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10.1093/bioinformatics/btu651 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu651 C4253833!4253833!25297070     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2014 ; 30 (24): 3532-40 Nephropedia Template TP {{tp|p=25297070|t=2014. Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data |pdf=|usr=}}{{25297070}} gab.com Text Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25297070 #FOAvip Motivation: Most tumor samples are a heterogeneous mixture of cells, including admixture by normal (non-cancerous) cells and subpopulations of cancerous cells with different complements of somatic aberrations. This intra-tumor heterogeneity complicates the analysis of somatic aberrations in DNA sequencing data from tumor samples.Results: We describe an algorithm called THetA2 that infers the composition of a tumor sample?including not only tumor purity but also the number and content of tumor subpopulations?directly from both whole-genome (WGS) and whole-exome (WXS) high-throughput DNA sequencing data. This algorithm builds on our earlier Tumor Heterogeneity Analysis (THetA) algorithm in several important directions. These include improved ability to analyze highly rearranged genomes using a variety of data types: both WGS sequencing (including low ?7× coverage) and WXS sequencing. We apply our improved THetA2 algorithm to WGS (including low-pass) and WXS sequence data from 18 samples from The Cancer Genome Atlas (TCGA). We find that the improved algorithm is substantially faster and identifies numerous tumor samples containing subclonal populations in the TCGA data, including in one highly rearranged sample for which other tumor purity estimation algorithms were unable to estimate tumor purity.Availability and implementation: An implementation of THetA2 is available at http://compbio.cs.brown.edu/softwareContact:layla@cs.brown.edu or braphael@brown.eduSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25297070 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25297070 #FOAvip English Wikipedia <ref>{{Cite pmid|25297070}}</ref> Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data #MMPMID25297070 Oesper L; Satas G; Raphael BJ Bioinformatics 2014[Dec]; 30 (24): 3532-40 PMID25297070show ga Motivation: Most tumor samples are a heterogeneous mixture of cells, including admixture by normal (non-cancerous) cells and subpopulations of cancerous cells with different complements of somatic aberrations. This intra-tumor heterogeneity complicates the analysis of somatic aberrations in DNA sequencing data from tumor samples.Results: We describe an algorithm called THetA2 that infers the composition of a tumor sample?including not only tumor purity but also the number and content of tumor subpopulations?directly from both whole-genome (WGS) and whole-exome (WXS) high-throughput DNA sequencing data. This algorithm builds on our earlier Tumor Heterogeneity Analysis (THetA) algorithm in several important directions. These include improved ability to analyze highly rearranged genomes using a variety of data types: both WGS sequencing (including low ?7× coverage) and WXS sequencing. We apply our improved THetA2 algorithm to WGS (including low-pass) and WXS sequence data from 18 samples from The Cancer Genome Atlas (TCGA). We find that the improved algorithm is substantially faster and identifies numerous tumor samples containing subclonal populations in the TCGA data, including in one highly rearranged sample for which other tumor purity estimation algorithms were unable to estimate tumor purity.Availability and implementation: An implementation of THetA2 is available at http://compbio.cs.brown.edu/softwareContact:layla@cs.brown.edu or braphael@brown.eduSupplementary information:Supplementary data are available at Bioinformatics online. äQuantifying tumor heterogeneity in whole-genome and whole-exome sequen(epmc).pdf DeepDyve Pubget Overpricing