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http://mint.bio.uniroma2.it/virusmint/

A virus protein interactions database that collects and annotates all the interactions between human and viral proteins and integrates this information in the human protein interaction network. It uses the PSI-MI standard and is fully integrated with the MINT database. You can search for any viral or human protein by entering either common names or database identifiers or display a complete viral interactome.

Proper citation: VirusMINT (RRID:SCR_005987) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/SCAR/

A web tool to create, display and manipulate structures of small molecules, proteins and DNA.

Proper citation: SCAR (RRID:SCR_006227) Copy   

•   Source: SciCrunch Registry

http://www.kabatdatabase.com/

The Kabat Database determines the combining site of antibodies based on the available amino acid sequences. The precise delineation of complementarity determining regions (CDR) of both light and heavy chains provides the first example of how properly aligned sequences can be used to derive structural and functional information of biological macromolecules. The Kabat database now includes nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules, and other proteins of immunological interest. The Kabat Database searching and analysis tools package is an ASP.NET web-based portal containing lookup tools, sequence matching tools, alignment tools, length distribution tools, positional correlation tools and much more. The searching and analysis tools are custom made for the aligned data sets contained in both the SQL Server and ASCII text flat file formats. The searching and analysis tools may be run on a single PC workstation or in a distributed environment. The analysis tools are written in ASP.NET and C# and are available in Visual Studio .NET 2003/2005/2008 formats. The Kabat Database was initially started in 1970 to determine the combining site of antibodies based on the available amino acid sequences at that time. Bence Jones proteins, mostly from human, were aligned, using the now-known Kabat numbering system, and a quantitative measure, variability, was calculated for every position. Three peaks, at positions 24-34, 50-56 and 89-97, were identified and proposed to form the complementarity determining regions (CDR) of light chains. Subsequently, antibody heavy chain amino acid sequences were also aligned using a different numbering system, since the locations of their CDRs (31-35B, 50-65 and 95-102) are different from those of the light chains. CDRL1 starts right after the first invariant Cys 23 of light chains, while CDRH1 is eight amino acid residues away from the first invariant Cys 22 of heavy chains. During the past 30 years, the Kabat database has grown to include nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules and other proteins of immunological interest. It has been used extensively by immunologists to derive useful structural and functional information from the primary sequences of these proteins.

Proper citation: Kabat Database of Sequences of Proteins of Immunological Interest (RRID:SCR_006465) Copy   

•   Source: SciCrunch Registry

http://humanmetabolism.org/

A comprehensive biochemical knowledge-base on human metabolism, this community-driven, consensus metabolic reconstruction integrates metabolic information from five different resources: * Recon 1, a global human metabolic reconstruction (Duarte et al, PNAS, 104(6), 1777-1782, 2007) * EHMN, Edinburgh Human Metabolic Network (Hao et al., BMC Bioinformatics 11, 393, 2010) * HepatoNet1, a liver metabolic reconstruction (Gille et al., Molecular Systems Biology 6, 411, 2010), * Ac/FAO module, an acylcarnitine/fatty acid oxidation module (Sahoo et al., Molecular bioSystems 8, 2545-2558, 2012), * a human small intestinal enterocytes reconstruction (Sahoo and Thiele, submitted). Additionally, more than 370 transport and exchange reactions were added, based on a literature review. Recon 2 is fully semantically annotated (Le Nov��re, N. et al. Nat Biotechnol 23, 1509-1515, 2005) with references to persistent and publicly available chemical and gene databases, unambiguously identifying its components and increasing its applicability for third-party users. Here you can explore the content of the reconstruction by searching/browsing metabolites and reactions. Recon 2 predictive model is available in the Systems Biology Markup Language format.

Proper citation: Recon x (RRID:SCR_006345) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/NON-RED/index.html

A web tool to select biological sequences from a given set, with similarity / homology less than a user-defined level. This web-based application takes as input a set of N sequences and outputs a set of sequences of user-determined redundancy. Initially, the algorithm runs an all-against-all BLAST alignment on the input data set and creates an NxN matrix of pairwise distances defined by the similarity percentages. In the next step, the algorithm removes the sequence with the largest number of neighbors, causing that sequence not to be counted as a neighbor of any other sequence during the next iterations. It then reassesses the number of neighbors of each sequence and repeats the previous step until the sequences left over have no more neighbors. The user can specify the similarity (%) threshold and the minimum coverage length of the alignments. Sequences with a similarity below the threshold or a smaller coverage than the minimum length are not considered to be neighbors.

Proper citation: NON-RED (RRID:SCR_006225) Copy   

•   Source: SciCrunch Registry

http://www.ala.org.au/

Online repository of information about Australian plants, animals, and fungi. Development started in 2006. The Commonwealth Scientific and Industrial Research Organisation is organisation significantly involved in development of ALA.

Proper citation: Atlas of Living Australia (RRID:SCR_006467) Copy   

•   Source: SciCrunch Registry

http://arxiv.org/

Electronic archive and distribution server for research articles providing open access to more than 850,000 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics. Users can retrieve papers via the web interface. Registered authors may use the web interface to submit their articles to arXiv. Authors can also update their submissions if they choose, though previous versions remain available. Listings of newly submitted articles in areas of interest are available via the web interface, via RSS feeds, and by subscription to automatic email alerts.

