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http://epgd.biosino.org/SysZNF/

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 2, 2016. SysZNF is an information resource for C2H2 Zinc Finger genes in humans and mice. C2H2 Zinc Finger genes (C2H2-ZNF) constitute the largest class of transcription factors in humans and mouse. C2H2 zinc finger proteins primarily bind to DNA. In most cases, they attach to regions near certain genes and turn the genes on and off as needed. The researches on these genes show light on the evolution of gene regulation systems and development. Therefore, we develop SysZNF (Systematical information resource of Zinc Finger genes) to collect the information related to C2H2 Zinc Finger genes. The aim of SysZNF was to provide a user-friendly interface for rendering the information (DNA, Expression, Protein, Reference and so on) of each C2H2-ZNF (e.g., ZNF10) and to enable a comprehensive analysis of C2H2-ZNF. This project was supported by the Proteome-Center at Rostock University (PCRU) who conceives the concept of the database and Key laboratory of Systems biology at the Shanghai Institute for Biological Sciences (SIBS) who implemented the database. It is maintained jointly by PCRU and SIBS.

Proper citation: SysZNF - C2H2 Zinc Finger genes (RRID:SCR_007056) Copy   

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http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/

A database of interactions between HIV-1 and human proteins published in the peer-reviewed literature. The goal is to provide a concise, yet detailed, summary of all known interactions of HIV-1 proteins with host cell proteins, other HIV-1 proteins, or proteins from disease organisms associated with HIV/AIDS. For each HIV-1 human protein interaction the following information is provided: * NCBI Reference Sequence (RefSeq) protein accession numbers. * NCBI Entrez Gene ID numbers. * Amino acids from each protein that are known to be involved in the interaction. * Brief description of the protein-protein interaction. * Keywords to support searching for interactions. * PubMed identification numbers (PMIDs) for all journal articles describing the interaction. In addition, all protein-protein interactions documented in the database are integrated into Entrez Gene records and listed in the ''HIV-1 protein interactions'' section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication.

Proper citation: HIV-1 Human Protein Interaction Database (RRID:SCR_006879) Copy   

•   Source: SciCrunch Registry

https://myhits.sib.swiss/

Database devoted to protein domains. It is also a collection of tools for the investigation of the relationships between protein sequences and motifs described on them.

Proper citation: MyHits (RRID:SCR_006757) Copy   

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http://www.genome.ad.jp/aaindex/

AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. AAindex consists of three sections now: AAindex1 for the amino acid index of 20 numerical values, AAindex2 for the amino acid mutation matrix and AAindex3 for the statistical protein contact potentials. All data are derived from published literature. An amino acid index is a set of 20 numerical values representing any of the different physicochemical and biological properties of amino acids. The AAindex1 section of the Amino Acid Index Database is a collection of published indices together with the result of cluster analysis using the correlation coefficient as the distance between two indices. This section currently contains 544 indices. Another important feature of amino acids that can be represented numerically is the similarity between amino acids. Thus, a similarity matrix, also called a mutation matrix, is a set of 210 numerical values, 20 diagonal and 20x19/2 off-diagonal elements, used for sequence alignments and similarity searches. The AAindex2 section of the Amino Acid Index Database is a collection of published amino acid mutation matrices together with the result of cluster analysis. This section currently contains 94 matrices. In the release 9.0, we added a collection of published protein pairwise contact potentials to AAindex as AAindex3. This section currently contains 47 contact potential matrices. Sponsors: This work was supported by grants and resources from the Ministry of Education, Culture, Sports, Science and Technology, and the Japan Science and Technology Agency, and the Bioinformatics Center, Institute for Chemical Research, Kyoto University and the Super Computer System, Human Genome Center, Institute of Medical Science, University of Tokyo.

