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10.1186/s13059-017-1311-2 http://scihub22266oqcxt.onion/10.1186/s13059-017-1311-2 C5606061!5606061!28927434     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genome+Biol 2017 ; 18 (ä): ä Nephropedia Template TP {{tp|p=28927434|t=2017. SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models |pdf=|usr=}}{{28927434}} gab.com Text SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=28927434 #FOAvip Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-017-1311-2) contains supplementary material, which is available to authorized users. Twit Text FOAVip SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=28927434 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28927434 #FOAvip English Wikipedia <ref>{{Cite pmid|28927434}}</ref> SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models #MMPMID28927434 Zafar H; Tzen A; Navin N; Chen K; Nakhleh L Genome Biol 2017[]; 18 (ä): ä PMID28927434show ga Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-017-1311-2) contains supplementary material, which is available to authorized users. äSiFit: inferring tumor trees from single-cell sequencing data under fi(epmc).pdf DeepDyve Pubget Overpricing