins have been cataloged to navigate the complex nomenclature of spliceosome proteins. Links to gene and protein records are also provided for the spliceosome components in other databases. To navigate spliceosome assembly dynamics, tools were created to compare the association of spliceosome proteins with complexes that form at specific stages of spliceosome assembly based on a compendium of mass spectrometry experiments that identified proteins in purified splicing complexes.

Proper citation: Spliceosome Database (RRID:SCR_002097) Copy   

•   Source: SciCrunch Registry

http://www.germonline.org/

Cross-species microarray expression database focusing on high-throughput expression data relevant for germline development, meiosis and gametogenesis as well as the mitotic cell cycle. The database contains a unique combination of information: 1) High-throughput expression data obtained with whole-genome high-density oligonucleotide microarrays (GeneChips). 2) Sample annotation (mouse over the sample name and click on it) using the Multiomics Information Management and Annotation System (MIMAS 3.0). 3) In vivo protein-DNA binding data and protein-protein interaction data (available for selected species). 4) Genome annotation information from Ensembl version 50. 5) Orthologs are identified using data from Ensembl and OMA and linked to each other via a section in the report pages. The portal provides access to the Saccharomyces Genomics Viewer (SGV) which facilitates online interpretation of complex data from experiments with high-density oligonucleotide tiling microarrays that cover the entire yeast genome. The database displays only expression data obtained with high-density oligonucleotide microarrays (GeneChips)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: GermOnline (RRID:SCR_002807) Copy   

•   Source: SciCrunch Registry

http://hint.yulab.org/

A database of high-quality protein-protein interactions in different organisms.

Proper citation: HINT (RRID:SCR_002762) Copy   

•   Source: SciCrunch Registry

http://pgsb.helmholtz-muenchen.de/plant/athal/index.jsp

Repository for genome sequence data in the European Scientists Sequencing Arabidopsis (ESSA) project, part of the Arabidopsis Genome Initiative. It is moving towards becoming an integrated biological knowledge resource by integrating diverse data, tools, query and visualization capabilities. The aim is to create a comprehensive resource for Arabidopsis as a model that can then be used to transfer knowledge onto sequences from other species, including crop plants.

Proper citation: MIPS Arabidopsis thaliana Database (RRID:SCR_003088) Copy   

•   Source: SciCrunch Registry

http://ppdb.agr.gifu-u.ac.jp/ppdb/cgi-bin/index.cgi

A plant promoter database that provides information on transcription start sites (TSSs), core promoter structure and regulatory element groups (REGs) as putative and comprehensive transcriptional regulatory elements. Microarray data-based predictions have been appended as REG annotations which inform their putative physiological roles.

Proper citation: PPDB: Plant Promoter Database (RRID:SCR_003395) Copy   

•   Source: SciCrunch Registry

http://organelledb.lsi.umich.edu/

Database of organelle proteins, and subcellular structures / complexes from compiled protein localization data from organisms spanning the eukaryotic kingdom. All data may be downloaded as a tab-delimited text file and new localization data (and localization images, etc) for any organism relevant to the data sets currently contained in Organelle DB is welcomed. The data sets in Organelle DB encompass 138 organisms with emphasis on the major model systems: S. cerevisiae, A. thaliana, D. melanogaster, C. elegans, M. musculus, and human proteins as well. In particular, Organelle DB is a central repository of yeast protein localization data, incorporating results from both previous and current (ongoing) large-scale studies of protein localization in Saccharomyces cerevisiae. In addition, we have manually curated several recent subcellular proteomic studies for incorporation in Organelle DB. In total, Organelle DB is a singular resource consolidating our knowledge of the protein composition of eukaryotic organelles and subcellular structures. When available, we have included terms from the Gene Ontologies: the cellular component, molecular function, and biological process fields are discussed more fully in GO. Additionally, when available, we have included fluorescent micrographs (principally of yeast cells) visualizing the described protein localization. Organelle View is a visualization tool for yeast protein localization. It is a visually engaging way for high school and undergraduate students to learn about genetics or for visually-inclined researchers to explore Organelle DB. By revealing the data through a colorful, dimensional model, we believe that different kinds of information will come to light.

Proper citation: Organelle DB (RRID:SCR_007837) Copy   

•   Source: SciCrunch Registry