MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates

Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noi...
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Autor*in:	Bérard, Caroline [verfasserIn] Seifert, Michael Mary-Huard, Tristan Martin-Magniette, Marie-Laure

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Hide Markov Model Spatial Dependency Tiling Array Hide Markov Model Model Model Plant Arabidopsis Thaliana

Anmerkung:	© Bérard et al.; licensee BioMed Central Ltd. 2013

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 14(2013), 1 vom: 09. Sept.
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:1 ; day:09 ; month:09

Links:	Volltext

DOI / URN:	10.1186/1471-2105-14-271

Katalog-ID:	SPR026885751

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520			\|a Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org.
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matchkey_str	article:14712105:2013----::utcimxmarakgfrhphpaanlssoeigptadpnec
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allfields	10.1186/1471-2105-14-271 doi (DE-627)SPR026885751 (SPR)1471-2105-14-271-e DE-627 ger DE-627 rakwb eng Bérard, Caroline verfasserin aut MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bérard et al.; licensee BioMed Central Ltd. 2013 Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. Hide Markov Model (dpeaa)DE-He213 Spatial Dependency (dpeaa)DE-He213 Tiling Array (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Model Plant Arabidopsis Thaliana (dpeaa)DE-He213 Seifert, Michael aut Mary-Huard, Tristan aut Martin-Magniette, Marie-Laure aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 09. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:09 month:09 https://dx.doi.org/10.1186/1471-2105-14-271 kostenfrei 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 09 09
spelling	10.1186/1471-2105-14-271 doi (DE-627)SPR026885751 (SPR)1471-2105-14-271-e DE-627 ger DE-627 rakwb eng Bérard, Caroline verfasserin aut MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bérard et al.; licensee BioMed Central Ltd. 2013 Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. Hide Markov Model (dpeaa)DE-He213 Spatial Dependency (dpeaa)DE-He213 Tiling Array (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Model Plant Arabidopsis Thaliana (dpeaa)DE-He213 Seifert, Michael aut Mary-Huard, Tristan aut Martin-Magniette, Marie-Laure aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 09. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:09 month:09 https://dx.doi.org/10.1186/1471-2105-14-271 kostenfrei 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 09 09
allfields_unstemmed	10.1186/1471-2105-14-271 doi (DE-627)SPR026885751 (SPR)1471-2105-14-271-e DE-627 ger DE-627 rakwb eng Bérard, Caroline verfasserin aut MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bérard et al.; licensee BioMed Central Ltd. 2013 Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. Hide Markov Model (dpeaa)DE-He213 Spatial Dependency (dpeaa)DE-He213 Tiling Array (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Model Plant Arabidopsis Thaliana (dpeaa)DE-He213 Seifert, Michael aut Mary-Huard, Tristan aut Martin-Magniette, Marie-Laure aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 09. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:09 month:09 https://dx.doi.org/10.1186/1471-2105-14-271 kostenfrei 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 09 09
allfieldsGer	10.1186/1471-2105-14-271 doi (DE-627)SPR026885751 (SPR)1471-2105-14-271-e DE-627 ger DE-627 rakwb eng Bérard, Caroline verfasserin aut MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bérard et al.; licensee BioMed Central Ltd. 2013 Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. Hide Markov Model (dpeaa)DE-He213 Spatial Dependency (dpeaa)DE-He213 Tiling Array (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Model Plant Arabidopsis Thaliana (dpeaa)DE-He213 Seifert, Michael aut Mary-Huard, Tristan aut Martin-Magniette, Marie-Laure aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 09. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:09 month:09 https://dx.doi.org/10.1186/1471-2105-14-271 kostenfrei 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 09 09
allfieldsSound	10.1186/1471-2105-14-271 doi (DE-627)SPR026885751 (SPR)1471-2105-14-271-e DE-627 ger DE-627 rakwb eng Bérard, Caroline verfasserin aut MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bérard et al.; licensee BioMed Central Ltd. 2013 Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. Hide Markov Model (dpeaa)DE-He213 Spatial Dependency (dpeaa)DE-He213 Tiling Array (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Model Plant Arabidopsis Thaliana (dpeaa)DE-He213 Seifert, Michael aut Mary-Huard, Tristan aut Martin-Magniette, Marie-Laure aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 09. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:09 month:09 https://dx.doi.org/10.1186/1471-2105-14-271 kostenfrei 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 09 09
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source	Enthalten in BMC bioinformatics 14(2013), 1 vom: 09. Sept. volume:14 year:2013 number:1 day:09 month:09
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author	Bérard, Caroline
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topic_title	MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates Hide Markov Model (dpeaa)DE-He213 Spatial Dependency (dpeaa)DE-He213 Tiling Array (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Model Plant Arabidopsis Thaliana (dpeaa)DE-He213
topic	misc Hide Markov Model misc Spatial Dependency misc Tiling Array misc Hide Markov Model Model misc Model Plant Arabidopsis Thaliana
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title	MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates
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title_full	MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates
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author_browse	Bérard, Caroline Seifert, Michael Mary-Huard, Tristan Martin-Magniette, Marie-Laure
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title_sort	multichipmixhmm: an r package for chip-chip data analysis modeling spatial dependencies and multiple replicates
title_auth	MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates
abstract	Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. © Bérard et al.; licensee BioMed Central Ltd. 2013
abstractGer	Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. © Bérard et al.; licensee BioMed Central Ltd. 2013
abstract_unstemmed	Background Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements. Conclusion We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. © Bérard et al.; licensee BioMed Central Ltd. 2013
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title_short	MultiChIPmixHMM: an R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates
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author2	Seifert, Michael Mary-Huard, Tristan Martin-Magniette, Marie-Laure
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up_date	2024-07-03T23:16:16.839Z
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