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https://github.com/showteeth/GEfetch2R

Software R package to access and explore existing scRNA-seq data from various public repositories. Supports downloading scRNA-seq data types, including raw data (SRA and ENA), count matrices (GEO, UCSC Cell Browser, and PanglaoDB), and processed objects (Zenodo, CELLxGENE, and HCA). Can access and explore existing scRNA-seq data, load output/downloaded count matrices and annotations to R (SeuratObject/DESeqDataSet), extract a subset of the SeuratObject based on cell metadata and genes, and merge multiple SeuratObjects if applicable. Provides format conversions between different scRNA-seq objects, including SeuratObject, AnnData, SingleCellExperiment, CellDataSet/cell_data_set, and loom.

Proper citation: GEfetch2R (RRID:SCR_026714) Copy   

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

Categories and terms used to classify content (documents, images, etc) in electronic content repositories for life science / BioPharma. Initial version contains content model for use with clinical trial electronic Trial Master Files or eTMF archives. A Content model contains content classification categories (classes) and metadata properties (data properties). Data properties should be assigned to each Content Type. Recent changes include: addition of electronic signature support, medical imaging classifications.

Proper citation: Content Archive Resource Exchange Lexicon (RRID:SCR_010298) Copy   

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http://www.project-redcap.org/

Web application that allows users to build and manage online surveys and databases. Using REDCap's stream-lined process for rapidly developing projects, you may create and design projects using 1) the online method from your web browser using the Online Designer; and/or 2) the offline method by constructing a "data dictionary" template file in Microsoft Excel, which can be later uploaded into REDCap. Both surveys and databases (or a mixture of the two) can be built using these methods. REDCap provides audit trails for tracking data manipulation and user activity, as well as automated export procedures for seamless data downloads to Excel, PDF, and common statistical packages (SPSS, SAS, Stata, R). Also included are a built-in project calendar, a scheduling module, ad hoc reporting tools, and advanced features, such as branching logic, file uploading, and calculated fields. REDCap has a quick and easy software installation process, so that you can get REDCap running and fully functional in a matter of minutes. Several language translations have already been compiled for REDCap (e.g. Chinese, French, German, Portuguese), and it is anticipated that other languages will be available in full versions of REDCap soon. The REDCap Shared Library is a repository for REDCap data collection instruments and forms that can be downloaded and used by researchers at REDCap partner institutions.

Proper citation: REDCap (RRID:SCR_003445) Copy   

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http://catmaid.org/

A toolkit designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy. The interface is inspired by GoogleMaps, with which it shares basic navigation concepts, enhanced to allow the exploration of 3D biological image data acquired by optical or physical sectioning microscopy techniques. The interface enables seamless sharing of regions of interest through bookmarks and synchronized navigation through multiple registered data sets. With massive biological image data sets it is unrealistic to create a sustainable centralized repository. A unique feature of CATMAID is its partially decentralized architecture where the presented image data can reside on any Internet accessible server and yet can be easily cross-referenced in the central database. In this way no image data are duplicated and the data producers retain full control over their images. CATMAID is intended to serve as data sharing platform for biologists using high-resolution imaging techniques to probe large specimens. Any high-throughput, high-content imaging project such as gene expression pattern screens would benefit from the interface for data sharing and annotation. Features: * Fast terabyte-scale image data browsing * Collaborative microcircuit reconstruction and annotation * Flexible hierarchical semantic annotation * Multiple linked image stack display * Neuron Catalog * SVG and WebGL-based neuronal morphology viewer * Open source software

Proper citation: CATMAID (RRID:SCR_006278) Copy   

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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   

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http://vinovia.ncl.ac.uk/emagewebapp/pages/eadhb_home.jsf

Database of a set of standard 3D virtual models at different stages of development from Carnegie Stages (CS) 12-23 (approximately 26-56 days post conception) in which various anatomical regions have been defined with a set of anatomical terms at various stages of development (known as an ontology). Experimental data is captured and converted to digital format and then mapped to the appropriate 3D model. The ontology is used to define sites of gene expression using a set of standard descriptions and to link the expression data to an ''''anatomical tree''''. Human data from stages CS12 to CS23 can be submitted to the HUDSEN Gene Expression Database. The anatomy ontology currently being used is based on the Edinburgh Human Developmental Anatomy Database which encompasses all developing structures from CS1 to CS20 but is not detailed for developing brain structures. The ontology is being extended and refined (by Prof Luis Puelles, University of Murcia, Spain) and will be incorporated into the HUDSEN database as it is developed. Expression data is annotated using two methods to denote sites of expression in the embryo: spatial annotation and text annotation. Additionally, many aspects of the detection reagent and specimen are also annotated during this process (assignment of IDs, nucleotide sequences for probes etc). There are currently two main ways to search HUDSEN - using a gene/protein name or a named anatomical structure as the query term. The entire contents of the database can be browsed using the data browser. Results may be saved. The data in HUDSEN is generated from both from researchers within the HUDSEN project, and from the wider scientific community. The HUDSEN human gene expression spatial database is a collaboration between the Institute of Human Genetics in Newcastle, UK, and the MRC Human Genetics Unit in Edinburgh, UK, and was developed as part of the Electronic Atlas of the Developing Human Brain (EADHB) project (funded by the NIH Human Brain Project). The database is based on the Edinburgh Mouse Atlas gene expression database (EMAGE), and is designed to be an openly available resource to the research community holding gene expression patterns during early human development.