Proper citation: arXiv (RRID:SCR_006500) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org

International database for laboratory mouse. Data offered by The Jackson Laboratory includes information on integrated genetic, genomic, and biological data. MGI creates and maintains integrated representation of mouse genetic, genomic, expression, and phenotype data and develops reference data set and consensus data views, synthesizes comparative genomic data between mouse and other mammals, maintains set of links and collaborations with other bioinformatics resources, develops and supports analysis and data submission tools, and provides technical support for database users. Projects contributing to this resource are: Mouse Genome Database (MGD) Project, Gene Expression Database (GXD) Project, Mouse Tumor Biology (MTB) Database Project, Gene Ontology (GO) Project at MGI, and MouseCyc Project at MGI.

Proper citation: Mouse Genome Informatics (MGI) (RRID:SCR_006460) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/SecStr/

A tool to Predict the Secondary Structure of a protein from its amino acid sequence alone. The SecStr package uses six different secondary structure prediction methods (Nagano, Garnier et al., Burges et al., Chou and Fasman , Lim and Dufton and Hider). The results of those methods are combined into a Joint Prediction Histogram (JPH) as described by Hamodrakas, 1988 and Hamodrakas et al., 1982. As previously mentioned, the SecStr package contains computer programs making use of the secondary structure prediction methods of Nagano, Garnier et al., Burges et al., Chou and Fasman, Lim and Dufton and Hider. These programs were written in Fortran. The results of individual prediction methods are combined as described by Hamodrakas (1988), using a Perl program, to produce joint prediction histograms (JPH), for three types of secondary structure, which may be presented separately on a Java Applet. The output may be given either in text or graphics mode. For the latter a Java capable browser is required.

Proper citation: SecStr (RRID:SCR_006220) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/CW-PRED/

A web tool for the prediction of Cell Wall-Anchored Proteins in Gram+ Bacteria. Gram-positive bacteria have surface proteins that are often implicated in virulence. A group of extracellular proteins attached to the cell wall contains an LPXTG-like motif that is target for cleavage and covalent coupling to peptidoglycan by sortase enzymes. A new Hidden Markov Model (HMM), an extension to the HMM model from Litou et al., http://www.ncbi.nlm.nih.gov/pubmed/18464329, was developed for predicting the LPXTG and LPXTG-like cell-wall proteins of Gram-positive bacteria. An analysis of 177 completely sequenced genomes has been performed as well. We identified in total 1456 cell-wall proteins, from which 1283 have the LPXTG motif, 39 the NPXTG motif, 53 have the LPXTA and 81 the LAXTG motif.

Proper citation: CW-PRED (RRID:SCR_006188) Copy   

•   Source: SciCrunch Registry

http://omicslab.genetics.ac.cn/GOEAST/

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods. Compared with available GO analysis tools, GOEAST has the following unique features: * GOEAST supports analysis for data from various resources, such as expression data obtained using Affymetrix, illumina, Agilent or customized microarray platforms. GOEAST also supports non-microarray based experimental data. The web-based feature makes GOEAST very user friendly; users only have to provide a list of genes in correct formats. * GOEAST provides visualizable analysis results, by generating graphs exhibiting enriched GO terms as well as their relationships in the whole GO hierarchy. * Note that GOEAST generates separate graph for each of the three GO categories, namely biological process, molecular function and cellular component. * GOEAST allows comparison of results from multiple experiments (see Multi-GOEAST tool). The displayed color of each GO term node in graphs generated by Multi-GOEAST is the combination of different colors used in individual GOEAST analysis. Platform: Online tool

Proper citation: GOEAST - Gene Ontology Enrichment Analysis Software Toolkit (RRID:SCR_006580) Copy   

•   Source: SciCrunch Registry

http://www.disgenet.org

Database and discovery platform containing publicly available collections of genes and variants associated to human diseases. Integrates data from curated repositories, GWAS catalogues, animal models and scientific literature.

Proper citation: DisGeNET (RRID:SCR_006178) Copy   

•   Source: SciCrunch Registry

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

Database that provides the genome sequence assembly of the International Rice Genome Sequencing Project (IRGSP), manually curated annotation of the sequence, and other genomics information that could be useful for comprehensive understanding of the rice biology. RAP-DB contains clone positions, structures and functions of genes validated by cDNAs, RNA genes detected by massively parallel signature sequencing (MPSS) technology and sequence similarity, flanking sequences of mutant lines, transposable elements, etc. Other annotation data such as Gnomon can be displayed along with those of RAP for comparison.

Proper citation: RAP-DB (RRID:SCR_006610) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/drugport/

DrugPort provides an analysis of the structural information available in the Protein Data Bank (PDB) relating to drug molecules and their protein targets. The drug-target data comes from the DrugBank database. You can search the entries by identifier, test or by protein sequence, or you can use the browse options in the menu on the left.

