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10.1186/s13059-016-0940-1 http://scihub22266oqcxt.onion/10.1186/s13059-016-0940-1 C4842274!4842274 !27107712     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27107712 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Genome+Biol 2016 ; 17 (ä): 74 Nephropedia Template TP {{tp|p=27107712 |t=2016. A benchmark for RNA-seq quantification pipelines |pdf=|usr=}}{{27107712 }} gab.com Text A benchmark for RNA-seq quantification pipelines JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=27107712 #FOAvip Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( http://bioconductor.org/packages/rnaseqcomp ). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. Twit Text FOAVip A benchmark for RNA-seq quantification pipelines ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=27107712 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27107712 #FOAvip English Wikipedia <ref>{{Cite pmid|27107712 }}</ref> A benchmark for RNA-seq quantification pipelines #MMPMID27107712 Teng M ; Love MI ; Davis CA ; Djebali S ; Dobin A ; Graveley BR ; Li S ; Mason CE ; Olson S ; Pervouchine D ; Sloan CA ; Wei X ; Zhan L ; Irizarry RA Genome Biol 2016[Apr]; 17 (ä): 74 PMID27107712 show ga Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( http://bioconductor.org/packages/rnaseqcomp ). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. |*Algorithms [MESH] |Animals [MESH] |Humans [MESH] |Reference Values [MESH] |Sensitivity and Specificity [MESH]A benchmark for RNA-seq quantification pipelines (epmc).pdf DeepDyve Pubget Overpricing