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10.1371/journal.pone.0174744 http://scihub22266oqcxt.onion/10.1371/journal.pone.0174744 C5381888!5381888!28379994     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2017 ; 12 (4): ä Nephropedia Template TP {{tp|p=28379994|t=2017. GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data |pdf=|usr=}}{{28379994}} gab.com Text GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28379994 #FOAvip Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html). Twit Text FOAVip GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28379994 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28379994 #FOAvip English Wikipedia <ref>{{Cite pmid|28379994}}</ref> GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data #MMPMID28379994 Mifsud B; Martincorena I; Darbo E; Sugar R; Schoenfelder S; Fraser P; Luscombe NM PLoS One 2017[]; 12 (4): ä PMID28379994show ga Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html). äGOTHiC, a probabilistic model to resolve complex biases and to identif(epmc).pdf DeepDyve Pubget Overpricing