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10.1186/1752-0509-7-S6-S16 http://scihub22266oqcxt.onion/10.1186/1752-0509-7-S6-S16 C4029456!4029456 !24565409     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24565409 &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       BMC+Syst+Biol 2013 ; 7 Suppl 6 (Suppl 6 ): S16 Nephropedia Template TP {{tp|p=24565409 |t=2013. Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data |pdf=|usr=}}{{24565409 }} gab.com Text Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data JO: BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=24565409 #FOAvip BACKGROUND: Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately. RESULTS: In this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs. CONCLUSIONS: Based on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set of enhancers can generalize with significant accuracy beyond the training set. Twit Text FOAVip Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data ????BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=24565409 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24565409 #FOAvip English Wikipedia <ref>{{Cite pmid|24565409 }}</ref> Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data #MMPMID24565409 Podsiad?o A ; Wrzesie? M ; Paja W ; Rudnicki W ; Wilczy?ski B BMC Syst Biol 2013[]; 7 Suppl 6 (Suppl 6 ): S16 PMID24565409 show ga BACKGROUND: Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately. RESULTS: In this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs. CONCLUSIONS: Based on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set of enhancers can generalize with significant accuracy beyond the training set. |*Nucleotide Motifs [MESH] |*Sequence Analysis [MESH] |Animals [MESH] |Chromatin Immunoprecipitation [MESH] |Chromatin/*genetics [MESH] |Computational Biology/*methods [MESH] |Drosophila melanogaster/genetics [MESH] |Enhancer Elements, Genetic/*genetics [MESH] |Epigenesis, Genetic [MESH] |Genetic Markers/genetics [MESH] |Histones/genetics [MESH]Active enhancer positions can be accurately predicted from chromatin m(epmc).pdf DeepDyve Pubget Overpricing