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10.1038/ng.3810 http://scihub22266oqcxt.onion/10.1038/ng.3810 C5395419!5395419!28288115     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 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 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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Genet 2017 ; 49 (4): 618-24 Nephropedia Template TP {{tp|p=28288115|t=2017. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data |pdf=|usr=}}{{28288115}} gab.com Text Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data JO: Nat Genet http://www.kidney.de/pget.php?&tw=1&pmid=28288115 #FOAvip Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here, we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which therefore are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the ?Big Data? available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters. Twit Text FOAVip Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data ????Nat Genet http://www.kidney.de/pget.php?&tw=1&pmid=28288115 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28288115 #FOAvip English Wikipedia <ref>{{Cite pmid|28288115}}</ref> Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data #MMPMID28288115 Huang YF; Gulko B; Siepel A Nat Genet 2017[Apr]; 49 (4): 618-24 PMID28288115show ga Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here, we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which therefore are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the ?Big Data? available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters. äFast, scalable prediction of deleterious noncoding variants from funct(epmc).pdf DeepDyve Pubget Overpricing