Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

<p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error pron...
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Autor*in:	Selbig Joachim [verfasserIn] Weicht Daniel [verfasserIn] Redestig Henning [verfasserIn] Hannah Matthew A [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 8(2007), 1, p 454
Übergeordnetes Werk:	volume:8 ; year:2007 ; number:1, p 454

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2105-8-454

Katalog-ID:	DOAJ026195925

Internformat


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matchkey_str	article:14712105:2007----::rncitofcotrepeitouigutpehrepesotmsre
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allfields	10.1186/1471-2105-8-454 doi (DE-627)DOAJ026195925 (DE-599)DOAJ8a6db98348e04c17aea2b3af4c795273 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Selbig Joachim verfasserin aut Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p< Computer applications to medicine. Medical informatics Biology (General) Weicht Daniel verfasserin aut Redestig Henning verfasserin aut Hannah Matthew A verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 454 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 454 https://doi.org/10.1186/1471-2105-8-454 kostenfrei https://doaj.org/article/8a6db98348e04c17aea2b3af4c795273 kostenfrei http://www.biomedcentral.com/1471-2105/8/454 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 8 2007 1, p 454
spelling	10.1186/1471-2105-8-454 doi (DE-627)DOAJ026195925 (DE-599)DOAJ8a6db98348e04c17aea2b3af4c795273 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Selbig Joachim verfasserin aut Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p< Computer applications to medicine. Medical informatics Biology (General) Weicht Daniel verfasserin aut Redestig Henning verfasserin aut Hannah Matthew A verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 454 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 454 https://doi.org/10.1186/1471-2105-8-454 kostenfrei https://doaj.org/article/8a6db98348e04c17aea2b3af4c795273 kostenfrei http://www.biomedcentral.com/1471-2105/8/454 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 8 2007 1, p 454
allfields_unstemmed	10.1186/1471-2105-8-454 doi (DE-627)DOAJ026195925 (DE-599)DOAJ8a6db98348e04c17aea2b3af4c795273 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Selbig Joachim verfasserin aut Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p< Computer applications to medicine. Medical informatics Biology (General) Weicht Daniel verfasserin aut Redestig Henning verfasserin aut Hannah Matthew A verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 454 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 454 https://doi.org/10.1186/1471-2105-8-454 kostenfrei https://doaj.org/article/8a6db98348e04c17aea2b3af4c795273 kostenfrei http://www.biomedcentral.com/1471-2105/8/454 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 8 2007 1, p 454
allfieldsGer	10.1186/1471-2105-8-454 doi (DE-627)DOAJ026195925 (DE-599)DOAJ8a6db98348e04c17aea2b3af4c795273 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Selbig Joachim verfasserin aut Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p< Computer applications to medicine. Medical informatics Biology (General) Weicht Daniel verfasserin aut Redestig Henning verfasserin aut Hannah Matthew A verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 454 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 454 https://doi.org/10.1186/1471-2105-8-454 kostenfrei https://doaj.org/article/8a6db98348e04c17aea2b3af4c795273 kostenfrei http://www.biomedcentral.com/1471-2105/8/454 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 8 2007 1, p 454
allfieldsSound	10.1186/1471-2105-8-454 doi (DE-627)DOAJ026195925 (DE-599)DOAJ8a6db98348e04c17aea2b3af4c795273 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Selbig Joachim verfasserin aut Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p< Computer applications to medicine. Medical informatics Biology (General) Weicht Daniel verfasserin aut Redestig Henning verfasserin aut Hannah Matthew A verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 454 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 454 https://doi.org/10.1186/1471-2105-8-454 kostenfrei https://doaj.org/article/8a6db98348e04c17aea2b3af4c795273 kostenfrei http://www.biomedcentral.com/1471-2105/8/454 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 8 2007 1, p 454
language	English
source	In BMC Bioinformatics 8(2007), 1, p 454 volume:8 year:2007 number:1, p 454
sourceStr	In BMC Bioinformatics 8(2007), 1, p 454 volume:8 year:2007 number:1, p 454
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Biology (General)
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container_title	BMC Bioinformatics
authorswithroles_txt_mv	Selbig Joachim @@aut@@ Weicht Daniel @@aut@@ Redestig Henning @@aut@@ Hannah Matthew A @@aut@@
publishDateDaySort_date	2007-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ026195925
language_de	englisch
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callnumber-first	R - Medicine
author	Selbig Joachim
spellingShingle	Selbig Joachim misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General) Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
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topic_title	R858-859.7 QH301-705.5 Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
topic	misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
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title_full	Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
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author_browse	Selbig Joachim Weicht Daniel Redestig Henning Hannah Matthew A
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title_sort	transcription factor target prediction using multiple short expression time series from arabidopsis thaliana
callnumber	R858-859.7
title_auth	Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
abstract	<p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.</p< <p<Results</p< <p<In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF – target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.</p< <p<Conclusion</p< <p<CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.</p<
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Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

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