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10.1155/2015/196082 http://scihub22266oqcxt.onion/10.1155/2015/196082 C4619761!4619761 !26539468     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26539468 &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 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 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\26539468 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biomed+Res+Int 2015 ; 2015 (ä): 196082 Nephropedia Template TP {{tp|p=26539468 |t=2015. Assessing Computational Steps for CLIP-Seq Data Analysis |pdf=|usr=}}{{26539468 }} gab.com Text Assessing Computational Steps for CLIP-Seq Data Analysis JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26539468 #FOAvip RNA-binding protein (RBP) is a key player in regulating gene expression at the posttranscriptional level. CLIP-Seq, with the ability to provide a genome-wide map of protein-RNA interactions, has been increasingly used to decipher RBP-mediated posttranscriptional regulation. Generating highly reliable binding sites from CLIP-Seq requires not only stringent library preparation but also considerable computational efforts. Here we presented a first systematic evaluation of major computational steps for identifying RBP binding sites from CLIP-Seq data, including preprocessing, the choice of control samples, peak normalization, and motif discovery. We found that avoiding PCR amplification artifacts, normalizing to input RNA or mRNAseq, and defining the background model from control samples can reduce the bias introduced by RNA abundance and improve the quality of detected binding sites. Our findings can serve as a general guideline for CLIP experiments design and the comprehensive analysis of CLIP-Seq data. Twit Text FOAVip Assessing Computational Steps for CLIP-Seq Data Analysis ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26539468 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26539468 #FOAvip English Wikipedia <ref>{{Cite pmid|26539468 }}</ref> Assessing Computational Steps for CLIP-Seq Data Analysis #MMPMID26539468 Liu Q ; Zhong X ; Madison BB ; Rustgi AK ; Shyr Y Biomed Res Int 2015[]; 2015 (ä): 196082 PMID26539468 show ga RNA-binding protein (RBP) is a key player in regulating gene expression at the posttranscriptional level. CLIP-Seq, with the ability to provide a genome-wide map of protein-RNA interactions, has been increasingly used to decipher RBP-mediated posttranscriptional regulation. Generating highly reliable binding sites from CLIP-Seq requires not only stringent library preparation but also considerable computational efforts. Here we presented a first systematic evaluation of major computational steps for identifying RBP binding sites from CLIP-Seq data, including preprocessing, the choice of control samples, peak normalization, and motif discovery. We found that avoiding PCR amplification artifacts, normalizing to input RNA or mRNAseq, and defining the background model from control samples can reduce the bias introduced by RNA abundance and improve the quality of detected binding sites. Our findings can serve as a general guideline for CLIP experiments design and the comprehensive analysis of CLIP-Seq data. |Binding Sites/genetics [MESH] |Caco-2 Cells [MESH] |Gene Expression Regulation [MESH] |High-Throughput Nucleotide Sequencing/*methods [MESH] |Humans [MESH] |MicroRNAs/genetics [MESH] |RNA, Messenger/*genetics/metabolism [MESH] |RNA-Binding Proteins/*genetics/metabolism [MESH]Assessing Computational Steps for CLIP-Seq Data Analysis (epmc).pdf DeepDyve Pubget Overpricing