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10.3389/fgene.2017.00083 http://scihub22266oqcxt.onion/10.3389/fgene.2017.00083 C5469877!5469877!28659971     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Genet 2017 ; 8 (ä): ä Nephropedia Template TP {{tp|p=28659971|t=2017. Computational Methods for Characterizing Cancer Mutational Heterogeneity |pdf=|usr=}}{{28659971}} gab.com Text Computational Methods for Characterizing Cancer Mutational Heterogeneity JO: Front Genet http://www.kidney.de/pget.php?&tw=1&pmid=28659971 #FOAvip Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables. Twit Text FOAVip Computational Methods for Characterizing Cancer Mutational Heterogeneity ????Front Genet http://www.kidney.de/pget.php?&tw=1&pmid=28659971 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28659971 #FOAvip English Wikipedia <ref>{{Cite pmid|28659971}}</ref> Computational Methods for Characterizing Cancer Mutational Heterogeneity #MMPMID28659971 Vandin F Front Genet 2017[]; 8 (ä): ä PMID28659971show ga Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables. äComputational Methods for Characterizing Cancer Mutational Heterogenei(epmc).pdf DeepDyve Pubget Overpricing