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http://bioinfo3d.cs.tau.ac.il/RsiteDB/

It is a database that details the interactions of extruded, unpaired RNA nucleotide bases. It presents and classifies the protein binding pockets that accommodate them, and also allows the recognition of similar protein binding patters involved in interactions with different RNA molecules. Given an unbound structure of a target protein, it allows the prediction of its RNA nucleotide binding sites. The goal of this database is to describe, classify, and predict the interactions between protein binding sites and single-stranded RNA bases. Specifically, RsiteDB describes the protein binding pockets that accommodate extruded nucleotides not involved in RNA base pairing. RsiteDB has two modes of operation. Analysis and classification of protein-RNA interactions: Given a protein-RNA complex RsiteDB analyzes its nucleotide and dinucleotide binding sites. It details the properties of the protein binding pockets that accommodate these extruded nucleotides and presents a list of proteins with similar binding pockets. These proteins may have a totally different overall sequences and structural folds. RsiteDB details and visualizes the features shared by all the binding sites classified to the same cluster. Prediction of RNA dinucleotide binding sites: Given a target, potentially unbound, protein structure we search its surface for regions similar to the created 3-D consensus binding patterns of RNA dinucleotides. The recognized regions are predicted to serve as binding sites. Using leave-one-out tests, the success rate of these predictions was estimated to be about 80%. It must be noted that currently we do not aim to predict whether a protein can bind RNA; rather, given an unbound RNA binding protein, our goal is to predict its binding sites and their modes of interaction. In addition, due to a low number of single nucleotide clusters, currently, we do not use them for the prediction.

Proper citation: RsiteDB- RNA binding sites database (RRID:SCR_007906) Copy   

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http://www.mcponline.org/content/3/10/1009.long

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 19, 2016. Database that offers information on molecules and interactions involving signaling pathways through literature-based curative explanation and laboratory results as well as basic information through links to other databases. Its major content consists of signaling entities and signaling interactions designed to describe various levels of signaling events. It is designed to convey chemical changes and logical information flow in detail through careful data modeling of complex signaling processes. ROSPath was developed for the purpose of aiding the research of ROS-mediated signaling pathways including growth factor-, stress- and cytokine-induced signaling that are main research interests of the Division of Molecular Life Sciences and Center for Cell Signaling Research in Ewha Womans University. ROSPath is designed to describe cellular signaling processes in molecular detail and to accumulate data and knowledge regarding signaling pathways with the organized database structure. It offers useful means to researchers by providing curative Information on the signaling pathways of interest and by providing means of managing data produced by high-throughput experiments such as proteomics and genomics tools. Furthermore, its goal is to provide effective and flexible tools for signaling pathway analysis and data mining by means of extensive data modeling and development of computer-aided tools.

Proper citation: ROSPath- Reactive Oxygen Species Related Signaling Pathway (RRID:SCR_007903) Copy   

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http://polydoms.cchmc.org

An integrated database of human coding single nucleotide polymorphisms (SNPs) and their annotations. Unlike other databases of similar nature, apart from integrating several coding SNPs (cSNPs) and protein-related information resources, we predict the implications of the non-synonymous SNPs (nsSNPs) using two well known algorithms (SIFT and PolyPhen). The results are presented in an intuitive visualization that depicts the cSNPs mapped onto protein domains and highlights those nsSNPs that are potentially damaging/deleterious or have been reported as disease allelic variants (based on OMIM). The query interface also supports searching for a list of proteins associated with any gene ontology term, pathway, disease term or gene family. Results can also be downloaded as a spreadsheet. The visualization page also provides links to several other related sources and dynamic links to literature references.

Proper citation: PolyDoms (RRID:SCR_007869) Copy   

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http://ribosome.med.miyazaki-u.ac.jp/

It is a database that provides detailed information about ribosomal protein (RP) genes. It contains data from humans and other organisms. Users can search this database by gene name and organism. Each record includes sequences (genomic, cDNA, and amino acid sequences), intron/exon structures, genomic locations, and information about orthologs. In addition, users can view and compare the gene structures from different organisms and make multiple amino acid sequence alignments. RPG also provides information on small nucleolar RNAs (snoRNAs) that are encoded in the introns of RP genes.

Proper citation: RPG - Ribosomal Protein Gene database (RRID:SCR_007904) Copy   

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http://point.bioinformatics.tw/Welcome.do

POINT is a protein-protein interaction database. It includes annotation of interologs and protein phsophorylation. This work analyzes the applicability of orthologs-based PPI prediction and provide the theoretical upper-bound of this approach.

