bioinformatics-databases

The Small Subunit rRNA Modification Database provides a listing of reported post-transcriptionally modified nucleosides and sequence sites in small subunit rRNAs from bacteria, archaea and eukarya. Data are compiled from reports of full or partial rRNA sequences, including RNase T1 oligonucleotide catalogs reported in earlier literature in studies of phylogenetic relatedness. Options for data presentation include full sequence maps, some of which have been assembled by database curators with the aid of contemporary gene sequence data, and tabular forms organized by source organism or chemical identity of the modification. A total of 32 rRNA sequence alignments are provided, annotated with sites of modification and chemical identities of modifications if known, with provision for scrolling full sequences or user-dictated subsequences for comparative viewing for organisms of interest. The database can be accessed through the World Wide Web at http://medlib.med.utah.edu/SSUmods.

In bacteria, small (~30-500 nt) non-coding RNAs (sRNAs) are the most abundant class of post-transcriptional regulators that are involved in diverse processes including quorum sensing, stress response, virulence and carbon metabolism. Based on the target molecules, sRNAs can be divided into two major groups: (i) mRNA-binding antisense sRNAs and (ii) protein-binding sRNAs. The antisense RNAs can further be categorized as cis-encoded antisense sRNAs, which are completely complementary to their targets, and trans-encoded antisense sRNAs, which are only partially complementary to their targets. In any case, the interaction between antisense RNAs and target mRNAs could direct a plethora of biological regulatory circuits. Recent developments in high-throughput techniques, such as genomic tiling arrays and RNA-Seq have provided invaluable insights into the detection and characterization of bacterial sRNAs. However, a comprehensive bacterial sRNA database is not yet available, especially for integrating and analyzing high-throughput sequencing data.

The KNOTTIN database provides standardized data on the knottin structural family (also referred to as the "Inhibitor Cystine Knot (ICK) motif/family/fold").

Read below essential information about knottins
Look at current statistics on the database content
Look at current statistics on loop lengths

Comments, suggestions or supplementary data are welcome.
When using this website and database, please cite: J. Gracy et al. Nucleic Acids Res. 2008, 36:D314-9, and J.-C. Gelly et al. Nucleic Acids Res. 2004, 32:D156-9.

KBDOCK is a 3D database system that defines and spatially clusters protein binding sites for knowledge-based protein docking. KBDOCK extracts protein domain-domain interaction (DDI) and domain-peptide interaction (DPI) information from the PDB using the PFAM domain classification in order to analyse the spatial arrangements of DDIs and DPIs by Pfam family, and to propose structural templates for protein docking.

Given a query Pfam domain, KBDOCK shows:

• a non-redundant list of DDIs involving the query domain, grouped by their binding site.
• a Jmol view showing the DDIs placed in the coordinate frame of the query domain. You can choose to view one DDI at a time or a group of DDIs which share a similar binding site according to our binding site direction vector algorithm (see references).
• a consensus sequence alignment of the domains. Each sequence is annotated with core and rim interacting residues, and also the binding site centre residue.

Given a query Pfam domain, KBDOCK can also show DDIs involving different but structurally similar Pfam domains. For each Pfam family, KBDOCK stores a list of "Pfam neighbours" which is calculated using the Kpax structure alignment algorithm. Given two query domain structures, if full-homology (FH)† DDI templates exist in KBDOCK, then for each distinct interface, KBDOCK provides:

• the best FH template (based on overall sequence identity) and the corresponding docking model.
• a Jmol view of the query domains superposed onto the FH template.
• a pairwise sequence alignment of the query domains and their corresponding template domain, showing the core, rim, and centre binding site residues.

If FH DDIs do not exist or if only one query domain structure is given, KBDOCK finds semi-homology (SH)† DDI templates. For each query domain and for each distinct binding site, it provides:

• the best SH template (based on domain sequence identity).
• a Jmol view of the query domain superposed onto the SH template. A centre binding site residue is proposed.
• a pairwise sequence alignment of the query domain and its corresponding template domain, showing the core, rim, and centre binding site residues.

KBDOCK can also find DDIs involving structurally similar Pfam domains to the query domains using pre-calculated Pfam neighbour lists.

NRED integrates annotated expression data from various sources. Use this form to filter expression results data based on probe characteristics and/or the values of the expression data. If no experimental result set is selected, the form can also be used to search the probe table. All search fields are optional. For help and descriptions of the different fields, simply hover your mouse over the form labels. To reference NRED, please cite Dinger et al., 2008, Nucleic Acid Res.

Welcome to dbPHSP (http://jjwanglab.org/dbpshp)(hg19/GRCh37). The database contained curated publications about positive selection in different human populations, which consisted of over 15,000 loci from either publications attempting to study positively selected genomic locus and gene related to specific functions/traits/diseases, or publications to detect the genome-wide selective signals with different statistical methods.