Proper citation: HUDSEN Human Gene Expression Spatial Database (RRID:SCR_006325) Copy   

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http://www.rnasoft.ca/sstrand

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A scientific community-crowdsourced database containing the RNA secondary structures of known types and organisms. It is meant to provide a simple and powerful way to analyze, search and update a shared repository of information.

Proper citation: RNA STRAND-The RNA secondary STRucture and statistical ANalysis Database (RRID:SCR_000086) Copy   

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http://www.nitrc.org/projects/landman/

Project to provide long-term hosting and release for small tools related to medical image analysis. Source repository contains highly experimental code intended for collaborative development. However, any interested parties are welcome to browse/reuse code. Stable/evolved projects will be moved to independent projects.

Proper citation: Landman NeuroImaging Tools (RRID:SCR_009503) Copy   

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http://zebrafish.wi.mit.edu/rnai/

Community built zebrafish RNAi platform that contains plasmids, successfully targeted genes and shRNA sequences, and a forum for discussion. This is a true community platform with users who add data, modify entiries, request features and share using the discussion board.

Proper citation: Zebrafish RNAi Database (RRID:SCR_008965) Copy   

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http://aphasia.talkbank.org/

Shared database of multimedia interactions for the study of communication in aphasia.Access to the data in AphasiaBank is password protected and restricted to members of the AphasiaBank consortium group.

Proper citation: AphasiaBank (RRID:SCR_016317) Copy   

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https://www.mirri.org/

Microbial stock center. Collaborative platform for the pan-European research infrastructure for microbial resources. Provides access to microorganisms, their derivatives, associated data and services for research, development and application.

Proper citation: MIRRI (RRID:SCR_016461) Copy   

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https://github.com/ncbi/pgap

Software tool as NCBI prokaryotic genome annotation pipeline. Used to annotate both complete genomes and draft genomes comprising multiple contigs. Integrated into NCBI infrastructure and processes, and uses modular software framework, GPipe, developed at NCBI for execution of all annotation tasks, from fetching of raw and curated data from public repositories through sequence alignment and model based gene prediction, to submission of annotated genomic data to public NCBI databases.

Proper citation: Prokaryotic Genomes Automatic Annotation Pipeline (RRID:SCR_021329) Copy   

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https://gene.sfari.org/database/human-gene/

Curated public database for autism research built on information extracted from the studies on molecular genetics and biology of Autism Spectrum Disorders (ASD). The genetic information includes data from linkage and association studies, cytogenetic abnormalities, and specific mutations associated with ASD. New gene submissions are welcome. Modules: * Human Gene: thoroughly annotated list of genes that have been studied in the context of autism, with information on the genes themselves, relevant references from the literature, and the nature of the evidence. Uniquely, SFARI Gene incorporates information on both common and rare variants. * Animal Model: information about lines of genetically modified mice that represent potential models of autism. This information includes the nature of the targeting construct, the background strain and, most importantly, a thorough summary of the phenotypic features of the mice that are most relevant to autism. * Protein Interaction (PIN): compilation of all known direct protein interactions for those gene products implicated in autism. It presents both graphical and tabular views of interactomes, highlighting connections between autism candidate genes. Each protein interaction is manually verified by consultation with the primary reference. * Copy Number Variant (CNV): a parallel resource providing genetic information about all known copy number variants linked to autism. * Gene Scoring: includes a "score" for each autism candidate gene, based on an assessment of the strength of human genetic evidence.

Proper citation: AutDB (RRID:SCR_001872) Copy   

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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   

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http://www.megabionet.org/atpid/webfile/

Centralized platform to depict and integrate the information pertaining to protein-protein interaction networks, domain architecture, ortholog information and GO annotation in the Arabidopsis thaliana proteome. The Protein-protein interaction pairs are predicted by integrating several methods with the Naive Baysian Classifier. All other related information curated is manually extracted from published literature and other resources from some expert biologists. You are welcomed to upload your PPI or subcellular localization information or report data errors. Arabidopsis proteins is annotated with information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways.