Proper citation: POINT: Prediction Of INTeractome (RRID:SCR_007866) Copy   

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http://ukcrop.net/

The UK Crop Plant Bioinformatics Network (UK CropNet) was established in 1996 as part of the BBSRC''s Plant and Animal Genome Analysis special initiative. Our focus is the development, management, and distribution of information relating to comparative mapping and genome research in crop plants. Find out more about our background or read our UK CropNet paper published in Nucleic Acids Research (pdf reader required).This site hosts a wide range of databases and software developed by UK CropNet, as well as hosting many other plant databases developed in the USA. You can perform a keyword text search across all of these databases or use our UK CropNet BLAST server to search against all of the sequences in these databases.

Proper citation: CropNet (RRID:SCR_007987) Copy   

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http://polya.umdnj.edu/

A database of mRNA polyadenylation sites. PolyA_DB version 1 contains human and mouse poly(A) sites that are mapped by cDNA/EST sequences. PolyA_DB version 2 contains poly(A) sites in human, mouse, rat, chicken and zebrafish that are mapped by cDNA/EST and Trace sequences. Sequence alignments between orthologous sites are available. PolyA_SVM predicts poly(A) sites using 15 cis elements identified for human poly(A) sites.

Proper citation: PolyA DB (RRID:SCR_007867) Copy   

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http://www.rnai.org/

It provides access to results from RNAi interference studies in C. elegans, including images, movies, phenotypes, and graphical maps. RNAiDB contains all published RNAi experiments in C. elegans that have been deposited in WormBase, including data from the literature and published large-scale RNAi studies. RNAi to gene mappings for all experiments have been re-analyzed using ePCR and/or a sliding n-mer window method to identify all genes in different genomic locations that may potentially be inhibited by each experiment. Gene maps showing canonical and putative alternate mappings are displayed graphically on RNAi Experiment and Gene/ORF card pages.

Proper citation: RNAiDB (RRID:SCR_007900) Copy   

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https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 29,2025. Electroencephalogram (EEG) data recorded from invasive and scalp electrodes. The EEG database contains invasive EEG recordings of 21 patients suffering from medically intractable focal epilepsy. The data were recorded during an invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. In eleven patients, the epileptic focus was located in neocortical brain structures, in eight patients in the hippocampus, and in two patients in both. In order to obtain a high signal-to-noise ratio, fewer artifacts, and to record directly from focal areas, intracranial grid-, strip-, and depth-electrodes were utilized. The EEG data were acquired using a Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-to-digital converter. Notch or band pass filters have not been applied. For each of the patients, there are datasets called ictal and interictal, the former containing files with epileptic seizures and at least 50 min pre-ictal data. the latter containing approximately 24 hours of EEG-recordings without seizure activity. At least 24 h of continuous interictal recordings are available for 13 patients. For the remaining patients interictal invasive EEG data consisting of less than 24 h were joined together, to end up with at least 24 h per patient. An interdisciplinary project between: * Epilepsy Center, University Hospital Freiburg * Bernstein Center for Computational Neuroscience (BCCN), Freiburg * Freiburg Center for Data Analysis and Modeling (FDM).

Proper citation: Electroencephalogram Database: Prediction of Epileptic Seizures (RRID:SCR_008032) Copy   

•   Source: SciCrunch Registry

http://www.modelling.leeds.ac.uk/sb/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of known ligand binding sites within the PDB which is navigable by PDB identifier or ligand 3 letter code e.g. NAD. Each binding site has a frequently updated register of structurally similar binding sites sharing atomic similarity detected by geometric hashing. Multiple alignments, structural superpositions and links to other structural databases are also available enabling further analysis. The rapid expansion of structural information for protein-ligand binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross reactivity and toxicity. We have developed a large database of ligand binding sites extracted automatically from the Protein Data Bank. This has been combined with a method for calculating binding site similarity based on geometric hashing to create a relational database for the retrieval of site similarity and binding site superposition. It contains an all-against-all comparison of binding sites and holds known protein-ligand binding sites, which are made accessible to data mining. Here we demonstrate its utility in two structure-based applications: in determining site similarity and in aiding the derivation of a receptor-based pharmacophore model.

