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Identification and characterization of lncRNAs in cancer with a view to their application in improving diagnosis and therapy remains a major challenge that requires new and innovative approaches. We have developed an integrative framework termed "CLING", aimed to prioritize candidate cancer-related lncRNAs based on their associations with known cancer lncRNAs. CLING focuses on joint optimization and prioritization of all candidates for each cancer type by integrating lncRNA topological properties and multiple lncRNA-centric networks. Validation analyses revealed that CLING is more effective than prioritization based on a single lncRNA network. Reliable AUC (Area Under Curve) scores were obtained across 10 cancer types, ranging from 0.85 to 0.94. Several novel lncRNAs predicted in the top 10 candidates for various cancer types have been confirmed by recent biological experiments. Furthermore, using a case study on liver hepatocellular carcinoma as an example, CLING facilitated the successful identification of novel cancer lncRNAs overlooked by differential expression analyses (DEA). This time- and cost-effective computational model may provide a valuable complement to experimental studies and assist in future investigations on lncRNA involvement in the pathogenesis of cancers. We have developed a web-based server for users to rapidly implement CLING and visualize data, which is freely accessible at http://bio-bigdata.hrbmu.edu.cn/cling/. CLING has been successfully applied to predict a few potential lncRNAs from thousands of candidates for many cancer types.
Pubmed ID: 32211391
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A database covering eight category functional interactions between noncoding RNAs (except tRNAs and rRNAs) and proteins related biomacromolecules (proteins, mRNAs and genomic DNAs) in six model organisms. Functional interactions imply both physical interactions between the ncRNA and protein, and other forms of interaction where the combination of an ncRNA and an mRNA or a genomic DNA sequence elicits a cellular reaction. This database is distinguished from other biomolecular interaction database by: 1. The data of NPInter is novel, in the sense that no earlier database has especially cataloged this type of data (ncRNA-protein interactions). The database now contains more than 700 published functional interactions from the six organisms E. coli, yeast, worm, fly, mouse and human in which functional interactions experiments have been concentrated. The amount of data is not large, but the NPInter covers almost all experimentally verified ncRNA functional interaction data which had been published before the end of last year. 2. The ncRNA functional interaction data are entered into NPInter only following publication in books or peer-reviewed journals. Entry is done manually by a curator, and thereafter double-checked by a second curator. 3. We introduce a classification of the functional interaction data, which is based on the functional interaction process the ncRNA takes part in. 4. NPInter also provides an efficient search option, allowing recovery of interactions, related publications and other information.
View all literature mentionsDatabase that hosts elaborated information for both predicted and experimentally verified, miRNA-lncRNA interactions. The database consists of two distinct modules. The Experimental Module contains detailed information for more than 5,000 interactions, between 2,958 lncRNAs and 120 miRNAs, ranging from miRNA and lncRNA related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. The Prediction Module, which is based on the latest version of DIANA-microT target prediction algorithm (DIANA-microT-CDS), contains detailed information for more than 10 million interactions, between 56,097 lncRNAs and 3,078 miRNAs, ranging from miRNA and lncRNA related details to specific information regarding their interaction sites, graphical representation of their binding and the predicted score. This module exhibits a unique feature for searching the database. Users are able to add genomic locations to their queries thus browsing every miRNA-lncRNA interaction that has at least one MRE located inside the queried locus.
View all literature mentionsWeb tool to predict biological targets of miRNAs by searching for presence of conserved 8mer, 7mer and 6mer sites that match seed region of each miRNA. Nonconserved sites are also predicted and sites with mismatches in seed region that are compensated by conserved 3' pairing. Used to search for predicted microRNA targets in mammals.
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