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Sequence Characteristics Distinguish Transcribed Enhancers from Promoters and Predict Their Breadth of Activity.

Identifieur interne : 000655 ( PubMed/Curation ); précédent : 000654; suivant : 000656

Sequence Characteristics Distinguish Transcribed Enhancers from Promoters and Predict Their Breadth of Activity.

Auteurs : Laura L. Colbran [États-Unis] ; Ling Chen [États-Unis] ; John A. Capra [États-Unis]

Source :

RBID : pubmed:30696717

Descripteurs français

English descriptors

Abstract

Enhancers and promoters both regulate gene expression by recruiting transcription factors (TFs); however, the degree to which enhancer vs. promoter activity is due to differences in their sequences or to genomic context is the subject of ongoing debate. We examined this question by analyzing the sequences of thousands of transcribed enhancers and promoters from hundreds of cellular contexts previously identified by cap analysis of gene expression. Support vector machine classifiers trained on counts of all possible 6-bp-long sequences (6-mers) were able to accurately distinguish promoters from enhancers and distinguish their breadth of activity across tissues. Classifiers trained to predict enhancer activity also performed well when applied to promoter prediction tasks, but promoter-trained classifiers performed poorly on enhancers. This suggests that the learned sequence patterns predictive of enhancer activity generalize to promoters, but not vice versa. Our classifiers also indicate that there are functionally relevant differences in enhancer and promoter GC content beyond the influence of CpG islands. Furthermore, sequences characteristic of broad promoter or broad enhancer activity matched different TFs, with predicted ETS- and RFX-binding sites indicative of promoters, and AP-1 sites indicative of enhancers. Finally, we evaluated the ability of our models to distinguish enhancers and promoters defined by histone modifications. Separating these classes was substantially more difficult, and this difference may contribute to ongoing debates about the similarity of enhancers and promoters. In summary, our results suggest that high-confidence transcribed enhancers and promoters can largely be distinguished based on biologically relevant sequence properties.

DOI: 10.1534/genetics.118.301895
PubMed: 30696717

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pubmed:30696717

Le document en format XML

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promoter activity is due to differences in their sequences or to genomic context is the subject of ongoing debate. We examined this question by analyzing the sequences of thousands of transcribed enhancers and promoters from hundreds of cellular contexts previously identified by cap analysis of gene expression. Support vector machine classifiers trained on counts of all possible 6-bp-long sequences (6-mers) were able to accurately distinguish promoters from enhancers and distinguish their breadth of activity across tissues. Classifiers trained to predict enhancer activity also performed well when applied to promoter prediction tasks, but promoter-trained classifiers performed poorly on enhancers. This suggests that the learned sequence patterns predictive of enhancer activity generalize to promoters, but not vice versa. Our classifiers also indicate that there are functionally relevant differences in enhancer and promoter GC content beyond the influence of CpG islands. Furthermore, sequences characteristic of broad promoter or broad enhancer activity matched different TFs, with predicted ETS- and RFX-binding sites indicative of promoters, and AP-1 sites indicative of enhancers. Finally, we evaluated the ability of our models to distinguish enhancers and promoters defined by histone modifications. Separating these classes was substantially more difficult, and this difference may contribute to ongoing debates about the similarity of enhancers and promoters. In summary, our results suggest that high-confidence transcribed enhancers and promoters can largely be distinguished based on biologically relevant sequence properties.</div>
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</ArticleIdList>
<ReferenceList>
<Reference>
<Citation>Nature. 2014 Mar 27;507(7493):462-70</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">24670764</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Cell. 2012 Oct 26;151(3):658-70</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">23101632</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nat Neurosci. 2010 Nov;13(11):1330-7</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">20975757</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nucleic Acids Res. 2009 Oct;37(19):6305-15</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">19736212</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nature. 2012 Aug 2;488(7409):116-20</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">22763441</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Science. 2013 Feb 22;339(6122):950-3</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">23430654</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Gene. 2003 Jan 16;303:11-34</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">12559563</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Cell. 1986 Jun 6;45(5):753-60</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">3085957</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>BMC Med Genomics. 2017 May 24;10(Suppl 1):34</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">28589862</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Res. 2012 Dec;22(12):2399-408</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">23100115</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Biol. 2018 Jul 25;19(1):99</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">30045748</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Trends Cell Biol. 2018 Aug;28(8):608-630</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">29759817</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Bioinformatics. 2011 Apr 1;27(7):1017-8</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">21330290</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Pac Symp Biocomput. 2002;:564-75</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">11928508</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nature. 2014 Mar 27;507(7493):455-461</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">24670763</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Bioessays. 2015 Mar;37(3):314-23</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25450156</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>PLoS One. 2014 Nov 06;9(11):e111914</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25375357</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Science. 2013 Oct 11;342(6155):253-7</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">24115442</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Mol Cell. 2013 Mar 7;49(5):825-37</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">23473601</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nat Genet. 2014 Dec;46(12):1311-20</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25383968</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Res. 2016 Aug;26(8):1023-33</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">27311442</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nat Rev Genet. 2010 Jun;11(6):439-46</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">20442713</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Cell. 2015 May 21;161(5):1012-1025</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25959774</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Brief Bioinform. 2016 Nov;17(6):967-979</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26634919</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>N Engl J Med. 2015 Sep 3;373(10):895-907</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26287746</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Trends Genet. 2015 Aug;31(8):426-33</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26073855</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Biol. 2015 Dec 23;16:290</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26699896</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Annu Rev Genet. 2012;46:1-19</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">22905871</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Res. 2012 Nov;22(11):2278-89</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">22759862</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nucleic Acids Res. 2013 Jan;41(Database issue):D195-202</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">23175603</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Methods Mol Biol. 2010;609:223-39</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">20221922</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Mol Cell. 2012 Feb 24;45(4):447-58</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">22264824</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nature. 2015 Feb 19;518(7539):317-30</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25693563</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Trends Genet. 2019 Feb;35(2):93-103</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">30553552</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Pediatr Res. 2006 Apr;59(4 Pt 2):21R-5R</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">16549544</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nucleic Acids Res. 2016 Jan 4;44(D1):D110-5</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26531826</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Biochim Biophys Acta. 1991 Dec 10;1072(2-3):129-57</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">1751545</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Cell. 2010 Mar 5;140(5):744-52</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">20211142</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nature. 2012 Sep 6;489(7414):75-82</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">22955617</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nucleic Acids Res. 2007 Jan;35(Database issue):D88-92</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">17130149</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Biol. 2013;14(10):R117</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">24156763</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nucleic Acids Res. 2018 Jan 4;46(D1):D252-D259</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">29140464</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nature. 2011 Feb 10;470(7333):279-83</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">21160473</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nat Genet. 2007 Mar;39(3):311-8</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">17277777</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>PLoS Genet. 2013;9(8):e1003649</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">23935528</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Curr Opin Cell Biol. 1997 Apr;9(2):240-6</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">9069263</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Cell. 2015 Aug 27;162(5):948-59</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26317464</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Nat Biotechnol. 2016 Nov;34(11):1180-1190</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">27701403</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Curr Biol. 2010 Sep 14;20(17):R754-63</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">20833320</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>PLoS Comput Biol. 2014 Jul 17;10(7):e1003711</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25033408</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Science. 2015 May 8;348(6235):648-60</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25954001</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Genome Biol. 2007;8(2):R24</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">17324271</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>Bioinformatics. 2010 Mar 15;26(6):841-2</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">20110278</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList>
<Reference>
<Citation>BMC Genomics. 2017 Jul 17;18(1):536</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">28716036</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>

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