It also includes 15 statistical terms for each single nucleotide polymorphism site from the HapMap III and 1000 Genomes Project genotyping data. These attributes include variant allele frequency, variant heterozygosity, within population diversity, haplotype homozygosity, long-range haplotypes, pairwise population differentiation and evolutionary conservation. We also provided interactive pages for visualization and annotation of different selective signals.

The JASPAR CORE database contains a curated, non-redundant set of profiles, derived from published collections of experimentally defined transcription factor binding sites for eukaryotes. The prime difference to similar resources (TRANSFAC, etc) consist of the open data acess, non-redundancy and quality.

When should it be used? When seeking models for specific factors or structural classes, or if experimental evidence is paramount

The iPfam database is a catalog of protein family interactions, including domain and ligand interactions, calculated from known structures. We take all known structures in the PDB, identify the protein domain(s) that are present in each structure and look for bonds between each domain and any other domains and/or small chemical ligands found in the structure. The bonds are automatically estimated based on both geometric and chemical properties of the two atoms involved.

InterDom is a database of putative interacting protein domains derived from multiple sources, ranging from domain fusions (Rosetta Stone), protein interactions (DIP and BIND), protein complexes (PDB), to scientific literature (MEDLINE).

Interdom focuses on providing supporting evidence for validating and annotating detected protein interactions and complexes based on putative protein domain interactions. InterDom enhances the quality of in silico derivations by adopting an integrative strategy, assigning higher confidence to domain interactions that are independently derived from different data sources and methods.

The current version of InterDom is 2.0, released July 31, 2007. Click here for the statistics data of InterDom v2.0

1. What kind of information can I find inside this database?

SUPERTARGET is a database developed in the first place to collect informations about drug-target relations. It consist mainly of three different types of entities:

1. DRUGS
2. PROTEINS
3. SIDE-EFFECTS.

Beside this, informations about pathways and ontologies can be gained. Separate section is dedicated to a special soubgroup of the the targets - the cytochromes (CYPs) P450. All these entities are connected between each other through drug-protein, protein-protein and drug-side-effect relations and include rich annotation about the source, ID's, physical properties, references and much more. 

Proteins are retrieved from UniProt and are displayed with synonyms and organism information. Also information about target-target interactions and sequenc similarities between the targets are involved. Additional information as 3D-structures from PDB and EC-numbers are given if available. Drugs gained from SuperDrugwere mapped with BindingDB, integrated to SUPERTARGET and assigned metadata as ATC-codes (Anatomical Therapeutic Chemical), structure information and binding affinities. The side-effects contained in the database were fetched from SIDER and related to the drugs.

2. How was the data in the database created?

The aim of SUPERTARGET is to offer an at most comprehensive datasets. Here for drug-target relations from different well-known databases as DrugBank,BindingDB and SuperCyp were integrated. To enhance the completeness of the dataset additional newly explored relations were incorporated, too. The new information was obtained in two steps:

1. Firstly, text-mining algorithms were applied to sort all PubMed listed papers by their relevance for drug-target relations.
2. In a second step, the 7,000 papers with the highest rank were manually revised.

Protein-protein interaction data was obtained from ConsensusPathDB which integrates physical protein-protein interactions, metabolic and signaling reactions and gene regulatory interactions. Information on complex composition comes from Corum a protein complex database. Detailed overview about sources and relations between the entities presented by SUPERTARGET is summarized in following figure :

Listeria species are ubiquitous in the environment and often contaminate foods because they grow under conditions used for food preservation. Listeria monocytogenes, the human and animal pathogen, causes Listeriosis, an infection with a high mortality rate in risk groups such as immune-compromised individuals. Furthermore, L.monocytogenes is a model organism for the study of intracellular bacterial pathogens. The publication of its genome sequence and that of the non-pathogenic species Listeria innocua initiated numerous comparative studies and efforts to sequence all species comprising the genus. The Proteome database LEGER (http://leger2.gbf.de/cgi-bin/expLeger.pl) was developed to support functional genome analyses by combining information obtained by applying bioinformatics methods and from public databases to improve the original annotations. LEGER offers three unique key features: (i) it is the first comprehensive information system focusing on the functional assignment of genes and proteins; (ii) integrated visualization tools, KEGG pathway and Genome Viewer, alleviate the functional exploration of complex data; and (iii) LEGER presents results of systematic post-genome studies, thus facilitating analyses combining computational and experimental results. Moreover, LEGER provides an unpublished membrane proteome analysis of L.innocua and in total visualizes experimentally validated information about the subcellular localizations of 789 different listerial proteins.

mESAdb is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression datasets. mESAdb analysis modules allow a) mining of selected microRNA expression datasets for a list of microRNAs; and b) pair-wise multivariate analysis of expression datasets within and between taxa; and c) association of microRNA lists or microRNAs with a given motif with annotation databases, HUGE Navigator [1], KEGG [2], and GO [3]. The use of existing and customized R packages facilitates future addition of datasets and analysis tools. Furthermore, the ability to upload and analyze user-specified datasets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.

