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http://webdocs.cs.ualberta.ca/~bioinfo/PA/Sub/

Web server specialized to predict the subcellular localization of proteins using established machine learning techniques.

Proper citation: Proteome Analyst Specialized Subcellular Localization Server (RRID:SCR_003143) Copy   

•   Source: SciCrunch Registry

http://www.phi-base.org/

Database that catalogs experimentally verified pathogenicity, virulence and effector genes from fungal, Oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. It is an invaluable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention. In collaboration with the FRAC team, it also includes antifungal compounds and their target genes. Each entry is curated by domain experts and is supported by strong experimental evidence (gene disruption experiments, STM etc), as well as literature references in which the original experiments are described. Each gene is presented with its nucleotide and deduced amino acid sequence, as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, genes have been annotated using controlled vocabularies and links to external sources (Gene Ontology terms, EC Numbers, NCBI taxonomy, EMBL, PubMed and FRAC).

Proper citation: PHI-base (RRID:SCR_003331) Copy   

•   Source: SciCrunch Registry

http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/WegoLoc

Data analysis service that predicts protein subcellular localizations of animal, fungal, plant, and human proteins based on sequence similarity and gene ontology information.

Proper citation: WegoLoc (RRID:SCR_001402) Copy   

•   Source: SciCrunch Registry

http://www.arabidopsisreactome.org

Curated database of core pathways and reactions in plant biology that covers biological pathways ranging from the basic processes of metabolism to high-level processes such as cell cycle regulation. While it is targeted at Arabidopsis pathways, it also includes many biological events from other plant species. This makes the database relevant to the large number of researchers who work on other plants. Arabidopsis Reactome currently contains both in-house curated pathways as well as imported pathways from AraCyc and KEGG databases. All the curated information is backed up by its provenance: either a literature citation or an electronic inference based on sequence similarity. Their ontology ensures that the various events are linked in an appropriate spatial and temporal context.

Proper citation: Arabidopsis Reactome (RRID:SCR_002063) Copy   

•   Source: SciCrunch Registry

http://www.croptrust.org/main/

Not yet vetted by NIF curator

Proper citation: Israel Plant Gene Bank (RRID:SCR_008718) Copy   

•   Source: SciCrunch Registry

http://www.nordgen.org/index.php/en/content/view/full/467

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 18,2023.

Proper citation: Nordic Genetic Resource Centre (Plants) (RRID:SCR_010529) Copy   

•   Source: SciCrunch Registry

http://www.lfd.uci.edu/

Biomedical technology research center and training resource that develops novel fluorescence technologies, including instrumentation, methods and software applicable to cellular imaging and the elucidation of dynamic processes in cells. The LFD's main activities are: * Services and Resources: the LFD provides a state-of-the-art laboratory for fluorescence measurements, microscopy and spectroscopy, with technical assistance to visiting scientists. * Research and Development: the LFD designs, tests, and implements advances in the technology of hardware, software, and biomedical applications. * Training and Dissemination: the LFD disseminates knowledge of fluorescence spectroscopic principles, instrumentation, and applications to the scientific community.

Proper citation: Laboratory for Fluorescence Dynamics (RRID:SCR_001437) Copy   

•   Source: SciCrunch Registry

https://github.com/hangelwen/miR-PREFeR

An accurate, fast, and easy-to-use plant miRNA prediction software tool using small RNA-Seq data. It utilizes expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species.

Proper citation: miR-PREFeR (RRID:SCR_003353) Copy   

•   Source: SciCrunch Registry

http://www.cropscience.bayer.com/

Crop science company with products in crop protection and nonagricultural pest control. It also has activities in seeds and plant traits. (Adapted from Wikipedia)

Proper citation: Bayer CropScience (RRID:SCR_010498) Copy   

•   Source: SciCrunch Registry

http://www3a.biotec.or.th/c-mii/

A software tool for plant miRNA and target identification. C-mii pipelines are based on combined steps and criteria from previous studies and also incorporated with several tools such as standalone BLAST and UNAFold and pre-installed databases including miRBase, UniProt, and Rfam. C-mii provides following distinguished features. First, it comes with graphical user interfaces of well-defined pipelines for both miRNA and target identifications with reliable results. Second, it provides a set of filters allowing users to reduce the number of results corresponding to the recently proposed constraints in plant miRNA and target biogenesis. Third, it extends the standard computational steps of miRNA target identification with miRNA-target folding module and GO annotation. Fourth, it supplies the bird eye views of the identification results with info-graphics and grouping information. Fifth, it provides helper functions for database update and auto-recovery to ease system usage and maintenance. Finally, it supports the multi-project and multi-thread management to improve the computational speed.

Proper citation: C-mii (RRID:SCR_010839) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.psb.ugent.be/webtools/tapir/

Web server designed for prediction of plant microRNA targets.

Proper citation: TAPIR: target prediction for plant microRNAs (RRID:SCR_000237) Copy   

•   Source: SciCrunch Registry

http://mapman.gabipd.org/web/guest/mapman

Software tool that displays large genomics datasets (e.g. gene expression data from Arabidopsis Affymetrix arrays) onto diagrams of metabolic pathways or other biological processes.

