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http://chemistry.st-andrews.ac.uk/staff/jbom/group/databases.html

It is a publicly available web-based database that aims to provide further understanding of protein-ligand interactions. It''s a resource containing biomolecular data, including binding energies, Tanimoto ligand similarity scores and protein sequence similarities of protein-ligand complexes. The PLD contains biomolecular data including calculated binding energies, Tanimoto ligand similarity scores and protein percentage sequence similarities. The database has potential for application as a tool in molecular design.

Proper citation: Protein Ligand Database (RRID:SCR_006980) Copy   

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

Database of records from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), or amyotrophic lateral sclerosis (n = 13). Records from 16 healthy control subjects are also included here. The raw data were obtained using force-sensitive resistors, with the output roughly proportional to the force under the foot. Stride-to-stride measures of footfall contact times were derived from these signals.

Proper citation: Gait Dynamics in Neuro-Degenerative Disease Data Base (RRID:SCR_006979) Copy   

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http://ekhidna.biocenter.helsinki.fi/dali/start

Resource out of service. Documented on May, 5th, 2021.The Dali Database is based on all-against-all 3D structure comparison of protein structures in the Protein Data Bank (PDB). The structural neighborhoods and alignments are automatically maintained and regularly updated using the Dali search engine. The Dali Database contains structural alignments of PDB90 versus the full PDB using DaliLite. The data can be viewed interactively here, or downloaded in its entirety Users may search by PDB identifier or keyword.

Proper citation: Dali database (RRID:SCR_006974) Copy   

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http://zork.wustl.edu/nida/neurosnp.html

The goal of this project is to aid genetic association studies of addiction by creating a resource of biologically relevant genes, pathways and single nucleotide polymorphisms (SNPs). The primary users of the NeuroSNP resource are investigators conducting genome-wide association studies (GWASs) of addiction-related phenotypes. NeuroSNP will allow investigators to identify biologically relevant genes for addiction based on curated expert knowledge, and assess the coverage of these genes provided by commercial SNP microarrays. If investigators wish to ensure the coverage of certain addiction-related genes is optimal, NeuroSNP provides a mechanism for supplementation. While commercial SNP microarrays offer affordable and comprehensive coverage of the human genome, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is believed to be influenced by complex interactions involving several genes and pathways. NIDA has assembled a number of investigators specializing in fields such as genetics, pharmacogenetics, bioinformatics and neurobiology through a Request for Information. These investigators have pooled their expert knowledge to produce a database of addiction-related genes and SNPs. Commercial SNP microarrays, such as those offered by Affymetrix and Illumina, are then analyzed to determine how well certain addiction-related genes are covered. When the coverage is less than optimal, a SNP prioritization scheme is used to supplement the commercial array with the most biologically informative markers. For example, SNPs in coding regions, promoters, and evolutionary conserved regions are selected first.

Proper citation: NeuroSNP Project (RRID:SCR_007029) Copy   

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https://sites.google.com/site/bipolardatabase/

Database of 141 studies which have investigated brain structure (using MRI and CT scans) in patients with bipolar disorder compared to a control group. Ninety-eight studies and 47 brain structures are included in the meta-analysis. The database and meta-analysis are contained in an Excel spreadsheet file which may be freely downloaded from this website.

Proper citation: Bipolar Disorder Neuroimaging Database (RRID:SCR_007025) Copy   

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

KEGG LIGAND contains knowledge of chemical substances and reactions that are relevant to life. It is a composite database consisting of COMPOUND, GLYCAN, REACTION, RPAIR, and ENZYME databases, whose entries are identified by C, G, R, RP, and EC numbers, respectively. ENZYME is derived from the IUBMB/IUPAC Enzyme Nomenclature, but the others are internally developed and maintained. The primary database of KEGG LIGAND is a relational database with the KegDraw interface, which is used to generated the secondary (flat file) database for DBGET.

Proper citation: Database of Chemical Compounds and Reactions in Biological Pathways (RRID:SCR_006851) Copy   

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

Database (anonymous FTP) resulting from a collaborative effort to identify a core set of human and mouse protein coding regions that are consistently annotated and of high quality. The long term goal is to support convergence towards a standard set of gene annotations. Collaborators are EBI, NCBI, UCSC, WTSI and the initial results are also available from the participants'''' genome browser Web sites. In addition, CCDS identifiers are indicated on the relevant NCBI RefSeq and Entrez Gene records and in Map Viewer displays of RNA (RefSeq) and Gene annotations on the reference assembly.

Proper citation: Consensus CDS (RRID:SCR_006729) Copy   

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http://haldanessieve.org/

Blog discussing preprints in population and evolutionary genetics.

Proper citation: Haldanes Sieve (RRID:SCR_007178) Copy   

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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   

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http://blogs.discovermagazine.com/loom/

The Loom is a blog about life, past and future. Written by DISCOVER contributing editor and columnist Carl Zimmer. Carl Zimmer writes about science regularly for the New York Times and magazines such as Discover, where he is a contributing editor and columnist.

Proper citation: The Loom (RRID:SCR_006877) Copy   

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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   

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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   

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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   

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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.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   

•   Source: SciCrunch Registry