KineticDB is the thoroughly curated database of protein folding kinetics which contains currently experiments on 87 unique proteins and about hundred of mutants. The main goal of KineticDB is to provide users with the diverse set of protein folding rates known from experiment. Currently the database contains kinetic data on single domain proteins, separate protein domains and short polypeptides without S-S bonds in the native structure.

We hope that the database will be useful for you. Help us make the database as wide and up-to-date as possible - send us references containing new kinetic data! We will be grateful for any contribution to the database from the community: for bug reports, for new kinetic data and for any questions

How to cite:
Bogatyreva N.S., Osypov A.A., Ivankov D.N. (2009). KineticDB: a database of protein folding kinetics. NAR, 37:D342-D346.

With the completion of the sequencing of the genomes of human and other organisms, attention has focused on the characterization and function of proteins, the products of genes. The availability of sequence data and the growing impact of structural biology on biomedical research have prompted scientific groups from several countries to undertake projects in the emerging field of structural genomics.

The objective is to make these structures widely available for clinical and basic studies that will expand the knowledge of the role of proteins both in normal biological processes and in disease. The National Institute of General Medical Sciences (NIGMS) played a major role in the early planning for structural genomics and in 1999 organized a national program, the Protein Structure Initiative.

The PSI program officially concluded in 2015, having determined nearly 7000 protein structures, developed ~450 new or improved technologies, and wrote over 2200 publications. The impact of this work enabled the greater biological and biomedical communities.

The Fungal Program scales up sequencing and analysis of fungal genomes to explore the diversity of fungi important for energy and the environment, and to promote functional studies on a system level. Encoded in the genomes of the organisms of the kingdom Fungi are biological processes with high relevance to the Department of Energy missions in bioenergy production, carbon cycling and biogeochemistry. Combining new sequencing technologies and comparative genomics analysis, we work on large and complex sequencing projects such as surveying the broad phylogenetic and ecological diversity of fungi, and capturing genomic variation in natural populations and engineered strains. This approach allows us to build a foundation for translating the genomic potential of fungi into practical applications.

Optimal global pairwise alignments from about 270,000 ribosomal RNA (rRNA) internal transcribed spacer 2 (ITS2) sequences - all against all - have been generated in order to model ITS2 secondary structures based on sequences with known structures. Via 60,000 known ITS2 sequences that fit a common core of the ITS2 secondary structure described for the eukaryotes (Schultz et al. 2005), homology based modeling (Wolf et al. 2005) and reannotation procedures revealed in addition more than 150,000 homologous structures that could not be predicted by standard RNA folding programs.

InvFEST aims to become a reference site to share information and collaborate towards the complete characterization of human polymorphic inversions. It is a data-warehouse implementation that integrates several data of interest related to inversions with an online analytical processing engine (OLAP) to gather information and compute a report of each inversion.

The InvFEST database stores and merges inversion predictions from healthy individuals into a non-redundant dataset by overlapping the position of the breakpoints of each prediction and taking into account the resolution of each study. Most predictions come from mapping information of paired-end sequences (PEM) obtained by different studies of the literature, which in some cases have been reanalyzed by GRIAL, a program specifically designed to detect inversions from PEM data. Moreover, it stores information of validations and genotyping assays, frequency in different populations, association with genes and segmental duplications, and the evolutionary history of the inversions.

The database will keep on updating information by incorporating new predictions, validations, genotyping data, and any other information, either extracted from peer reviewed research studies or generated in our lab. We always welcome your suggestions and comments.

The InvFEST database is an outcome of the INVFEST project, supported by the European Research Council (ERC) Starting Grant 243212 under the European Union Seventh Research Framework Programme (FP7).

CTCF is a highly conserved transcriptional regulator protein that performs diverse functions such as regulating gene expression and organizing the 3D structure of the genome. Here, we describe recent updates to a database of CTCF-binding sites, CTCFBSDB (http://insulatordb.uthsc.edu/), which now contains almost 15 million CTCF-binding sequences in 10 species. Since the original publication of the database, studies of the 3D structure of the genome, such as those provided by Hi-C experiments, have suggested that CTCF plays an important role in mediating intra- and inter-chromosomal interactions. To reflect this important progress, we have integrated CTCF-binding sites with genomic topological domains defined using Hi-C data. Additionally, the updated database includes new features enabled by new CTCF-binding site data, including binding site occupancy and the ability to visualize overlapping CTCF-binding sites determined in separate experiments.