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

SYSTOMONAS is a comprehensive database of molecular networks in Pseudomonas focusing on Pseudomonas aeruginosa. We use a systems biology approach to get a deeper understanding of all cellular processes of P. aeruginosa during infection. Our long term goal is the development of a dynamic model simulating P. aeruginosa during infection. The basis for such an approach is SYSTOMONAS, a comprehensive database that includes systems data from all levels of analysis as microarray and proteomics data, metabolite measurements, sequence data, gene-regulatory networks and enzyme data. Therefore, we started with metabolomics analysis and extended to transcriptomics, genomics, and proteomics aspects. Along with the wet lab results additional data is stored, which is extracted from literature or derived from other external databases. Major sources of SYSTOMONAS are KEGG, PRODORIC, BRENDA (see section ''Sources''), which are partly stored via the data warehouse system and partly dynamically connected via SOAP, a platform-independent data transfer protocol. Comparing a Pseudomonas protein of interest with other well-characterized proteins may deliver useful insights into the evolution, distribution, and species specific function. Therefore, we searched for all deduced proteins of the SYSTOMONAS database for orthologous proteins in other Pseudomonas species to obtain orthologous protein clusters. Pseudomonas aeruginosa, systems biology, transcriptomics, genomics, proteomics

Proper citation: SYSTOMONAS: SYSTems biology of pseudOMONAS (RRID:SCR_007958) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1669717/

This is a dataset of clinical HIV sequences, including a method of decoding the evolutionary pathways by which HIV evolves drug resistance. "Fitness landscape" describing how HIV proteins can evolve, is shown as a kinetic network. Drug resistance is a major problem in the treatment of AIDS, due to the very high mutation rate of human immunodeficiency virus (HIV) and subsequent rapid development of resistance to new drugs. Identification of mutations associated with drug resistance is critical for both individualized treatment selection and new drug design. We have performed an automated mutation analysis of HIV Type 1 (HIV-1) protease and reverse transcriptase (RT) from approximately 50,000 AIDS patient plasma samples sequenced by Specialty Laboratories Inc. from 1999 to mid-2002. This dataset provides a nearly complete mutagenesis of HIV protease and enables the calculation of statistically significant Ka/Ks values for each individual amino acid mutation in protease and RT. Positive selection (i.e., Ka/Ks>1 indicating increased reproductive fitness) detected 19 of 23 known drug-resistant mutation positions in protease and 20 of 34 such positions in RT. We also discovered 163 new amino acid mutations in HIV protease and RT that are strong candidates for drug resistance or fitness. Our results match available independent data on protease mutations associated with specific drug treatments and mutations with positive reproductive fitness, with high statistical significance (the P values for the observed matches to occur by random chance are 1e-5.2 and 1e-16.6, respectively). Our data indicate that positive selection mapping is an analysis that can yield powerful insights from high-throughput sequencing of rapidly mutating pathogens. This database has been made possible by the generous contribution of HIV sequence chromatograms by Specialty Laboratories, Inc.

Proper citation: The HIV Positive Selection Mutation Database (RRID:SCR_007957) Copy   

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http://smartdb.bioinf.med.uni-goettingen.de/

It collects information about scaffold/matrix attached regions and the nuclear matrix proteins that are supposed be involved in the interaction of these elements with the nuclear matrix. It covers the whole range from yeast to human. The SMAR table gives information on individual sequence elements of experimentally proven matrix binding activity. In release 2.3 it contains 500 entries. The sequences therein can be assigned to more than 150 genes from eukaryotic species ranging from yeast to human. The SMARbinder table contains 96 entries (release 2.3), but this figure does not reflect the number of independent S/MAR binding proteins. First of all, homologous factors from different species such as human and mouse SATB1 are given in different entries since they may differ in some aspects. Moreover, products of distinct but very similar genes or alternative splice products are included as separate entries. In some cases a more general term defining a S/MAR-binding activity may appear as one entry eventhough it might be composed of two or more subunits. The SMARbinder table will only contain those proteins of nuclear localization for which an interaction with a well defined S/MAR has been shown. Besides that the SMARbinder table will also include proteins that are proven components of the the salt-resitent (LIS-resistent) nuclear matrix. Gene entries, besides of giving the gene name in a long and a short (abbreviated) denomination, collect all links to individual S/MARs given in S/MARt DB and/or provide pointers to "S/MARbinders". The entries also contain links to transcription factor binding sites listed in TRANSFAC and give a link to the corresponding TRRD entry describing the regulatory features of the gene on different hierarchical levels.

Proper citation: S/MARt DB (RRID:SCR_007910) Copy   

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http://pubanatomy.org

An integrated exploration of biomedical literature and data. An anatomy viewer can be accessed and searches of PubMed literature are visualized as to the anatomical regions that they effect. PubAnatomy takes advantage of the 25-micron voxel level mouse brain structure annotation generated by the Allen Brain Institute and integrates Allen Brain Atlas gene expression data, relationships between brain regions and diseases for more efficient exploration of Medline database and gene expression data.

Proper citation: PubAnatomy (RRID:SCR_007999) Copy   

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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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