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10.1186/s13059-017-1248-5 http://scihub22266oqcxt.onion/10.1186/s13059-017-1248-5 C5488362!5488362!28655331     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       Genome+Biol 2017 ; 18 (ä): ä Nephropedia Template TP {{tp|p=28655331|t=2017. BRIE: transcriptome-wide splicing quantification in single cells |pdf=|usr=}}{{28655331}} gab.com Text BRIE: transcriptome-wide splicing quantification in single cells JO: Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=28655331 #FOAvip Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-017-1248-5) contains supplementary material, which is available to authorized users. Twit Text FOAVip BRIE: transcriptome-wide splicing quantification in single cells ????Genome Biol http://www.kidney.de/pget.php?&tw=1&pmid=28655331 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28655331 #FOAvip English Wikipedia <ref>{{Cite pmid|28655331}}</ref> BRIE: transcriptome-wide splicing quantification in single cells #MMPMID28655331 Huang Y; Sanguinetti G Genome Biol 2017[]; 18 (ä): ä PMID28655331show ga Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing.Electronic supplementary material: The online version of this article (doi:10.1186/s13059-017-1248-5) contains supplementary material, which is available to authorized users. äBRIE: transcriptome-wide splicing quantification in single cells (epmc).pdf DeepDyve Pubget Overpricing