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10.1186/s13059-014-0501-4 http://scihub22266oqcxt.onion/10.1186/s13059-014-0501-4 C4397945!4397945!25367074     free     free     free       Genome+Biol 2014 ; 15 (10): ä Nephropedia Template TP {{tp|p=25367074|t=2014. Bayesian transcriptome assembly |pdf=|usr=}}{{25367074}} gab.com Text Bayesian transcriptome assembly JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=25367074 #FOAvip RNA sequencing allows for simultaneous transcript discovery and quantification, but reconstructing complete transcripts from such data remains difficult. Here, we introduce Bayesembler, a novel probabilistic method for transcriptome assembly built on a Bayesian model of the RNA sequencing process. Under this model, samples from the posterior distribution over transcripts and their abundance values are obtained using Gibbs sampling. By using the frequency at which transcripts are observed during sampling to select the final assembly, we demonstrate marked improvements in sensitivity and precision over state-of-the-art assemblers on both simulated and real data. Bayesembler is available at https://github.com/bioinformatics-centre/bayesembler.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-014-0501-4) contains supplementary material, which is available to authorized users. Twit Text FOAVip Bayesian transcriptome assembly ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=25367074 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25367074 #FOAvip English Wikipedia <ref>{{Cite pmid|25367074}}</ref> Bayesian transcriptome assembly #MMPMID25367074 Maretty L; Sibbesen JA; Krogh A Genome Biol 2014[]; 15 (10): ä PMID25367074show ga RNA sequencing allows for simultaneous transcript discovery and quantification, but reconstructing complete transcripts from such data remains difficult. Here, we introduce Bayesembler, a novel probabilistic method for transcriptome assembly built on a Bayesian model of the RNA sequencing process. Under this model, samples from the posterior distribution over transcripts and their abundance values are obtained using Gibbs sampling. By using the frequency at which transcripts are observed during sampling to select the final assembly, we demonstrate marked improvements in sensitivity and precision over state-of-the-art assemblers on both simulated and real data. Bayesembler is available at https://github.com/bioinformatics-centre/bayesembler.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-014-0501-4) contains supplementary material, which is available to authorized users. äBayesian transcriptome assembly (epmc).pdf DeepDyve Pubget Overpricing