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10.1186/s13059-016-0936-x http://scihub22266oqcxt.onion/10.1186/s13059-016-0936-x C4858868!4858868!27149953     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27149953.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): ä Nephropedia Template TP {{tp|p=27149953|t=2016. Tree inference for single-cell data |pdf=|usr=}}{{27149953}} gab.com Text Tree inference for single-cell data JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=27149953 #FOAvip Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-016-0936-x) contains supplementary material, which is available to authorized users. Twit Text FOAVip Tree inference for single-cell data ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=27149953 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27149953 #FOAvip English Wikipedia <ref>{{Cite pmid|27149953}}</ref> Tree inference for single-cell data #MMPMID27149953 Jahn K; Kuipers J; Beerenwinkel N Genome Biol 2016[]; 17 (ä): ä PMID27149953show ga Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-016-0936-x) contains supplementary material, which is available to authorized users. äTree inference for single-cell data (epmc).pdf DeepDyve Pubget Overpricing