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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 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28655331 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28655331 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Genome+Biol 2017 ; 18 (1 ): 123 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. 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[Jun]; 18 (1 ): 123 PMID28655331 show 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. |Alternative Splicing/*genetics [MESH] |Bayes Theorem [MESH] |Exons/genetics [MESH] |High-Throughput Nucleotide Sequencing [MESH] |Humans [MESH] |Protein Isoforms/*genetics [MESH] |RNA Splicing/*genetics [MESH] |Sequence Analysis, RNA [MESH] |Single-Cell Analysis [MESH]BRIE: transcriptome-wide splicing quantification in single cells (epmc).pdf DeepDyve Pubget Overpricing