Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data

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 method...
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Autor*in:	Podsiadło, Agnieszka [verfasserIn] Wrzesień, Mariusz Paja, Wiesław Rudnicki, Witold Wilczyński, Bartek

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Histone Modification Epigenetic Mark Random Forest Classifier Chromatin Mark Feature Importance

Anmerkung:	© Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC systems biology - London : BioMed Central, 2007, 7(2013), Suppl 6 vom: 13. Dez.
Übergeordnetes Werk:	volume:7 ; year:2013 ; number:Suppl 6 ; day:13 ; month:12

Links:	Volltext

DOI / URN:	10.1186/1752-0509-7-S6-S16

Katalog-ID:	SPR028417194

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520			\|a 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 ofenhancers can generalize with significant accuracy beyond the training set.
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allfields	10.1186/1752-0509-7-S6-S16 doi (DE-627)SPR028417194 (SPR)1752-0509-7-S6-S16-e DE-627 ger DE-627 rakwb eng Podsiadło, Agnieszka verfasserin aut Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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 ofenhancers can generalize with significant accuracy beyond the training set. Histone Modification (dpeaa)DE-He213 Epigenetic Mark (dpeaa)DE-He213 Random Forest Classifier (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Feature Importance (dpeaa)DE-He213 Wrzesień, Mariusz aut Paja, Wiesław aut Rudnicki, Witold aut Wilczyński, Bartek aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 6 vom: 13. Dez. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 6 day:13 month:12 https://dx.doi.org/10.1186/1752-0509-7-S6-S16 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2013 Suppl 6 13 12
spelling	10.1186/1752-0509-7-S6-S16 doi (DE-627)SPR028417194 (SPR)1752-0509-7-S6-S16-e DE-627 ger DE-627 rakwb eng Podsiadło, Agnieszka verfasserin aut Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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 ofenhancers can generalize with significant accuracy beyond the training set. Histone Modification (dpeaa)DE-He213 Epigenetic Mark (dpeaa)DE-He213 Random Forest Classifier (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Feature Importance (dpeaa)DE-He213 Wrzesień, Mariusz aut Paja, Wiesław aut Rudnicki, Witold aut Wilczyński, Bartek aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 6 vom: 13. Dez. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 6 day:13 month:12 https://dx.doi.org/10.1186/1752-0509-7-S6-S16 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2013 Suppl 6 13 12
allfields_unstemmed	10.1186/1752-0509-7-S6-S16 doi (DE-627)SPR028417194 (SPR)1752-0509-7-S6-S16-e DE-627 ger DE-627 rakwb eng Podsiadło, Agnieszka verfasserin aut Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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 ofenhancers can generalize with significant accuracy beyond the training set. Histone Modification (dpeaa)DE-He213 Epigenetic Mark (dpeaa)DE-He213 Random Forest Classifier (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Feature Importance (dpeaa)DE-He213 Wrzesień, Mariusz aut Paja, Wiesław aut Rudnicki, Witold aut Wilczyński, Bartek aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 6 vom: 13. Dez. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 6 day:13 month:12 https://dx.doi.org/10.1186/1752-0509-7-S6-S16 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2013 Suppl 6 13 12
allfieldsGer	10.1186/1752-0509-7-S6-S16 doi (DE-627)SPR028417194 (SPR)1752-0509-7-S6-S16-e DE-627 ger DE-627 rakwb eng Podsiadło, Agnieszka verfasserin aut Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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 ofenhancers can generalize with significant accuracy beyond the training set. Histone Modification (dpeaa)DE-He213 Epigenetic Mark (dpeaa)DE-He213 Random Forest Classifier (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Feature Importance (dpeaa)DE-He213 Wrzesień, Mariusz aut Paja, Wiesław aut Rudnicki, Witold aut Wilczyński, Bartek aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 6 vom: 13. Dez. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 6 day:13 month:12 https://dx.doi.org/10.1186/1752-0509-7-S6-S16 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2013 Suppl 6 13 12
allfieldsSound	10.1186/1752-0509-7-S6-S16 doi (DE-627)SPR028417194 (SPR)1752-0509-7-S6-S16-e DE-627 ger DE-627 rakwb eng Podsiadło, Agnieszka verfasserin aut Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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 ofenhancers can generalize with significant accuracy beyond the training set. Histone Modification (dpeaa)DE-He213 Epigenetic Mark (dpeaa)DE-He213 Random Forest Classifier (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Feature Importance (dpeaa)DE-He213 Wrzesień, Mariusz aut Paja, Wiesław aut Rudnicki, Witold aut Wilczyński, Bartek aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 6 vom: 13. Dez. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 6 day:13 month:12 https://dx.doi.org/10.1186/1752-0509-7-S6-S16 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2013 Suppl 6 13 12
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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author	Podsiadło, Agnieszka
spellingShingle	Podsiadło, Agnieszka misc Histone Modification misc Epigenetic Mark misc Random Forest Classifier misc Chromatin Mark misc Feature Importance Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data
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topic_title	Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data Histone Modification (dpeaa)DE-He213 Epigenetic Mark (dpeaa)DE-He213 Random Forest Classifier (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Feature Importance (dpeaa)DE-He213
topic	misc Histone Modification misc Epigenetic Mark misc Random Forest Classifier misc Chromatin Mark misc Feature Importance
topic_unstemmed	misc Histone Modification misc Epigenetic Mark misc Random Forest Classifier misc Chromatin Mark misc Feature Importance
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title	Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data
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title_full	Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data
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title_sort	active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data
title_auth	Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data
abstract	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 ofenhancers can generalize with significant accuracy beyond the training set. © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	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 ofenhancers can generalize with significant accuracy beyond the training set. © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	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 ofenhancers can generalize with significant accuracy beyond the training set. © Podsiadło et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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container_issue	Suppl 6
title_short	Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data
url	https://dx.doi.org/10.1186/1752-0509-7-S6-S16
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author2	Wrzesień, Mariusz Paja, Wiesław Rudnicki, Witold Wilczyński, Bartek
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up_date	2024-07-03T19:21:46.090Z
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data

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