Proper citation: Amino Acid Index Database (RRID:SCR_007044) Copy   

•   Source: SciCrunch Registry

http://unitrap.cbm.fvg.it/

A curated collection of all the trapped ES cell clones. Gene-trapping is a valuable tool that uses random mutagenesis to create hypomorphic or null alleles by insertion of exogenous DNA. Since numerous public and private projects have been performing gene trapping over the last few years,it is natural that large overlaps exist and some vectors produce better knock-outs than others due to their insertion site. Considering the high need to develop a comprehensive database that would include both public and private data to provide public access to this essential biological resource, we developed UniTrap, a curated collection of all the trapped ES cell clones, collected from public and private databases. We have developed a bioinformatics pipeline to automate the identification and characterization of trapped genes starting from their transcriptional sequence tags.We process gene trap sequence tags from ES cell clones to generate ‘UniTraps’, i.e. distinct collections of unambiguous insertions at the same subgenic region of annotated genes (RefSeq and Ensembl genes). The UniTrap resource contains data relative to well-known trapped genes. We aim to provide the wet lab researchers with a comprehensive, regularly updated database and curated tools for(i) identifying and comparing the clones carrying a trap into the genes of interest,(ii) evaluating the severity of the mutation to the protein function in each independent trapping event, and(iii) supplying complete information to perform PCR, RT-PCR and restriction experiments to verify the clone and identify the exact point of vector insertion.

Proper citation: UniTrap (RRID:SCR_007046) Copy   

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http://rkd.ucdavis.edu/interactome.shtml

It was created to host functional genomic information gathered as part of a large NSF funded rice kinase proteomics project. The goal is to integrate disparate data sets into a logical, user friendly format. To accomplish this, they have developed a platform to display user selected functional genomic data on a phylogenetic tree. The RKD also includes an interactive chromosomal map showing the positions of all rice kinases and an interactive protein-protein interaction maps.

Proper citation: Rice Kinase Database (RRID:SCR_006990) Copy   

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http://bioinformatics.biol.uoa.gr/cuticleDB

A relational database containing all structural proteins of Arthropod cuticle identified to date. Many come from direct sequencing of proteins isolated from cuticle and from sequences from cDNAs that share common features with these authentic cuticular proteins. It also includes proteins from the five sequenced genomes where manual annotation has been applied to cuticular proteins: Anopheles gambiae, Apis mellifera, Bombyx mori, Drosophila melanogaster, and Nasonia vitripennis. Some sequences were confirmed as authentic cuticular proteins because protein sequencing revealed that they were present in cuticle; others were identified by sequence homology and other criteria. Entries provides information about whether sequences are putative or authentic cuticular proteins. CuticleDB was primarily designed to contain correct and full annotation of cuticular protein data. The database will be of help to future genome annotators. Users will be able to test hypotheses for the existence of known and also of yet unknown motifs in cuticular proteins. An analysis of motifs may contribute to understanding how proteins contribute to the physical properties of cuticle as well as to the precise nature of their interaction with chitin.

Proper citation: CuticleDB (RRID:SCR_007045) Copy   

•   Source: SciCrunch Registry

http://www.signaling-gateway.org/molecule/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 29,2025. Relational database of all significant published qualitative and quantitative information on cell signaling proteins. The Molecule Pages database was developed with the specific aim of allowing interactions, and indeed whole pathways, to be modeled. The goal is to filter the data to present only validated information. In addition, the Gateway is the home of Signaling Update, which provides a one-stop overview of the latest and hottest research in cell signaling for both the specialist and non-specialist alike.

Proper citation: UCSD-Nature Signaling Gateway Molecule Pages (RRID:SCR_006907) Copy   

•   Source: SciCrunch Registry

http://www.fda.gov/Food/IngredientsPackagingLabeling/FoodAdditivesIngredients/ucm115326.htm

PAFA contains administrative, chemical and toxicological information on over 2000 substances directly added to food. In addition, the database contains only administrative and chemical information on less than 1000 such substances. The more than 3000 total substances together comprise an inventory often referred to as Everything Added to Food in the United States (EAFUS). The EAFUS list of substances contains ingredients added directly to food that FDA has either approved as food additives or listed or affirmed as GRAS. Nevertheless, it contains only a partial list of all food ingredients that may in fact be lawfully added to food, because under federal law some ingredients may be added to food under a GRAS determination made independently from the FDA. The list contains many, but not all, of the substances subject to independent GRAS determinations. :Sponsors: This information is generated from a database maintained by the U.S. Food and Drug Administration (FDA) Center for Food Safety and Applied Nutrition (CFSAN) under an ongoing program known as the Priority-based Assessment of Food Additives (PAFA).