Proper citation: MapMan (RRID:SCR_003543) Copy   

•   Source: SciCrunch Registry

http://www.i3crb.fr/

Project to improve data and sample exchanges and to facilitate large scale analysis of data by improving interoperability of French Biological Resources Centres (BRC or biobanks) IT systems and biological databases. The work done in this project will be linked to other national (IBiSA, ANR, R��seau des Biobanques, Club 3C-R), European (BBMRI, ELIXIR) or international project (P3G). In the preliminary phase (2009-2010) I3-CRB has developed a directory of French Biological Resource Centres / Biobanks where one may register their French BRC or perform a search across all of them. Detail by overall data or kingdom is provided as well as many filtering options. Access to biological samples is provided by the participating BRC''''s. Biological Resources Centres (BRC or biobanks) collect annotated biological samples from various sources (human, animal, plant, bacteria...). The type of samples depends on the collection and the associated thematic (DNA, proteins, cells, tissues, blood, serum, organisms...). The aims of these centers are to collect, to store, to transform and to distribute the biological samples. They constitute a vital infrastructure for life science and health research. Goals of the French Biobanks/Biological Resource Centres: * List French biobanks and their biological collections * Improve sample exchanges * Improve the international visibility of the French biological collections MeSH terms have been integrated: Domains, diseases, and location of the disease (Anatomy). Collections/species are based on NCBI Taxonomy.

Proper citation: I3-CRB: Interoperable IT Infrastructure for Biological Resources Centres / Biobanks - France (RRID:SCR_006991) Copy   

•   Source: SciCrunch Registry

http://goblet.molgen.mpg.de/cgi-bin/goblet2008/goblet.cgi

Tool that performs annotation based on GO and pathway terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. GOblet expects query sequences to be in FASTA-Format (with header-lines). Protein and nucleotide sequences are accepted. Total size of all sequences submitted per request should not be larger than 50kb currently. For security reasons: Larger post's will be rejected. Due to limited capacities the queries may be processed in batches depending on the server load. The output of the BLAST job is filtered automatically and the relevant hits are displayed. In addition, the respective GO-terms are shown together with the complete GO-hierarchy of parent terms., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GOblet (RRID:SCR_006998) 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

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

Database providing a systematic and comprehensive view of morphological phenotypes regulated by plant hormones, as well as regulatory genes participating in numerous plant hormone responses. By integrating the data from mutant studies, transgenic analysis and gene ontology annotation, genes related to the stimulus of eight plant hormones were identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid. Another pronounced characteristics of this database is that a phenotype ontology was developed to precisely describe all kinds of morphological processes regulated by plant hormones with standardized vocabularies. To increase the coverage of phytohormone related genes, the database has been updated from AHD to AHD2.0 adding and integrating several pronounced features: (1) added 291 newly published Arabidopsis hormone related genes as well as corrected information (e.g. the arguable ABA receptors) based on the recent 2-year literature; (2) integrated orthologues of sequenced plants in OrthoMCLDB into each gene in the database; (3) integrated predicted miRNA splicing site in each gene in the database; (4) provided genetic relationship of these phytohormone related genes mining from literature, which represents the first effort to construct a relatively comprehensive and complex network of hormone related genes as shown in the home page of our database; (5) In convenience to in-time bioinformatics analysis, they also provided links to a powerful online analysis platform Weblab that they have recently developed, which will allow users to readily perform various sequence analysis with these phytohormone related genes retrieved from AHD2.0; (6) provided links to other protein databases as well as more expression profiling information that would facilitate users for a more systematic analysis related to phytohormone research. Please help to improve the database with your contributions.

Proper citation: Arabidopsis Hormone Database (RRID:SCR_001792) Copy   

•   Source: SciCrunch Registry

http://www.accdc.com/

Comprehensive lists of plant and animal species, with a rarity rank and legal status for each. It has has over 635,000 geo-located records of species occurrences and over 40,000 records of extremely rare to uncommon species in the Atlantic region, including New Brunswick, Nova Scotia, Prince Edward Island, Newfoundland, and Labrador. The Atlantic CDC also maintains biological and other types of data in a variety of linked databases. The CDC welcomes inquiries from those who would like to contribute data about plant or animal species at risk or rare communities in Atlantic Canada. Its mission is to assemble and provide objective and understandable data and expertise about species and ecological communities of conservation concern, including those at risk, and undertake field biological inventories to support decision-making, research, and education in Atlantic Canada. The Atlantic CDC develops species location data, known as element occurrence records. Occurrence precision (accuracy) ranges from quite precise (within meters) to less precise (within counties) but most commonly it is within 1 5 km. Element occurrence (EO) refers to one or more locations considered important to the continued existence of a species or ecological community. For species, over 30 types of data: taxonomy, biology, etc. are typically examined when identifying EOs. An EO is generally the habitat occupied by a local population. However, occurrence varies among species and some species have more than one type of occurrence (e.g., breeding and winter occurrences). Breeding colonies, breeding ponds, denning sites, and hibernacula are general examples of different types of animal EOs. For an ecological community, an EO may be the area containing a patch of that community type.

Proper citation: Atlantic Canada Conservation Data Centre (RRID:SCR_006061) Copy   

•   Source: SciCrunch Registry

http://aps.unmc.edu/AP/main.php

Database and data analysis system dedicated to glossary, nomenclature, classification, information search, prediction, design, and statistics of Antimicrobial peptides and beyond. The peptide data stored in the APD were gleaned from the literature (PubMed, PDB, Google, and Swiss-Prot) manually in the past several years. Peptides will be registered into this database if: # they are from natural sources (bacteria, protozoa, fungi, plants, and animals); # their antimicrobial activities are demonstrated (MIC

Proper citation: APD (RRID:SCR_006606) Copy   

•   Source: SciCrunch Registry

http://bar.utoronto.ca/welcome.htm

Web-based tools for working with functional genomics and other data, including Gene Expression and Protein Tools, Molecular Markers and Mapping Tools, and Other Genomic Tools. Most are designed with the plant (mainly Arabidopsis) researcher in mind, but a couple of them can be useful to the wider research community, e.g. Mouse eFP Browser or BlastDigester. The associated paper for most tools is available.

Proper citation: BAR (RRID:SCR_006748) Copy   

•   Source: SciCrunch Registry