Tools   Select Another Resource Report Type

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.genscript.com/psort/wolf_psort.html

Data analysis service for protein subcellular localization prediction.

Proper citation: WoLF PSORT (RRID:SCR_002472) Copy   

•   Source: SciCrunch Registry

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://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

http://gbrowse.org/

A database and interactive web site for manipulating and displaying annotations on genomes. Features include: detailed views of the genome; use of a variety of premade or personally made glyphs ; customizable order and appearance of tracks by administrators and end-users; search by annotation ID, name, or comment; support of third party annotation using GFF formats; DNA and GFF dumps; connectivity to different databases, including BioSQL and Chado; and a customizable plug-in architecture (e.g. run BLAST, find oligonucleotides, design primers, etc.). GBrowse is distributed as source code for Macintosh OS X, UNIX and Linux platforms, and as pre-packaged binaries for Windows machines. It can be installed using the standard Perl module build procedure, or automated using a network-based install script. In order to use the net installer, you will need to have Perl 5.8.6 or higher and the Apache web server installed. The wiki portion accepts data submissions.

Proper citation: GBrowse (RRID:SCR_006829) 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

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://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://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://purl.bioontology.org/ontology/CO

Ontology that includes crop-specific trait ontologies for several economically important plants like rice, wheat, maize, potato, musa, chickpea and sorghum along with other important domains for crop research such as germplasm, passport, trait measurement scales, experimental design factors etc.

Proper citation: Crop Ontology (RRID:SCR_010299) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/PAE

THIS RESOURCES IS NO LONGER IN SERVICE, documented on April 23, 2014. REPLACED BY: Plant Ontology (PO). A controlled vocabulary of plant morphological and anatomical structures representing organs, tissues, cell types, and their biological relationships based on spatial and developmental organization. Note that this has been subsumed into the PO. This file is created by filtering plant_ontology_assert.obo to contain only terms from the plant anatomical entity branch of the PO. For more information, please see: http://palea.cgrb.oregonstate.edu/viewsvn/Poc/tags/live/

Proper citation: Plant Anatomy (RRID:SCR_010408) Copy   

•   Source: SciCrunch Registry

https://obofoundry.org/ontology/cl.html

Ontology designed as a structured controlled vocabulary for cell types. It was constructed for use by the model organism and other bioinformatics databases. It includes cell types from prokaryotes, mammals, and fungi. The ontology is available in the formats adopted by the Open Biological Ontologies umbrella and is designed to be used in the context of model organism genome and other biological databases.

Proper citation: Cell Type Ontology (RRID:SCR_004251) 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://purl.bioontology.org/ontology/PSDS

A controlled vocabulary of growth and developmental stages in various plants. Note that this has been subsumed into the Plant Ontology (PO). This file is created by filtering plant_ontology_assert.obo to contain only terms from the plant structure development stage branch of the PO. For more information, please see: http://palea.cgrb.oregonstate.edu/viewsvn/Poc/tags/live/

Proper citation: Plant Structure Development Stage (RRID:SCR_010410) 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

https://www.genevestigator.com/gv/

A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.

Proper citation: Genevestigator (RRID:SCR_002358) Copy   

•   Source: SciCrunch Registry