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10.1093/bioinformatics/btt152 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btt152 C3661050!3661050 !23559638     free     free     free 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 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 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 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 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 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\23559638 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2013 ; 29 (11 ): 1390-8 Nephropedia Template TP {{tp|p=23559638 |t=2013. Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers |pdf=|usr=}}{{23559638 }} gab.com Text Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=23559638 #FOAvip MOTIVATION: Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved. RESULTS: Numerous studies have used universal protein-binding microarray (PBM) technology to determine the in vitro binding specificities of hundreds of TFs for all possible 8 bp sequences (8mers). We have developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into background noise, TF familywise effects and effects due to the particular TF. Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding data. Moreover, our model provides simultaneous identification of TF subclasses and their shared sequence preferences, and also of 8mers bound preferentially by individual members of TF subclasses. Such results may aid in deciphering cis-regulatory codes and determinants of protein-DNA binding specificity. AVAILABILITY AND IMPLEMENTATION: Source code, compiled code and R and Python scripts are available from http://thebrain.bwh.harvard.edu/hierarchicalANOVA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=23559638 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=23559638 #FOAvip English Wikipedia <ref>{{Cite pmid|23559638 }}</ref> Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers #MMPMID23559638 Jiang B ; Liu JS ; Bulyk ML Bioinformatics 2013[Jun]; 29 (11 ): 1390-8 PMID23559638 show ga MOTIVATION: Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved. RESULTS: Numerous studies have used universal protein-binding microarray (PBM) technology to determine the in vitro binding specificities of hundreds of TFs for all possible 8 bp sequences (8mers). We have developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into background noise, TF familywise effects and effects due to the particular TF. Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding data. Moreover, our model provides simultaneous identification of TF subclasses and their shared sequence preferences, and also of 8mers bound preferentially by individual members of TF subclasses. Such results may aid in deciphering cis-regulatory codes and determinants of protein-DNA binding specificity. AVAILABILITY AND IMPLEMENTATION: Source code, compiled code and R and Python scripts are available from http://thebrain.bwh.harvard.edu/hierarchicalANOVA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |*Regulatory Elements, Transcriptional [MESH] |Analysis of Variance [MESH] |Artifacts [MESH] |Bayes Theorem [MESH] |Binding Sites [MESH] |Chromatin Immunoprecipitation [MESH] |DNA-Binding Proteins/classification/metabolism [MESH] |Oligonucleotide Array Sequence Analysis/*methods [MESH] |Software [MESH]Bayesian hierarchical model of protein-binding microarray k-mer data r(epmc).pdf DeepDyve Pubget Overpricing