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10.1155/2015/318217 http://scihub22266oqcxt.onion/10.1155/2015/318217 C4620388!4620388!26543854     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26543854|t=2015. OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes |pdf=|usr=}}{{26543854}} gab.com Text OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26543854 #FOAvip Background. In prokaryotic organisms, a substantial fraction of adjacent genes are organized into operons?codirectionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Although several available operon databases provide information with varying levels of reliability, very few resources provide experimentally supported results. Therefore, we believe that the biological community could benefit from having a new operon prediction database with operons predicted using next-generation RNA-seq datasets. Description. We present operomeDB, a database which provides an ensemble of all the predicted operons for bacterial genomes using available RNA-sequencing datasets across a wide range of experimental conditions. Although several studies have recently confirmed that prokaryotic operon structure is dynamic with significant alterations across environmental and experimental conditions, there are no comprehensive databases for studying such variations across prokaryotic transcriptomes. Currently our database contains nine bacterial organisms and 168 transcriptomes for which we predicted operons. User interface is simple and easy to use, in terms of visualization, downloading, and querying of data. In addition, because of its ability to load custom datasets, users can also compare their datasets with publicly available transcriptomic data of an organism. Conclusion. OperomeDB as a database should not only aid experimental groups working on transcriptome analysis of specific organisms but also enable studies related to computational and comparative operomics. Twit Text FOAVip OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26543854 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26543854 #FOAvip English Wikipedia <ref>{{Cite pmid|26543854}}</ref> OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes #MMPMID26543854 Chetal K; Janga SC Biomed Res Int 2015[]; 2015 (ä): ä PMID26543854show ga Background. In prokaryotic organisms, a substantial fraction of adjacent genes are organized into operons?codirectionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Although several available operon databases provide information with varying levels of reliability, very few resources provide experimentally supported results. Therefore, we believe that the biological community could benefit from having a new operon prediction database with operons predicted using next-generation RNA-seq datasets. Description. We present operomeDB, a database which provides an ensemble of all the predicted operons for bacterial genomes using available RNA-sequencing datasets across a wide range of experimental conditions. Although several studies have recently confirmed that prokaryotic operon structure is dynamic with significant alterations across environmental and experimental conditions, there are no comprehensive databases for studying such variations across prokaryotic transcriptomes. Currently our database contains nine bacterial organisms and 168 transcriptomes for which we predicted operons. User interface is simple and easy to use, in terms of visualization, downloading, and querying of data. In addition, because of its ability to load custom datasets, users can also compare their datasets with publicly available transcriptomic data of an organism. Conclusion. OperomeDB as a database should not only aid experimental groups working on transcriptome analysis of specific organisms but also enable studies related to computational and comparative operomics. äOperomeDB: A Database of Condition-Specific Transcription Units in Pro(epmc).pdf DeepDyve Pubget Overpricing