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10.6026/97320630010175 http://scihub22266oqcxt.onion/10.6026/97320630010175 C4070045!4070045!24966516     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformation 2014 ; 10 (4): 175-9 Nephropedia Template TP {{tp|p=24966516|t=2014. A novel sequence and context based method for promoter recognition |pdf=|usr=}}{{24966516}} gab.com Text A novel sequence and context based method for promoter recognition JO: Bioinformation http://www.kidney.de/pget.php?&tw=1&pmid=24966516 #FOAvip Identification of promoters in DNA sequence using computational techniques is a significant research area because of its direct association in transcription regulation. A wide range of algorithms are available for promoter prediction. Most of them are polymerase dependent and cannot handle eukaryotes and prokaryotes alike. This study proposes a polymerase independent algorithm, which can predict whether a given DNA fragment is a promoter or not, based on the sequence features and statistical elements. This algorithm considers all possible pentamers formed from the nucleotides A, C, G, and T along with CpG islands, TATA box, initiator elements, and downstream promoter elements. The highlight of the algorithm is that it is not polymerase specific and can predict for both eukaryotes and prokaryotes in the same computational manner even though the underlying biological mechanisms of promoter recognition differ greatly. The proposed Method, Promoter Prediction System - PPS-CBM achieved a sensitivity, specificity, and accuracy percentages of 75.08, 83.58 and 79.33 on E. coli data set and 86.67, 88.41 and 87.58 on human data set. We have developed a tool based on PPS-CBM, the proposed algorithm, with which multiple sequences of varying lengths can be tested simultaneously and the result is reported in a comprehensive tabular format. The tool also reports the strength of the prediction.Availability: The tool and source code of PPS-CBM is available at http://keralabs.org Twit Text FOAVip A novel sequence and context based method for promoter recognition ????Bioinformation http://www.kidney.de/pget.php?&tw=1&pmid=24966516 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24966516 #FOAvip English Wikipedia <ref>{{Cite pmid|24966516}}</ref> A novel sequence and context based method for promoter recognition #MMPMID24966516 P U; Dubey JK; RV K; Cherian BS; Gopalakrishnan G; Nair AS Bioinformation 2014[]; 10 (4): 175-9 PMID24966516show ga Identification of promoters in DNA sequence using computational techniques is a significant research area because of its direct association in transcription regulation. A wide range of algorithms are available for promoter prediction. Most of them are polymerase dependent and cannot handle eukaryotes and prokaryotes alike. This study proposes a polymerase independent algorithm, which can predict whether a given DNA fragment is a promoter or not, based on the sequence features and statistical elements. This algorithm considers all possible pentamers formed from the nucleotides A, C, G, and T along with CpG islands, TATA box, initiator elements, and downstream promoter elements. The highlight of the algorithm is that it is not polymerase specific and can predict for both eukaryotes and prokaryotes in the same computational manner even though the underlying biological mechanisms of promoter recognition differ greatly. The proposed Method, Promoter Prediction System - PPS-CBM achieved a sensitivity, specificity, and accuracy percentages of 75.08, 83.58 and 79.33 on E. coli data set and 86.67, 88.41 and 87.58 on human data set. We have developed a tool based on PPS-CBM, the proposed algorithm, with which multiple sequences of varying lengths can be tested simultaneously and the result is reported in a comprehensive tabular format. The tool also reports the strength of the prediction.Availability: The tool and source code of PPS-CBM is available at http://keralabs.org äA novel sequence and context based method for promoter recognition (epmc).pdf DeepDyve Pubget Overpricing