Proper citation: SitesBase (RRID:SCR_007932) Copy   

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http://egg.umh.es/databases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 25, 2013. RISSC is a database of ribosomal 16S-23S spacer sequences intended mainly for molecular biology studies in typing, phylogeny and population genetics. Ribosomal spacers have proven to be extremely useful tools for typing and identifying closely related prokaryotes due to their high variability in size and/or sequence, much more so than the flanking 16S and 23S rRNA genes. These genes are commonly used to establish molecular relationships among microbes at a taxonomic level of species or higher (e.g genus, domain...). However their internal transcribed spacers (ITS) are much more useful to discriminate at the species or even strain level. Currently, many published papers are showing the growing importance of these regions of the ribosomal operon in these types of studies. A second, much shorter, ribosomal spacer can be found between rRNA genes 23S and 5S, also of phylogenetic interest. We intend to incorporate them into the database in the near future. By creating RISSC, our intention is to provide the scientific community with a comprehensive set of ribosomal spacer sequences, fully edited and characterized with a key feature as is the presence/absence of tRNA genes within them, ready to be used and compared with their own ITS sequences.

Proper citation: RISSC - Ribosomal Internal Spacer Sequence Collection (RRID:SCR_007898) Copy   

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

The SKY/M-FISH and CGH databases provide a public platform for investigators to share and compare their molecular cytogenetic data. The database is open to everyone and all users can view an individual investigator's public data or compare public cases from different investigators. Those wishing to contribute their own data must register and can choose to keep their data private for a period not to exceed two years. Spectral Karyotyping (SKY), Multiplex Fluorescence In Situ Hybridization (M-FISH) and Comparative Genomic Hybridization (CGH) are complementary fluorescent molecular cytogenetic techniques. SKY/M-FISH permits the simultaneous visualization of each human or mouse chromosome in a different color, facilitating the identification of chromosomal aberrations. CGH utilizes the hybridization of differentially labeled tumor and reference DNA to generate a map of DNA copy number changes in tumor genomes.

Proper citation: SKY/M-FISH/CGH (RRID:SCR_007933) Copy   

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http://sisyphus.mrc-cpe.cam.ac.uk

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A collection of manually curated protein structural alignments and their interrelationships. Each multiple alignment within the SISYPHUS database consists of structurally similar regions common to a group of proteins. These regions range from oligomeric biological units, or individual domains to fragments of different size representing either internal structural repeats or motifs common to structurally distinct proteins. The SISYPHUS multiple alignments are displayed with SPICE, a browser that provides an integrated view of protein sequences, structures and their annotations.

Proper citation: SISYPHUS (RRID:SCR_007930) Copy   

•   Source: SciCrunch Registry

http://RiceGAAS.dna.affrc.go.jp/

A rice genome automated annotation system. This system integrates programs for prediction and analysis of protein-coding gene structure. Integrated softwares are coding region prediction programs ( GENSCAN, RiceHMM, FGENESH, MZEF ), splice site prediction programs (SplicePredictor ), homology search analysis programs ( Blast, HMMER, ProfileScan, MOTIF ), tRNA gene prediction program ( tRNAscan-SE ), repetitive DNA analysis programs ( RepeatMasker, Printrepeats ), signal scan search program ( Signal Scan ), protein localization site prediction program ( PSORT ), and program of classification and secondary structure prediction of membrane proteins ( SOSUI ). Blast against full-length cDNA sequences of japonica rice is integrated. The full-length rice cDNA sequence is provided by KOME database. Interpretation of the coding region is fully automated and gene prediction is accomplished without manual evaluation and modification. Therefore some differences exist between the predicted genes by the system and the manually predicted genes included in the GenBank entries. Please see "comparison table of gene prediction", http://RiceGAAS.dna.affrc.go.jp/rga-bin/col_accur.pl in detail. Further, a unique function is automatically assigned for predicted gene by GFSelector based on the protein homology of the gene. Additionally, the keyword search from the functions predicted by GFSelector is provided.

Proper citation: RiceGAAS (RRID:SCR_007896) Copy   

•   Source: SciCrunch Registry

http://retroryza.fr/

This database provides some information and resources related to LTR-retrotransposons in rice genome. The availability of the pseudomolecules of the Asian cultivated rice (Oryza sativa ssp. japonica cv. Nipponbare) allowed the construction, for the first time, of a non-redundant database of LTR retrotransposon sequences for an agronomically important plant species. 242 distinct families are curated, of which 194 have not been described elsewhere. These newly identified sequences, representing mainly low copy number elements, were identified by in-silico approaches. Reference molecules of each LTR retrotransposon family were characterized, annotated and deposited in RetrOryza. Further analysis will be focused on the identification and the annotation of LTR retrotransposons of several species within the Oryza genus.