Proper citation: Everything Added to Food in the United States (RRID:SCR_006747) Copy   

•   Source: SciCrunch Registry

http://www.suba.bcs.uwa.edu.au/

SUBA provides a powerful tool to investigate subcellular localization in Arabidopsis. SUBA houses large scale proteomic and GFP localization sets from cellular compartments of Arabidopsis, and also contains pre-compiled bioinformatic predictions for protein subcellular localizations. The Database functions through the unification of disparate datasets and through the provision of a web accessible interface for the construction of user based queries resulting in a one-stop-shop for protein localization in this model plant. Subcellular localization information can contribute towards our understanding of protein function, protein redundancy and of biological inter-relationships. In an attempt to get a clearer picture of our experimental data and to more generally understand subcellular partitioning we have brought together various data sources to build SUBA.

Proper citation: SUB-cellular location database for Arabidopsis proteins II (RRID:SCR_006668) Copy   

•   Source: SciCrunch Registry

http://www.re3data.org/

Global registry of research data repositories from all academic disciplines that allows the easy identification of appropriate research data repositories, both for data producers and users. Information icons display principal attributes of a repository that can be used for multi-faceted searches. Repository operators can suggest their infrastructures to be listed via a simple application form. A repository is indexed when the minimum requirements are met, i.e. mode of access to the data and repository, as well as the terms of use.

Proper citation: re3data.org (RRID:SCR_006782) Copy   

•   Source: SciCrunch Registry

http://prodoric.tu-bs.de/

Database about gene regulation and gene expression in prokaryotes. It includes a manually curated and unique collection of transcription factor binding sites. A variety of bioinformatics tools for the prediction, analysis and visualization of regulons and gene reglulatory networks is included. The integrated approach provides information about molecular networks in prokaryotes with focus on pathogenic organisms. In detail this concerns: * transcriptional regulation (transcription factors and their DNA binding sites * signal transduction (two-component systems, phosphylation cascades) * protein interactions (complex formation, oligomerization) * biochemical pathways (chemical reactions) * other regulation events (e.g. codon usage, etc. ...) It aims to be a resource to model protein-host interactions and to be a suitable platform to analyze high-throughput data from proteomis and transcriptomics experiments (systems biology). Currently it mainly contains detailed information about operon and promoter structures including huge collections of transcription factor binding sites. If an appropriate number of regulatory binding sites is available, a position weight matrix (PWM) and a sequence logo is provided, which can be used to predict new binding sites. This data is collected manually by screening the original scientific literature. PRODORIC also handles protein-protein interactions and signal-transduction cascades that commonly occur in form of two-component systems in prokaryotes. Furthermore it contains metabolic network data imported from the KEGG database., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRODORIC (RRID:SCR_007074) Copy   

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http://www.bioinfo.no/tools/TAED

A database of sequence alignments and phylogenetic trees for chordates and embryophytes. The Adaptive Evolution Database (TAED) was first presented as a collection of branches from chordate and embryophyte gene families with fast evolutionary rates mapped onto the NCBI taxonomy (1,2). The original gene families were from the Master Catalog and are proprietary (3). A new version of TAED is now presented as a taxonomic shell together with a gene family database. In addition to multiple sequence alignments and phylogenetic trees for all families of chordate and embryophyte sequences, the ratio of non-synonymous to synonymous nucleotide substitution rates (Ka/Ks) is provided for each branch of every phylogenetic tree. This ratio, when significantly greater than 1, is an indicator of positive selection and potentially a change of function of the encoded protein. With a gene tree to species tree mapping, the branches significantly greater than 1 are collated together in a phylogenetic context. The framework is expandable to incorporate other genomic-scale information in a phylogenetic context. Ultimately, the database is designed both to provide high-quality gene families with multiple sequence alignments and phylogenetic trees for chordates and embryophytes, and to enable asking the question, What makes each species unique at the molecular genomic level?

Proper citation: TAED - The Adaptive Evolution Database (RRID:SCR_006930) Copy   

•   Source: SciCrunch Registry

http://www.dddc.ac.cn/pdtd/

It is a dual function database that associates an informatics database to a structural database of known and potential drug targets. PDTD is a comprehensive, web-accessible database of drug targets, and focuses on those drug targets with known 3D-structures. PDTD contains 1207 entries covering 841 known and potential drug targets with structures from the Protein Data Bank (PDB). Drug targets of PDTD were categorized into 15 and 13 types according to two criteria: therapeutic areas and biochemical criteria. The database supports extensive searching function using PDB ID, target name and category, related disease.

