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NONCODE is an integrated knowledge database dedicated to non-coding RNAs (excluding tRNAs and rRNAs). Now, there are 16 species in NONCODE(human, mouse, cow, rat, chicken, fruitfly, zebrafish, celegans, yeast, Arabidopsis, chimpanzee, gorilla, orangutan, rhesus macaque, opossum and platypus).The source of NONCODE includes literature and other public databases. We searched PubMed using key words ‘ncrna’, ‘noncoding’, ‘non-coding’,‘no code’, ‘non-code’, ‘lncrna’ or ‘lincrna. We retrieved the new identified lncRNAs and their annotation from the Supplementary Material or web site of these articles. Together with the newest data from Ensembl , RefSeq, lncRNAdb and GENCODE were processed through a standard pipeline for each species.
The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. The data stored within the DIP database were curated, both, manually by expert curators and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Please, check the reference page to find articles describing the DIP database in greater detail. The Database of Ligand-Receptor Partners (DLRP) is a subset of DIP (Database of Interacting Proteins). The DLRP is a database of protein ligand and protein receptor pairs that are known to interact with each other. By interact we mean that the ligand and receptor are members of a ligand-receptor complex and, unless otherwise noted, transduce a signal. In some instances the ligand and/or receptor may form a heterocomplex with other ligands/receptors in order to be functional. We have entered the majority of interactions in DLRP as full DIP entries, with links to references and additional information
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<<<!!!<<< 2019-12-23: the repository is offline >>>!!!>>> Introduction of genome-scale metabolic network: The completion of genome sequencing and subsequent functional annotation for a great number of species enables the reconstruction of genome-scale metabolic networks. These networks, together with in silico network analysis methods such as the constraint based methods (CBM) and graph theory methods, can provide us systems level understanding of cellular metabolism. Further more, they can be applied to many predictions of real biological application such as: gene essentiality analysis, drug target discovery and metabolic engineering
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>>>!!!<<< OMICtools is no longer online >>>!!!<<< We founded OMICtools in 2012 with the vision to drive progress in life science. We wanted to empower life science practitioners all over the world to achieve breakthroughs by getting data to talk. While we made tremendous progress over the past three years, developing a bioinformatics database of software and dynamic protocols, attracting more than 1.5M visitors a year, we lacked the financial support we needed to continue. We certainly gave it our all. We'd like to thank everyone who believed in us and supported us on this journey: all our users, our community, our friends, families and employees (who we consider as our extended family!). omicX will probably shut down its operations within the next few weeks. The team and I remain firmly committed to our vision, particularly at this very difficult time. It is now, more than ever before, that researchers need access to a resource that pools collective scientific intelligence. We have accumulated an awful lot of experience which we are keen to share. If your institution would be interested in taking over our website and database, to provide researchers with continued access to the platform, or you simply want to stay in touch with the omicX team, contact us at contact@omictools.com or at carine.toutain@fhbx.eu.
The NCBI Nucleotide database collects sequences from such sources as GenBank, RefSeq, TPA, and PDB. Sequences collected relate to genome, gene, and transcript sequence data, and provide a foundation for research related to the biomedical field.