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10.1186/1471-2105-15-188 http://scihub22266oqcxt.onion/10.1186/1471-2105-15-188 C4067528!4067528 !24929920     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24929920 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Bioinformatics 2014 ; 15 (ä): 188 Nephropedia Template TP {{tp|p=24929920 |t=2014. An integrative method to normalize RNA-Seq data |pdf=|usr=}}{{24929920 }} gab.com Text An integrative method to normalize RNA-Seq data JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24929920 #FOAvip BACKGROUND: Transcriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, several normalization approaches have emerged, differing both in the statistical strategy employed and in the type of corrected biases. However, there is no clear standard normalization method. RESULTS: We present a novel methodology to normalize RNA-Seq data, taking into account transcript size, GC content, and sequencing depth, which are the major quantification-related biases. In this study, we found that transcripts shorter than 600 bp have an underestimated expression level, while longer transcripts are even more overestimated that they are long. Second, it was well known that the higher the GC content (>50%), the more the transcripts are underestimated. Third, we demonstrated that the sequencing depth impacts the size bias and proposed a correction allowing the comparison of expression levels among many samples. The efficiency of our approach was then tested by comparing the correlation between normalized RNA-Seq data and qRT-PCR expression measurements. All the steps are automated in a program written in Perl and available on request. CONCLUSIONS: The methodology presented in this article identifies and corrects different biases that influence RNA-Seq quantification, and provides more accurate estimations of gene expression levels. This method can be applied to compare expression quantifications from many samples, but preferentially from the same tissue. In order to compare samples from different tissue, a calibration using several reference genes will be required. Twit Text FOAVip An integrative method to normalize RNA-Seq data ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24929920 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24929920 #FOAvip English Wikipedia <ref>{{Cite pmid|24929920 }}</ref> An integrative method to normalize RNA-Seq data #MMPMID24929920 Filloux C ; Cédric M ; Romain P ; Lionel F ; Christophe K ; Dominique R ; Abderrahman M ; Daniel P BMC Bioinformatics 2014[Jun]; 15 (ä): 188 PMID24929920 show ga BACKGROUND: Transcriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, several normalization approaches have emerged, differing both in the statistical strategy employed and in the type of corrected biases. However, there is no clear standard normalization method. RESULTS: We present a novel methodology to normalize RNA-Seq data, taking into account transcript size, GC content, and sequencing depth, which are the major quantification-related biases. In this study, we found that transcripts shorter than 600 bp have an underestimated expression level, while longer transcripts are even more overestimated that they are long. Second, it was well known that the higher the GC content (>50%), the more the transcripts are underestimated. Third, we demonstrated that the sequencing depth impacts the size bias and proposed a correction allowing the comparison of expression levels among many samples. The efficiency of our approach was then tested by comparing the correlation between normalized RNA-Seq data and qRT-PCR expression measurements. All the steps are automated in a program written in Perl and available on request. CONCLUSIONS: The methodology presented in this article identifies and corrects different biases that influence RNA-Seq quantification, and provides more accurate estimations of gene expression levels. This method can be applied to compare expression quantifications from many samples, but preferentially from the same tissue. In order to compare samples from different tissue, a calibration using several reference genes will be required. |Animals [MESH] |Calibration [MESH] |Gene Expression Profiling/methods [MESH] |High-Throughput Nucleotide Sequencing/*methods [MESH] |Humans [MESH] |RNA/*genetics [MESH] |Sequence Analysis, RNA/*methods [MESH]An integrative method to normalize RNA-Seq data (epmc).pdf DeepDyve Pubget Overpricing