Proper citation: Potential Drug Target Database (RRID:SCR_007069) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/physiobank/database/gaitpdb/

Database that contains measures of gait from 93 patients with idiopathic PD (mean age: 66.3 years; 63% men), and 73 healthy controls (mean age: 66.3 years; 55% men). The database includes the vertical ground reaction force records of subjects as they walked at their usual, self-selected pace for approximately 2 minutes on level ground. Underneath each foot were 8 sensors (Ultraflex Computer Dyno Graphy, Infotronic Inc.) that measure force (in Newtons) as a function of time. The output of each of these 16 sensors has been digitized and recorded at 100 samples per second, and the records also include two signals that reflect the sum of the 8 sensor outputs for each foot. This database also includes demographic information, measures of disease severity (i.e., using the Hoehn & Yahr staging and/or the Unified Parkinson's Disease Rating Scale) and other related measures (available in HTML or xls spreadsheet format). A subset of the database includes measures recorded as subjects performed a second task (serial 7 subtractions) while walking, which shows excerpts of swing time series from a patient with PD and a control subject, under usual walking conditions and when performing serial 7 subtractions. Under usual walking conditions, variability is larger in the patient with PD (Coefficient of Variation = 2.7%), compared to the control subject (CV = 1.3%). Variability increases during dual tasking in the subject with PD (CV = 6.5%), but not in the control subject (CV = 1.2%).

Proper citation: Gait in Parkinson's Disease (RRID:SCR_006891) Copy   

•   Source: SciCrunch Registry

http://arabidopsis.med.ohio-state.edu

An information resource of Arabidopsis promoter sequences, transcription factors and their target genes that contains three databases. *AtcisDB consists of approximately 33,000 upstream regions of annotated Arabidopsis genes (TAIR9 release) with a description of experimentally validated and predicted cis-regulatory elements. *AtTFDB contains information on approximately 1,770 transcription factors (TFs). These TFs are grouped into 50 families, based on the presence of conserved domains. *AtRegNet contains 11,355 direct interactions between TFs and target genes. They provide free download of Arabidopsis thaliana cis-regulatory database (AtcisDB) and transcription factor database (AtTFDB).

Proper citation: Arabidopsis Gene Regulatory Information Server (RRID:SCR_006928) Copy   

•   Source: SciCrunch Registry

https://www.embrys.jp/embrys/html/About.html

Data collection of gene expression patterns mapped in whole-mount mouse embryo (ICR strain) of mid-gestational stages (Embryonic Day 9.5, 10.5, 11.5), in which most striking dynamics in pattern formation and organogenesis is observed. Collection of gene expression patterns of transcription factors (TFs) and TF-related factors such as transcription cofactors. Genes were extracted from databases including RIKEN Transcription Factor Database and Panther Classification System.

Proper citation: EMBRYS (RRID:SCR_006689) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/unists

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database of sequence tagged sites (STSs) derived from STS-based maps and other experiments. STSs are defined by PCR primer pairs and are associated with additional information such as genomic position, genes, and sequences. Chromosome maps are labeled by name of the originating organism, the map title, total markers, total UniSTSs and links to view maps as well as research documents available through PubMed, another NCBI database. The search functions within UniSTS allow the user to search by gene marker, chromosome, gene symbol and gene description terms to locate markers on specified genes. A representation of the UniSTS datasets is available by ftp. NOTE: All data from this resource have been moved to the Probe database, http://www.ncbi.nlm.nih.gov/probe. You can retrieve all UniSTS records by searching the probe database using the search term unists(properties). (use brackets insead of parenthesis). Additionally, legacy data remain on the NCBI FTP Site in the UniSTS Repository (ftp://ftp.ncbi.nih.gov/pub/ProbeDB/legacy_unists).

Proper citation: UniSTS (RRID:SCR_006843) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/COG

A database for phylogenetic classification for proteins encoded in complete genomes. Clusters of Orthologous Groups of proteins (COGs) were delineated by comparing protein sequences encoded in complete genomes, representing major phylogenetic lineages. Each COG consists of individual proteins or groups of paralogs from at least 3 lineages and thus corresponds to an ancient conserved domain. Please be aware that COGs hasn't been updated in many years and will not be.

Proper citation: COG (RRID:SCR_007139) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1347366/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 19, 2016. It provides a common access point to interaction resources at MIPS. It is designed to support the PSI-MI standard, for both downloading and uploading data. It provides the user with intuitive query forms to quickly retrieve the interactions of interest. Graphical representations allow an easy navigation through the protein interaction networks.

Proper citation: MPact: Representation of Interaction Data at MIPS (RRID:SCR_006687) Copy   

•   Source: SciCrunch Registry