Spectacle: fast chromatin state annotation using spectral learning

Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have over...
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Autor*in:	Song, Jimin [verfasserIn] Chen, Kevin C

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Epigenetic Mark Chromatin State Histone Mark Class Imbalance Chromatin Mark

Anmerkung:	© Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: Genome biology - London : BioMed Central, 2000, 16(2015), 1 vom: 12. Feb.
Übergeordnetes Werk:	volume:16 ; year:2015 ; number:1 ; day:12 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s13059-015-0598-0

Katalog-ID:	SPR030022320

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520			\|a Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle.
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allfields	10.1186/s13059-015-0598-0 doi (DE-627)SPR030022320 (SPR)s13059-015-0598-0-e DE-627 ger DE-627 rakwb eng Song, Jimin verfasserin aut Spectacle: fast chromatin state annotation using spectral learning 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. Epigenetic Mark (dpeaa)DE-He213 Chromatin State (dpeaa)DE-He213 Histone Mark (dpeaa)DE-He213 Class Imbalance (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Chen, Kevin C aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 12. Feb. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:12 month:02 https://dx.doi.org/10.1186/s13059-015-0598-0 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 16 2015 1 12 02
spelling	10.1186/s13059-015-0598-0 doi (DE-627)SPR030022320 (SPR)s13059-015-0598-0-e DE-627 ger DE-627 rakwb eng Song, Jimin verfasserin aut Spectacle: fast chromatin state annotation using spectral learning 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. Epigenetic Mark (dpeaa)DE-He213 Chromatin State (dpeaa)DE-He213 Histone Mark (dpeaa)DE-He213 Class Imbalance (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Chen, Kevin C aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 12. Feb. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:12 month:02 https://dx.doi.org/10.1186/s13059-015-0598-0 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 16 2015 1 12 02
allfields_unstemmed	10.1186/s13059-015-0598-0 doi (DE-627)SPR030022320 (SPR)s13059-015-0598-0-e DE-627 ger DE-627 rakwb eng Song, Jimin verfasserin aut Spectacle: fast chromatin state annotation using spectral learning 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. Epigenetic Mark (dpeaa)DE-He213 Chromatin State (dpeaa)DE-He213 Histone Mark (dpeaa)DE-He213 Class Imbalance (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Chen, Kevin C aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 12. Feb. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:12 month:02 https://dx.doi.org/10.1186/s13059-015-0598-0 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 16 2015 1 12 02
allfieldsGer	10.1186/s13059-015-0598-0 doi (DE-627)SPR030022320 (SPR)s13059-015-0598-0-e DE-627 ger DE-627 rakwb eng Song, Jimin verfasserin aut Spectacle: fast chromatin state annotation using spectral learning 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. Epigenetic Mark (dpeaa)DE-He213 Chromatin State (dpeaa)DE-He213 Histone Mark (dpeaa)DE-He213 Class Imbalance (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Chen, Kevin C aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 12. Feb. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:12 month:02 https://dx.doi.org/10.1186/s13059-015-0598-0 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 16 2015 1 12 02
allfieldsSound	10.1186/s13059-015-0598-0 doi (DE-627)SPR030022320 (SPR)s13059-015-0598-0-e DE-627 ger DE-627 rakwb eng Song, Jimin verfasserin aut Spectacle: fast chromatin state annotation using spectral learning 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. Epigenetic Mark (dpeaa)DE-He213 Chromatin State (dpeaa)DE-He213 Histone Mark (dpeaa)DE-He213 Class Imbalance (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213 Chen, Kevin C aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 12. Feb. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:12 month:02 https://dx.doi.org/10.1186/s13059-015-0598-0 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 16 2015 1 12 02
language	English
source	Enthalten in Genome biology 16(2015), 1 vom: 12. Feb. volume:16 year:2015 number:1 day:12 month:02
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Epigenetic Mark Chromatin State Histone Mark Class Imbalance Chromatin Mark
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container_title	Genome biology
authorswithroles_txt_mv	Song, Jimin @@aut@@ Chen, Kevin C @@aut@@
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author	Song, Jimin
spellingShingle	Song, Jimin misc Epigenetic Mark misc Chromatin State misc Histone Mark misc Class Imbalance misc Chromatin Mark Spectacle: fast chromatin state annotation using spectral learning
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topic_title	Spectacle: fast chromatin state annotation using spectral learning Epigenetic Mark (dpeaa)DE-He213 Chromatin State (dpeaa)DE-He213 Histone Mark (dpeaa)DE-He213 Class Imbalance (dpeaa)DE-He213 Chromatin Mark (dpeaa)DE-He213
topic	misc Epigenetic Mark misc Chromatin State misc Histone Mark misc Class Imbalance misc Chromatin Mark
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title	Spectacle: fast chromatin state annotation using spectral learning
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title_full	Spectacle: fast chromatin state annotation using spectral learning
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title_sort	spectacle: fast chromatin state annotation using spectral learning
title_auth	Spectacle: fast chromatin state annotation using spectral learning
abstract	Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Abstract Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. © Song and Chen; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Spectacle: fast chromatin state annotation using spectral learning
url	https://dx.doi.org/10.1186/s13059-015-0598-0
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