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Found 15 result(s)
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The Organelle Genome Megasequencing Program (OGMP) provides mitochondrial, chloroplast, and mitochondrial plasmid genome data. OGMP tools allow direct comparison of OGMP and NCBI validated records. Includes GOBASE, a taxonomically broad organelle genome database that organizes and integrates diverse data related to mitochondria and chloroplasts.
STRING is a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources: - Genomic Context - High-throughput Experiments - (Conserved) Coexpression - Previous Knowledge STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable.
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ArachnoServer is a manually curated database containing information on the sequence, three-dimensional structure, and biological activity of protein toxins derived from spider venom. Spiders are the largest group of venomous animals and they are predicted to contain by far the largest number of pharmacologically active peptide toxins (Escoubas et al., 2006). ArachnoServer has been custom-built so that a wide range of biological scientists, including neuroscientists, pharmacologists, and toxinologists, can readily access key data relevant to their discipline without being overwhelmed by extraneous information.
OrtholugeDB contains Ortholuge-based orthology predictions for completely sequenced bacterial and archaeal genomes. It is also a resource for reciprocal best BLAST-based ortholog predictions, in-paralog predictions (recently duplicated genes) and ortholog groups in Bacteria and Archaea. The Ortholuge method improves the specificity of high-throughput orthology prediction.
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MTD is focused on mammalian transcriptomes with a current version that contains data from humans, mice, rats and pigs. Regarding the core features, the MTD browses genes based on their neighboring genomic coordinates or joint KEGG pathway and provides expression information on exons, transcripts, and genes by integrating them into a genome browser. We developed a novel nomenclature for each transcript that considers its genomic position and transcriptional features.
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MaxQB stores and displays collections of large proteomics projects and allows joint analysis and comparison. As a first dataset is contains proteome data of 11 different human cell lines. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted.
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The Small Molecule Pathway Database (SMPDB) contains small molecule pathways found in humans, which are presented visually. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Accompanying data includes detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram.
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The Toxin and Toxin Target Database is a unique bioinformatics resource that combines detailed toxin data with comprehensive toxin target information. The focus of the T3DB is on providing mechanisms of toxicity and target proteins for each toxin. This dual nature of the T3DB, in which toxin and toxin target records are interactively linked in both directions, makes it unique from existing databases.
IMGT/mAb-DB provides a unique expertised resource on monoclonal antibodies (mAbs) with diagnostic or therapeutic indications, fusion proteins for immune applications (FPIA), composite proteins for clinical applications (CPCA) and relative proteins of the immune system (RPI) with clinical indications.
ChEMBL is a database of bioactive drug-like small molecules, it contains 2-D structures, calculated properties (e.g. logP, Molecular Weight, Lipinski Parameters, etc.) and abstracted bioactivities (e.g. binding constants, pharmacology and ADMET data). The data is abstracted and curated from the primary scientific literature, and cover a significant fraction of the SAR and discovery of modern drugs We attempt to normalise the bioactivities into a uniform set of end-points and units where possible, and also to tag the links between a molecular target and a published assay with a set of varying confidence levels. Additional data on clinical progress of compounds is being integrated into ChEMBL at the current time.