Proper citation: Arabidopsis thaliana Protein Interactome Database (RRID:SCR_001896) Copy   

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http://www.candidagenome.org/

Database of genetic and molecular biological information about Candida albicans. Contains information about genes and proteins, descriptions and classifications of their biological roles, molecular functions, and subcellular localizations, gene, protein, and chromosome sequence information, tools for analysis and comparison of sequences and links to literature information. Each CGD gene or open reading frame has an individual Locus Page. Genetic loci that are not tied to DNA sequence also have Locus Pages. Provides Gene Ontology, GO, to all its users. Three ontologies that comprise GO (Molecular Function, Cellular Component, and Biological Process) are used by multiple databases to annotate gene products, so that this common vocabulary can be used to compare gene products across species. Development of ontologies is ongoing in order to incorporate new information. Data submissions are welcome.

Proper citation: Candida Genome Database (RRID:SCR_002036) Copy   

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http://autosnpdb.appliedbioinformatics.com.au

We have developed an integrated SNP discovery pipeline, which identifies SNPs from assembled EST sequences. The results are maintained in a custom relational database along with EST source and annotation information. The current database hosts data for the important crops rice, barley and Brassica. Users may rapidly identify polymorphic sequences of interest through BLAST sequence comparison, keyword searches of annotations derived from UniRef90 and GenBank comparisons, GO annotations or in genes corresponding to syntenic regions of reference genomes. In addition, SNPs between specific varieties may be identified for targeted mapping and association studies. SNPs are viewed using a user-friendly graphical interface. The implementation of autoSNPdb allows researchers to query the results of SNP analysis to characterize SNPs between specific groups of individuals or within genes with predicted function. The system is flexible and researchers may add additional levels of annotation, and perform novel queries specific to their area of interest. If you have sequence data you'd like to include please contact Dave Edwards.

Proper citation: autoSNPdb (RRID:SCR_001927) Copy   

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http://pfs.nus.edu.sg/(S(dyrcwejlfws33vxe23zlvrf3))/CopyRightNotice.aspx?ReturnURL=%2fQueryInterface_V5_2.aspx

Search engine integrating various bio-informatic resources and algorithims to produce a one-stop resource for biologists to identify potentially functional SNPs. It caters to different groups of scientists interested in SNPs including those working in the following areas: * Whole-genome association studies * Gene-based association studies * Designing experiments to address the functionality of specific SNPs * Determining potentially functionally significant SNPs that are in LD with non-pfSNPs of interest. Users may add published SNP functions.

Proper citation: pfSNP (RRID:SCR_002167) Copy   

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http://www.humgen.rwth-aachen.de/

Catalog of all changes detected in PKHD1 (Polycystic Kidney and Hepatic Disease 1) in a locus specific database. Investigators are invited to submit their novel data to this database. These data should be meaningful for clinical practice as well as of relevance for the reader interested in molecular aspects of polycystic kidney disease (PKD). There are also some links and information for ARPKD patients and their parents. Autosomal recessive polycystic kidney disease (ARPKD/PKHD1) is an important cause of renal-related and liver-related morbidity and mortality in childhood. This study reports mutation screening in 90 ARPKD patients and identifies mutations in 110 alleles making up a detection rate of 61%. Thirty-four of the detected mutations have not been reported previously. Two underlying mutations in 40 patients and one mutation in 30 cases are disclosed, and no mutation was detected on the remaining chromosomes. Mutations were found to be scattered throughout the gene without evidence of clustering at specific sites. PKHD1 mutation analysis is a powerful tool to establish the molecular cause of ARPKD in a given family. Direct identification of mutations allows an unequivocal diagnosis and accurate genetic counseling even in families displaying diagnostic challenges.

Proper citation: Autosomal Recessive Polycystic Kidney Disease Mutation Database (RRID:SCR_002290) Copy   

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http://www.geisha.arizona.edu/geisha/

Online repository for chicken in situ hybridization information. This site presents whole mount in situ hybridization images and corresponding probe and genomic information for genes expressed in chicken embryos in Hamburger Hamilton stages 1-25 (0.5-5 days). The GEISHA project began in 1998 to investigate using high throughput whole mount in situ hybridization to identify novel, differentially expressed genes in chicken embryos. An initial expression screen of approximately 900 genes demonstrated feasibility of the approach, and also highlighted the need for a centralized repository of in situ hybridization expression data. Objectives: The goals of the GEISHA project are to obtain whole mount in situ hybridization expression information for all differentially expressed genes in the chicken embryo between HH stages 1-25, to integrate expression data with the chicken genome browsers, and to offer this information through a user-friendly graphical user interface. In situ hybridization images are obtained from three sources: 1. In house high throughput in situ hybridization screening: cDNAs obtained from several embryonic cDNA libraries or from EST repositories are screened for expression using high throughput in situ hybridization approaches. 2. Literature curation: Agreements with journals permit posting of published in situ hybridization images and related information on the GEISHA site. 3. Unpublished in situ hybridization information from other laboratories: laboratories generally publish only a small fraction of their in situ hybridization data. High quality images for which probe identity can be verified are welcome additions to GEISHA.

Proper citation: GEISHA - Gallus Expression in Situ Hybridization Analysis: A Chicken Embryo Gene Expression Database (RRID:SCR_007440) Copy   

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