Database of Bacterial ExoToxins for Human (DBETH) is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. DBETH also provides a platform to its users to identify potential exotoxin like sequences through Homology based as well as Non-homology based methods. In homology based approach the users can identify potential exotoxin like sequences either running BLASTp against the toxin sequences or by running HMMER against toxin domains identified by DBETH from human pathogenic bacterial exotoxins. In Non-homology based part DBETH uses a machine learning approach to identify potential exotoxins (Toxin Prediction by Support Vector Machine based approach). ... [Information of the supplier]
This database is primary aimed at providing an exhaustive and updated registry of sequence variants identified in auto-inflammatory disorder related genes. Since we believe that an attempt to retrieve phenotypical information from all patients identified throughout the world would be an impossible task, we chose to allow only one inclusion per variant (duplicates are automatically rejected), although we allocated a short space for clinical information on the initial patient. The relatively high number of variants with unknown associated phenotype likely stems from the fact that most data are submitted by laboratories performing genetic diagnosis, which do not always have relevant clinical information about the patients. Conversely, a number of apparently simple polymorphisms, i.e. intronic variants not located in splice acceptor or donor sites and silent mutations, were found in symptomatic individuals during the diagnostic test. Since functional experiments are generally lacking, we cannot rule out that these variants do not alter regulatory splice elements, thus acting as true mutations. For all these reasons, we recommend that this database should not be used as a reference for phenotype-genotype correlation. ... [Information of the supplier]
Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), we have developed the Stem Cell Discovery Engine (SCDE)—an online database of curated CSC experiments coupled to the Galaxy analytical framework. The SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. Our initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. (Taken from: Shannan J. Ho Sui, Kimberly Begley, Dorothy Reilly, Brad Chapman, Ray McGovern, Philippe Rocca-Sera, Eamonn Maguire, Gabriel M. Altschuler, Terah A. A. Hansen, Ramakrishna Sompallae, Andrei Krivtsov, Ramesh A. Shivdasani, Scott A. Armstrong, Aedín C. Culhane, Mick Correll, Susanna-Assunta Sansone, Oliver Hofmann, and Winston Hide: The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons. In: Nucl. Acids Res. (2012) 40(D1): D984-D991) ... [Miscellaneous as indicated]
