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10.1371/journal.pgen.1005657 http://scihub22266oqcxt.onion/10.1371/journal.pgen.1005657 C4667891!4667891!26630308     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Genet 2015 ; 11 (12): ä Nephropedia Template TP {{tp|p=26630308|t=2015. A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures |pdf=|usr=}}{{26630308}} gab.com Text A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures JO: PLoS Genet http://www.kidney.de/pget.php?&tw=1&pmid=26630308 #FOAvip Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or ?mutation signatures? at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5? to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with ?mixed-membership models? which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/. Twit Text FOAVip A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures ????PLoS Genet http://www.kidney.de/pget.php?&tw=1&pmid=26630308 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26630308 #FOAvip English Wikipedia <ref>{{Cite pmid|26630308}}</ref> A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures #MMPMID26630308 Shiraishi Y; Tremmel G; Miyano S; Stephens M PLoS Genet 2015[Dec]; 11 (12): ä PMID26630308show ga Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or ?mutation signatures? at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5? to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with ?mixed-membership models? which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/. äA Simple Model-Based Approach to Inferring and Visualizing Cancer Muta(epmc).pdf DeepDyve Pubget Overpricing