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http://dbtgr.hgc.jp/

DBTGR provides information on tunicate gene regulation, such as the location of expression, or the identified regulatory elements present in promoter sequences. The database also contains the promoters of homologous genes in multiple species to allow identification of conserved cis elements.

Proper citation: DataBase of Tunicate Gene Regulation (RRID:SCR_007620) Copy   

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http://apps.sanbi.ac.za/ddoc/

:DDOC provides a comprehensive compilation of the published research related to the genes associated with ovarian cancer. DDOC provides details of the cell line, tissue or cell type, expression status, disease stage, tumor grade, OC type and laboratory method provided in the literature. The links to the relevant sources of data used to extract information related to genes are also included. Many aspects of the information provided in the DDOC were curated by biologists, which increases its accuracy. DDOC is freely accessible for academic and non-profit users.

Proper citation: Dragon Database for Exploration of Ovarian Cancer Genes (RRID:SCR_007621) Copy   

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http://dbptm.mbc.nctu.edu.tw/

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   

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http://urgv.evry.inra.fr/projects/FLAGdb++/HTML/index.shtml

A database for the functional analysis of the Arabidopsis genome. The ultimate objective of this project is to develop a database and associated bioinformatics tools based on the integration of genomic data around a selection of plant complete genomes. This tool will help users to understand the biological role of plant genes by considering them in a wide context: a multigene family, a topological environment, and/or a functional network. The database and the associated user-friendly interface is developed with a conceptual effort for the graphical display and the hierarchical organization of the data. The running integration involves the structural and functional international annotations, EST from different plant species, novel gene predictions, mutant tags, gene families, protein motifs, transcriptome data, repeat sequences, primers and tags for genomic approaches (DNA chips, synteny studies, BAC library screening, RT-PCR, SNP discovery, ...), subcellular targeting, secondary structures, 3D models, MPSS tags, curated annotations and mutant phenotypes.

Proper citation: FLAGdb++ (RRID:SCR_007659) Copy   

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http://compbio.cs.queensu.ca/F-SNP/

F-SNP database provides integrated information about the functional effects of SNPs obtained from 16 bioinformatics tools and databases. The functional effects are predicted and indicated at the splicing, transcriptional, translational, and post-translational level. As such, the F-SNP database helps identify and focus on SNPs with potential pathological effect to human health. Users can find SNP's based on ID, associated disease, gene, or chromosomal region.

Proper citation: F-SNP: a collection of functional SNPs, specifically prioritized for disease association studies (RRID:SCR_007653) Copy   

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http://www.cmbi.kun.nl/EXProt/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. EXProt (database for EXPerimentally verified Protein functions) is a new non-redundant database containing protein sequences for which the function has been experimentally verified. EXProt is a selection of 6491 entries which are described to have an experimentally verified function. The entries in EXProt all have a unique ID number and provide information about organism, protein sequence, functional annotation, link to entry in original database, and if known, gene name and link to references in PubMed. The EXProt database can be searched with BLAST or FASTA with amino acid or nucleotide sequence as query sequence. Note that only the sequence goes into the field. EXProt database is also searchable in SRS6 at CMBI. In a near future entries from the genome project of Lactobacillus plantarum by Wageningen Centre for Food Sciences (WCFS) will be added to EXProt.

Proper citation: EXProt- database for EXPerimentally verified Protein functions (RRID:SCR_007652) Copy   

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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   

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http://cgl.imim.es/fcp/

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   

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http://jbirc.jbic.or.jp/hinv/evola/

Evola is a sub-database of H-InvDB, providing ortholog data as evolutionary annotation. Representative transcripts (one transcript per one gene locus) were analyzed as genes. Orthologs were first detected by computational analysis. Then, more reliable orthologs were determined by manual curation inspecting the phylogenetic trees., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Evola (RRID:SCR_007651) Copy   

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http://www.everest.cs.huji.ac.il

EVEREST is an automatic process of identifying and classifying of protein domains. Users can search for specific proteins using Protein ID or name, browse through protein families, and upload/download protein sequence data. EVEREST combines methodologies from the fields of finite metric spaces, machine learning and statistical modeling and achieves state of the art results. The process begins by constructing a database of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families, choosing the best putative families using machine learning techniques, and creating a statistical model for each of the chosen families. This procedure is then iterated: The aforementioned statistical models are used to scan all protein sequences, to recreate a segment database and to cluster them again. Performance was evaluated by comparing with Pfam and SCOP.

Proper citation: EVEREST - EVolutionary Ensembles of REcurrent SegmenTs (RRID:SCR_007650) Copy   

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http://euhcvdb.ibcp.fr

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented August 23, 2016. The euHCVdb is oriented towards protein sequence, structure, function analysis and structural biology of the Hepatitis C Virus. It is monthly updated from the EMBL Nucleotide sequence database and maintained in a relational database management system (PostgreSQL). Programs for parsing the EMBL database flat files, annotating HCV entries, filling up and querying the database used SQL and Java programming languages. Great efforts have been made to develop a fully automatic annotation procedure thanks to a reference set of HCV complete annotated well-characterized genomes of various genotypes. This automatic procedure ensures standardization of nomenclature for all entries and provides genomic regions/proteins present in the entry, bibliographic reference, genotype, interesting sites (e.g. HVR1) or domains (e.g. NS3 helicase), source of the sequence (e.g. isolate) and structural data that are available as protein 3D models. The euHCVdb is funded as part of the HepCVax cluster (EC grant QLK2-CT-2002-01329) and viRgil network of excellence (EC grant LSHM-CT-2004-503359).

