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10.1093/bioinformatics/btu274 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu274 C4058923!4058923!24931974     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2014 ; 30 (12): i113-20 Nephropedia Template TP {{tp|p=24931974|t=2014. Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation |pdf=|usr=}}{{24931974}} gab.com Text Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931974 #FOAvip Motivation: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species? landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed.Results: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-cell activation (Th0). To quantify Th17-specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected.Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/Contact:tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fiSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931974 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24931974 #FOAvip English Wikipedia <ref>{{Cite pmid|24931974}}</ref> Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation #MMPMID24931974 Äijö T; Butty V; Chen Z; Salo V; Tripathi S; Burge CB; Lahesmaa R; Lähdesmäki H Bioinformatics 2014[Jun]; 30 (12): i113-20 PMID24931974show ga Motivation: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species? landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed.Results: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-cell activation (Th0). To quantify Th17-specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected.Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/Contact:tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fiSupplementary information:Supplementary data are available at Bioinformatics online. äMethods for time series analysis of RNA-seq data with application to h(epmc).pdf DeepDyve Pubget Overpricing