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https://omictools.com/sno-scarnabase-tool

A curated database for small nucleolar RNAs and small cajal body-specific RNAs. It presents sno/scaRNA-associated genetic and functional data and provides access to several other database sources via web-accessible search interfaces. Consisting of 1979 sno/scaRNA records obtained from 85 organisms, sno/scaRNAbase is a combination of systematic literature curation and annotation effort. small nucleolar RNA, small cajal body-specific RNA

Proper citation: Sno/scaRNAbase (RRID:SCR_007938) Copy   

•   Source: SciCrunch Registry

http://www.compbio.dundee.ac.uk/SNAPPI/downloads.jsp

An object-oriented database of domain-domain interactions observed in structural data. SNAPPI-DB is a useful resource for any analysis of structures but has been opitmised for analysis on domain-domain interactions and domain-ligand interactions. The database has already been employed for 3 studies on the properties of domain-domain interactions and is currently being employed to train a protein-protein interaction predictor and a functional residue predictor. SNAPPI-DB has several features which are not available in other databases, including links to the MSD, speed, being object oriented, storage of multiple domain definitions, and storage of Protein Quaternary Structures (PQS).

Proper citation: SNAPPI (RRID:SCR_007937) Copy   

•   Source: SciCrunch Registry

https://www.oxfordjournals.org/our_journals/nar/database/summary/954

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 19, 2016. A database for the study of protein inter-atomic distance distribution. Currently, the distances are extracted from the protein structures determined through X-ray Crystallography, but they could also be obtained from NMR structural models. The known structures with the resolution higher than 2A and less than 70% sequence similarities are selected. Each type of distances is specified in terms of the types of the atoms it involves, the types of the residues containing the atoms, and the types of the residues in between the two end residues in sequence. An automated system is built to generate and process the data dynamically. The system consists of two levels of databases. The first one stores the sequence and structure information for a large set of high-resolution protein structures, with a similar data structure as the structural data represented in the PDB Data Bank. The second one stores the information for the distance distributions, with each record corresponding to a distribution function. The second database is built dynamically from the first one. The database can provide structural information in terms of distance distributions to structural biologists. Such information can be valuable for the study of many fundamental biological problems including protein structure prediction and determination, protein dynamics simulation, molecular design, protein structural analysis and classification, etc.

Proper citation: PIDD (RRID:SCR_007854) Copy   

•   Source: SciCrunch Registry

http://www.bioinfodatabase.com/pint/

A protein-protein interactions thermodynamic database which contains data of several thermodynamic parameters along with sequence and structural information experimental conditions and literature information. Each entry contains numerical data for features of the interacting proteins such as the free energy change, dissociation constant, association constant, enthalpy change, and heat capacity change. PINT includes: the name and source of the proteins involved in binding, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, and experimental conditions such as buffers, ions and additives, and literature information. PINT is cross-linked with other related databases such as PIR, SWISS-PROT, PDB and the NCBI PUBMED literature database.

Proper citation: PINT (RRID:SCR_007856) Copy   

•   Source: SciCrunch Registry

http://www.phytome.org

An online comparative genomics resource that is built upon publicly available sequence and map information from a diverse set of plant species, with a focus on the angiosperms, or flowering plants. It provides an interface to the results from a variety of phylogenomic analyses. Phytome is designed to facilitate functional genomics, molecular breeding and evolutionary studies in model and non-model plant species. Currently, Phytome contains phylogenetic and functional information for predicted protein sequences ("Unipeptides"). Future development will incorporate data and tools for analysis of sequence-based comparative maps.

Proper citation: Phytome (RRID:SCR_007852) Copy   

•   Source: SciCrunch Registry

http://topdb.enzim.hu

Collection of transmembrane protein datasets containing experimentally derived topology information from the literature and from public databases. Web interface of TOPDB includes tools for searching, relational querying and data browsing, visualisation tools for topology data.

Proper citation: Topology Data Bank of Transmembrane Proteins (RRID:SCR_007964) Copy   

•   Source: SciCrunch Registry

http://www.tassdb.info/

TassDB stores extensive data about alternative splice events at GYNGYN donors and NAGNAG acceptors. Currently, 114,554 tandem splice sites of eight species are contained in the database, 5,209 of which have EST/mRNA evidence for alternative splicing. Users can search by Transcript Accession Number and Gene Symbol, SQL Query, and Tandem Donor/Tandem Acceptor pairs.

Proper citation: TAndem Splice Site DataBase (RRID:SCR_007961) Copy   

•   Source: SciCrunch Registry

http://targetdb.pdb.org/

TargetDB, a target registration database, provides information on the experimental progress and status of targets selected for structure determination. Search sequences from the PSI Structural Genomics Centers and other Structural Genomics projects.For more information about how these proteins were cloned, expressed, purified, or other experimental protocols please go to the Protein expression, purification, and crystallization DataBase.

Proper citation: TargetDB: Structural Genomics Target Search (RRID:SCR_007960) Copy   

•   Source: SciCrunch Registry

http://tbestdb.bcm.umontreal.ca/searches/welcome.php

The taxonomically broad EST database TBestDB serves as a repository for EST data from a wide range of eukaryotes, many of which have previously not been thoroughly investigated. Users can search by annotated name, EC#, and view datasets that contain classification hierarchies for pathways, for reactions (the enzyme nomenclature system), for compounds, and for genes. Most of the data contained in TBestDB has been generated by the labs of the Protist EST Program located in six universities across Canada.

Proper citation: Taxonomically Broad EST Database (RRID:SCR_007962) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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://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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.plprot.ethz.ch/

A plastid protein database. It integrates data from large scale proteome analyses of different plastid types.These include etioplasts, chloroplasts, chromoplasts and the undifferentiated proplastid-like organelles of tobacco BY2 cells. This comparison allows establishing a core proteome that is common to all plastid types and provides furthermore information about plastid type-specific functions.

Proper citation: PLprot (RRID:SCR_007864) Copy   

•   Source: SciCrunch Registry