Proper citation: RetrOryza.org (RRID:SCR_007890) Copy   

•   Source: SciCrunch Registry

http://sirna.cgb.ki.se

This is a site with links to several siRNA services, including siRNA base sequence searches, specificity searches, known siRNA molecule searches, and target sequences. One of the sites, called siSVM, allows users to predict efficacy of siRNAs given their base sequence using features derived from the siRNA sequence. siSVM is designed to allow common methods of siRNA design to be included in the search. This includes motif rules,energy conditions and specificity searching. The second site it links to, siRNA specificity prediction, allows users to perform a specificity search for siRNAs to avoid off-target effects. SpecificityServer is designed to help you identify potential non-specific matches to your siRNA. It incorporates the latest information about non-specific matches (sequence-specific only). The third site it links to, siRNAdb, is a database of known siRNA molecules. It provides a list of sirnaID, target, geneID, geneAcc, TargetStart, and targetEnd.Category: RNA sequence databases

Proper citation: siRNAdb (RRID:SCR_007929) Copy   

•   Source: SciCrunch Registry

http://sirecords.umn.edu/siRecords/

On this site, you can search siRNA records, design siRNAs, or submit siRNA records resulting from your own study (requires register/login). Small interfering RNAs (siRNAs) are a class of 20-25 nucleotide-long double-stranded RNAs, and they are widely used as gene knock-down tool in molecular genetics, functional genomics, and drug discovery studies. However, despite numerous efforts, the design of potent siRNA remains inadequate. Design rules resulting from different studies often disagree with each other, and are often unsatisfactory. Typically, only about 75-80% siRNAs designed based on current rules result in >50% knock-down efficacy. Observing these difficulties, we have established this database of experimentally validated mammalian siRNAs with efficacy ratings. As of August 18, 2008, 17,192 records of experimentally validated siRNAs, targeting 5,086 genes, originated from 6,122 independent studies are hosted in siRecords.

Proper citation: siRecords (RRID:SCR_007928) Copy   

•   Source: SciCrunch Registry

http://pulm.bumc.bu.edu/siegeDB

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A database that provides access to data from several gene expression profile analysis results of smokers and non-smokers. In the experiment, researchers first obtained brushings from intra-pulmonary airways (the right upper lobe carina) and scrapings from the buccal mucosa, from normal smoking and non-smoking volunteers. RNA was isolated from these samples and gene expression profiles from intra-pulmonary airway epithelial cells were analyzed using Affymetrix U133A human gene expression arrays. All microarray data from these experimentshave been stored, preprocessed and analyzed in a relational MySQL database that is accessible through this website.

Proper citation: SIEGE- Smoking Induced Epithelial Gene Expression (RRID:SCR_007925) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/simap/

It provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith Waterman algorithm. SimpleSIMAP and AdvancedSIMAP retrieve homologs for given protein sequences that need to be contained in the SIMAP database. While SimpleSIMAP provides only selected parameters and preconfigured search spaces, the AdvancedSIMAP allows the user to specify search space, filtering and sorting parameters in a flexible manner. Both types of queries result in lists of homologs that are linked in turn to their homologs. So the web interfaces allow users to explore quickly and interactively the protein world by homology. Sponsors: SIMAP is supported by the Department of Genome Oriented Bioinformatics of the Technische Universitt Mnchen and the Institute for Bioinformatics of the GSF-National Research Center for Environment and Health.

Proper citation: SIMAP (RRID:SCR_007927) Copy   

•   Source: SciCrunch Registry

http://silkworm.genomics.org.cn/

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 on August 20,2019.A database of integrated genome resources for the silkworm, Bombyx mori. This database provides access to not only genomic data including functional annotation of genes, gene products and chromosomal mapping, but also extensive biological information such as microarray expression data, ESTs and corresponding references. SilkDB will be useful for the silkworm research community as well as comparative genomics. Recently, an international collaboration has been launched to assemble a complete silkworm genome sequence, which is based on the 6� and 3� draft genome sequences created by Chinese group and Japanese group in 2004 (Mita et al., 2004; Xia et al., 2004), respectively. The genome assembly quality has been greatly improved. Base on a high density SNP genetic map, over 80% of genome sequence could be mapped on 28 chromosomes of the silkworm. The first version of SilkDB was released in 2004. Since that time, the silkworm has become a focus in insect research community and the study of silkworm has been greatly accelerated. Now, we are happy to announce the release of a new version of SilkDB, which updated all of the data, added new information of genome sequence and genes, and provides new tools to facilitate use of the genome database.

Proper citation: SilkDB (RRID:SCR_007926) Copy   

•   Source: SciCrunch Registry