c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression

Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kechris, Katerina [verfasserIn] Li, Hao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Transcription Factor Binding Site Upstream Sequence Global Alignment Transcription Factor Motif Yeast Transcription Factor

Anmerkung:	© Kechris and Li; licensee BioMed Central Ltd. 2008

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 9(2008), 1 vom: 28. Nov.
Übergeordnetes Werk:	volume:9 ; year:2008 ; number:1 ; day:28 ; month:11

Links:	Volltext

DOI / URN:	10.1186/1471-2105-9-506

Katalog-ID:	SPR026847213

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520			\|a Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.
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912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
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912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_2190
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912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
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912			\|a GBV_ILN_4335
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matchkey_str	article:14712105:2008----::rdcicroaigeuneosrainoeetoishto
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publishDate	2008
allfields	10.1186/1471-2105-9-506 doi (DE-627)SPR026847213 (SPR)1471-2105-9-506-e DE-627 ger DE-627 rakwb eng Kechris, Katerina verfasserin aut c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kechris and Li; licensee BioMed Central Ltd. 2008 Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. Transcription Factor Binding Site (dpeaa)DE-He213 Upstream Sequence (dpeaa)DE-He213 Global Alignment (dpeaa)DE-He213 Transcription Factor Motif (dpeaa)DE-He213 Yeast Transcription Factor (dpeaa)DE-He213 Li, Hao aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 28. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:28 month:11 https://dx.doi.org/10.1186/1471-2105-9-506 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 9 2008 1 28 11
spelling	10.1186/1471-2105-9-506 doi (DE-627)SPR026847213 (SPR)1471-2105-9-506-e DE-627 ger DE-627 rakwb eng Kechris, Katerina verfasserin aut c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kechris and Li; licensee BioMed Central Ltd. 2008 Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. Transcription Factor Binding Site (dpeaa)DE-He213 Upstream Sequence (dpeaa)DE-He213 Global Alignment (dpeaa)DE-He213 Transcription Factor Motif (dpeaa)DE-He213 Yeast Transcription Factor (dpeaa)DE-He213 Li, Hao aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 28. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:28 month:11 https://dx.doi.org/10.1186/1471-2105-9-506 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 9 2008 1 28 11
allfields_unstemmed	10.1186/1471-2105-9-506 doi (DE-627)SPR026847213 (SPR)1471-2105-9-506-e DE-627 ger DE-627 rakwb eng Kechris, Katerina verfasserin aut c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kechris and Li; licensee BioMed Central Ltd. 2008 Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. Transcription Factor Binding Site (dpeaa)DE-He213 Upstream Sequence (dpeaa)DE-He213 Global Alignment (dpeaa)DE-He213 Transcription Factor Motif (dpeaa)DE-He213 Yeast Transcription Factor (dpeaa)DE-He213 Li, Hao aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 28. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:28 month:11 https://dx.doi.org/10.1186/1471-2105-9-506 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 9 2008 1 28 11
allfieldsGer	10.1186/1471-2105-9-506 doi (DE-627)SPR026847213 (SPR)1471-2105-9-506-e DE-627 ger DE-627 rakwb eng Kechris, Katerina verfasserin aut c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kechris and Li; licensee BioMed Central Ltd. 2008 Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. Transcription Factor Binding Site (dpeaa)DE-He213 Upstream Sequence (dpeaa)DE-He213 Global Alignment (dpeaa)DE-He213 Transcription Factor Motif (dpeaa)DE-He213 Yeast Transcription Factor (dpeaa)DE-He213 Li, Hao aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 28. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:28 month:11 https://dx.doi.org/10.1186/1471-2105-9-506 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 9 2008 1 28 11
allfieldsSound	10.1186/1471-2105-9-506 doi (DE-627)SPR026847213 (SPR)1471-2105-9-506-e DE-627 ger DE-627 rakwb eng Kechris, Katerina verfasserin aut c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kechris and Li; licensee BioMed Central Ltd. 2008 Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. Transcription Factor Binding Site (dpeaa)DE-He213 Upstream Sequence (dpeaa)DE-He213 Global Alignment (dpeaa)DE-He213 Transcription Factor Motif (dpeaa)DE-He213 Yeast Transcription Factor (dpeaa)DE-He213 Li, Hao aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 28. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:28 month:11 https://dx.doi.org/10.1186/1471-2105-9-506 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 9 2008 1 28 11
language	English
source	Enthalten in BMC bioinformatics 9(2008), 1 vom: 28. Nov. volume:9 year:2008 number:1 day:28 month:11
sourceStr	Enthalten in BMC bioinformatics 9(2008), 1 vom: 28. Nov. volume:9 year:2008 number:1 day:28 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Transcription Factor Binding Site Upstream Sequence Global Alignment Transcription Factor Motif Yeast Transcription Factor
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container_title	BMC bioinformatics
authorswithroles_txt_mv	Kechris, Katerina @@aut@@ Li, Hao @@aut@@
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author	Kechris, Katerina
spellingShingle	Kechris, Katerina misc Transcription Factor Binding Site misc Upstream Sequence misc Global Alignment misc Transcription Factor Motif misc Yeast Transcription Factor c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression
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topic_title	c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression Transcription Factor Binding Site (dpeaa)DE-He213 Upstream Sequence (dpeaa)DE-He213 Global Alignment (dpeaa)DE-He213 Transcription Factor Motif (dpeaa)DE-He213 Yeast Transcription Factor (dpeaa)DE-He213
topic	misc Transcription Factor Binding Site misc Upstream Sequence misc Global Alignment misc Transcription Factor Motif misc Yeast Transcription Factor
topic_unstemmed	misc Transcription Factor Binding Site misc Upstream Sequence misc Global Alignment misc Transcription Factor Motif misc Yeast Transcription Factor
topic_browse	misc Transcription Factor Binding Site misc Upstream Sequence misc Global Alignment misc Transcription Factor Motif misc Yeast Transcription Factor
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title	c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression
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title_full	c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression
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author_browse	Kechris, Katerina Li, Hao
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title_sort	c-reduce: incorporating sequence conservation to detect motifs that correlate with expression
title_auth	c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression
abstract	Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. © Kechris and Li; licensee BioMed Central Ltd. 2008
abstractGer	Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. © Kechris and Li; licensee BioMed Central Ltd. 2008
abstract_unstemmed	Background Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. Results In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila. Conclusion Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. © Kechris and Li; licensee BioMed Central Ltd. 2008
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title_short	c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression
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c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression

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