Proper citation: DrugPort (RRID:SCR_006573) Copy   

•   Source: SciCrunch Registry

https://www.mdanderson.org/research/departments-labs-institutes/programs-centers/michale-e-keeling-center-for-comparative-medicine-and-research/national-research-resources-program.html

SMBRR maintains the only self-sustaining national research resource of laboratory-born squirrel monkeys, their tissues and other biological materials, as well as the expertise to carry out research on this animal. Scientists with NIH grants utilize squirrel monkeys to study many diseases that threaten human health including Alzheimer's disease and other disorders of the central nervous system, drug addiction, malaria, and viral diseases. Center that carries out research on squirrel monkey biology and its research uses. It meets the needs of the biomedical research community in three ways: providing national resource for laboratory-born squirrel monkeys, having active research component that continues to add new information about the biology of the squirrel monkey with a particular emphasis on reproduction and colony management, and acts as a source of expertise for squirrel monkey biology, management and husbandry.

Proper citation: Squirrel Monkey Breeding and Research Resource (RRID:SCR_006291) Copy   

•   Source: SciCrunch Registry

http://bioapps.sabanciuniv.edu/enzyminer/

EnzyMiner automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability or the activity of a given enzyme. For querying EnzyMiner, please choose an enzyme from the list and specify if you are interested in disease related abstracts or non-disease related abstracts. For disease related abstracts, the mutation list and direct links to the abstracts will be displayed. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme''s stability or functionality. If your target enzyme is not in the list, please write the enzyme name to the query box. We will run the EnzyMiner for the desired enzyme and add the results to our database. EnzyMiner has been developed by Computational Biology Lab of Sabanci University.

Proper citation: Enzyminer. (RRID:SCR_006241) Copy   

•   Source: SciCrunch Registry

http://panoga.sabanciuniv.edu/

A web server to devise functionally important pathways through the identification of single nucleotide polymorphism (SNP)-targeted genes within these pathways. The strength of the methodology stems from its multidimensional perspective, where evidence from the following five resources is combined: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.

Proper citation: PANOGA (RRID:SCR_006242) Copy   

•   Source: SciCrunch Registry

http://www.botanical-dermatology-database.info/

BoDD is an electronic re-incarnation of BOTANICAL DERMATOLOGY by John Mitchell & Arthur Rook. This updated on-line version is made available to users with the kind permission of the original authors. The original edition has been digitized by Google Books. Although BoDD is actively being updated, updates are uploaded to the website only at about monthly intervals. A vast body of information collected by the Editor (Richard J. Schmidt PhD) awaits addition to the database. Users should be aware that some of the information that is currently accessible is neither accurate nor up-to-date. None of the information presented in BoDD should be regarded as a recommendation to treat any disease or disorder. The following are databases that are present in BoDD: -Balsaminaceae -Elaeagnaceae -Gelsemiaceae -Gentianaceae / Potaliacaceae -Hydroleaceae -Loganiaceae / Spigeliaceae / Strychnaceae -Martyniaceae -Orobanchaceae -Phrymaceae -Sabiaceae -Tamaricaceae

Proper citation: BoDD (RRID:SCR_006592) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/mcmbb/

A web tool used in the discrimination of beta-barrel outer membrane proteins with a Markov chain model. MCMBB is a fast algorithm, which discriminates beta-barrel outer membrane proteins from globular proteins and from alpha-helical membrane proteins. The algorithm is based on a 1st order Markov Chain model, which captures the alternating pattern of hydrophilic-hydrophobic residues occurring in the membrane-spanning beta-strands of beta-barrel outer membrane proteins. The model achieves high accuracy in discriminating outer membrane proteins, since it can discriminate beta-barrel outer membrane with a correct classification rate of 90.08% and the globular proteins with a correct classification rate of 92.67%. When submitting alpha-helical membrane proteins, the method shows an accuracy of 100%. A score greater than zero, indicates that the protein is more likely to be a beta-barrel outer membrane protein, whereas a result lower than zero, indicates that the protein is probable not a beta-barrel. You may enter up to 1000 sequences in Fasta format.

Proper citation: MCMBB (RRID:SCR_006198) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/bioinformatics/waveTM/

A web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. A sliding window of 20 residues is used in order to calculate an average residue hydrophobicity profile, using a hydrophobicity scale. Discrete Wavelet Transform is applied on the average residue hydrophobicity signal and the different frequency coefficients produced are adaptively thresholded so that a denoised signal is reconstructed. A dynamic programming algorithm processes the denoised signal to provide the optimal model for the number, the length and the location of membrane-spanning segments. The end points of the predicted segments are extended to include flanking hydrophobic residues. Topology prediction can also be obtained in conjunction with OrienTM (Liakopoulos et al, 2001). Analysis of a non-redundant test set, provides a ~95% per segment accuracy and ~90% per residue accuracy. Now, you can: * Run waveTM on a sequence * Browse the results obtained with the algorithm * View additional material concerning the hydrophobicity scale

Proper citation: waveTM (RRID:SCR_006199) Copy   

•   Source: SciCrunch Registry