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10.1198/jasa.2011.ap09706 http://scihub22266oqcxt.onion/10.1198/jasa.2011.ap09706 C4608541!4608541!26478641     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       J+Am+Stat+Assoc 2011 ; 106 (495): 891-903 Nephropedia Template TP {{tp|p=26478641|t=2011. A Statistical Framework for the Analysis of ChIP-Seq Data |pdf=|usr=}}{{26478641}} gab.com Text A Statistical Framework for the Analysis of ChIP-Seq Data JO: J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=26478641 #FOAvip Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources of biases in the ChIP-Seq technology. These biases typically arise from both the standard pre-processing protocol and the underlying DNA sequence of the generated data.We study data from a naked DNA sequencing experiment, which sequences non-cross-linked DNA after deproteinizing and shearing, to understand factors affecting background distribution of data generated in a ChIP-Seq experiment. We introduce a background model that accounts for apparent sources of biases such as mappability and GC content and develop a flexible mixture model named MOSAiCS for detecting peaks in both one- and two-sample analyses of ChIP-Seq data. We illustrate that our model fits observed ChIP-Seq data well and further demonstrate advantages of MOSAiCS over commonly used tools for ChIP-Seq data analysis with several case studies. Twit Text FOAVip A Statistical Framework for the Analysis of ChIP-Seq Data ????J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=26478641 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26478641 #FOAvip English Wikipedia <ref>{{Cite pmid|26478641}}</ref> A Statistical Framework for the Analysis of ChIP-Seq Data #MMPMID26478641 Kuan PF; Chung D; Pan G; Thomson JA; Stewart R; Kele? S J Am Stat Assoc 2011[]; 106 (495): 891-903 PMID26478641show ga Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources of biases in the ChIP-Seq technology. These biases typically arise from both the standard pre-processing protocol and the underlying DNA sequence of the generated data.We study data from a naked DNA sequencing experiment, which sequences non-cross-linked DNA after deproteinizing and shearing, to understand factors affecting background distribution of data generated in a ChIP-Seq experiment. We introduce a background model that accounts for apparent sources of biases such as mappability and GC content and develop a flexible mixture model named MOSAiCS for detecting peaks in both one- and two-sample analyses of ChIP-Seq data. We illustrate that our model fits observed ChIP-Seq data well and further demonstrate advantages of MOSAiCS over commonly used tools for ChIP-Seq data analysis with several case studies. äA Statistical Framework for the Analysis of ChIP-Seq Data (epmc).pdf DeepDyve Pubget Overpricing