Proper citation: euHCVdb: The European HCV database (RRID:SCR_007645) Copy   

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http://www.cbil.upenn.edu/EpoDB/

Database of genes that relate to vertebrate red blood cells. It includes DNA sequence, structural features, protein information, gene expression information and transcription factor binding sites. This database is no longer maintained or updated.

Proper citation: EpoDB - Erythropoiesis Database (RRID:SCR_007642) Copy   

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http://bioinfo.mc.vanderbilt.edu/ERGR/

The aim of the Ethanol-Related Gene Resource (ERGR) database is to provide a comprehensive and useful gene resource to the Ethanol/Alcohol research community. Currently, the ERGR database contains more than 30 large datasets from literature and 21 mouse QTLs from public database. These data are from 5 organisms (human, mouse, rat, fly and worm) and produced by multiple approaches (expression, association, linkage, QTL, literature search etc). Users can browse or search the database in different levels. Moreover, ERGR provides data integration (union and intersection) and candidate gene selection based on multiple datasets or organisms.

Proper citation: ERGR- Ethanol-Related Genome Resource (RRID:SCR_007643) Copy   

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http://openccdb-dev-web.crbs.ucsd.edu/software/index.shtm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Software to support registering brain images to the stereotaxic coordinate system of a brain atlas. It was specifically designed to work with the large scale brain mosaics. When data are uploaded to the CCDB, users may launch Jibber, a custom tool for defining correspondence points between the image and an atlas overlay. Jibber automatically downsamples the data, so that users can define the warping and scaling parameters with good interactive performance on the smaller copy. Once the warping transformation is computed, the original image and the transformation matrix are sent to a cluster of computers for warping. The current version of Jetsam is running on a 30 Sun V20 nodes and the execution time is roughly about 20 minutes per GB. The warped images are then automatically registered with an image web server that supports spatial queries based on stereotaxic coordinates. These servers generate optimized downsampled images, which can be displayed by standard online clients regardless of the size of the original image.

Proper citation: Image Workflow (RRID:SCR_007017) Copy   

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http://openccdb-dev-web.crbs.ucsd.edu/software/index.shtm

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 2, 2019. Ontology-based segmentation and analysis tools for electron tomographic data.

Proper citation: Jinx (RRID:SCR_007012) Copy   

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http://www.cns.atr.jp/dni/en/downloads/tools-for-brain-behavior-data-sharing/

This is MATLAB library to create Neuroshare data format. You can convert your own data into Neuroshare format file.

Proper citation: Matlab Neuroshare Library (RRID:SCR_006957) Copy   

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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   

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http://smallrna.udel.edu/index.php

This project has developed a sequence dataset of plant small RNAs based on the hypothesis that most if not all plants utilize important small RNA signaling networks. Different plant families are likely to have both common and lineage-specific miRNAs or other small RNAs with important biological roles. Comparative genomics approaches can be applied to distinguish potential miRNAs from siRNAs and to match the miRNAs to the target sequences. This project develops an unparalleled resource of millions of plant small RNAs for comparative analyses. The project includes sequencing of small RNAs from a diverse and agronomically-relevant set of plant species, focused analyses of important members of the Solanaceae and Poaceae, and development of a small RNA database and web interface for public access and analysis of data. These data will allow the experimental characterization of the majority of biologically important small RNAs for a range of plant species, and will be tremendously useful to a broad set of plant biologists interested in development, stress responses, epigenetics, evolution, RNA biology and other traits impacted by small RNAs. We offer a variety of tools to query the small RNA data set, with options to identify sequences based on homology, expression levels, conservation, or potential function: 1. Small RNA mapping tool: searches for small RNAs perfectly matching a genomic sequence provided by the user. 2. Small RNA mismatch tool: searches the database for small RNAs or other short sequences provided by the user, allowing mismatches. 3. Library-comparison tool to identify conserved small RNAs. 4. Library-comparison tool to identify differentially regulated small RNAs. 5. Reverse Target Prediction.

Proper citation: Comparative Sequencing of Plant Small RNAs (RRID:SCR_007003) Copy   

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https://mctfr.psych.umn.edu/

Composed of many projects, including the Minnesota Twin Family Study (MTFS) and The Sibling Interaction and Behavior Study (SIBS), this research center seeks to identify genetic and environmental influences on development and psychological traits. Both projects are longitudinal research studies including twins, siblings, and parents. Over 9800 individuals have contributed to these exciting projects! By studying twins and siblings and their families, we can estimate how genes and environment interact to influence character, strengths, vulnerabilities and values. Participants in the MTFS include families with same-sex identical or fraternal twins who were born in Minnesota. The SIBS study is comprised of adoptive and biological siblings and their parents. Most participants partake in day-long visits to the MCTFR, and due to the longitudinal nature of our projects, they return every 3-4 years for follow-up visits.

Proper citation: Minnesota Center for Twin and Family Research (RRID:SCR_006948) Copy   

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https://github.com/jstjohn/SimSeq

An illumina paired-end and mate-pair short read simulator. This project attempts to model as many of the quirks that exist in Illumina data as possible. Some of these quirks include the potential for chimeric reads, and non-biotinylated fragment pull down in mate-pair libraries .

Proper citation: SimSeq (RRID:SCR_006947) Copy   

•   Source: SciCrunch Registry