Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat
Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mochida, Keiichi [verfasserIn] Kawaura, Kanako [verfasserIn] Shimosaka, Etsuo [verfasserIn] Kawakami, Naoto [verfasserIn] Shin-I, Tadasu [verfasserIn] Kohara, Yuji [verfasserIn] Yamazaki, Yukiko [verfasserIn] Ogihara, Yasunari [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2006 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Molecular genetics and genomics - Berlin : Springer, 1908, 276(2006), 3 vom: 11. Juli, Seite 304-312 |
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| Übergeordnetes Werk: |
volume:276 ; year:2006 ; number:3 ; day:11 ; month:07 ; pages:304-312 |
| Links: |
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| DOI / URN: |
10.1007/s00438-006-0120-1 |
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| Katalog-ID: |
SPR005976375 |
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| 245 | 1 | 0 | |a Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
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| 520 | |a Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. | ||
| 650 | 4 | |a Global gene expression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Stress response gene |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Virtual display |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Common wheat |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kawaura, Kanako |e verfasserin |4 aut | |
| 700 | 1 | |a Shimosaka, Etsuo |e verfasserin |4 aut | |
| 700 | 1 | |a Kawakami, Naoto |e verfasserin |4 aut | |
| 700 | 1 | |a Shin-I, Tadasu |e verfasserin |4 aut | |
| 700 | 1 | |a Kohara, Yuji |e verfasserin |4 aut | |
| 700 | 1 | |a Yamazaki, Yukiko |e verfasserin |4 aut | |
| 700 | 1 | |a Ogihara, Yasunari |e verfasserin |4 aut | |
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10.1007/s00438-006-0120-1 doi (DE-627)SPR005976375 (SPR)s00438-006-0120-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Mochida, Keiichi verfasserin aut Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. Global gene expression (dpeaa)DE-He213 Stress response gene (dpeaa)DE-He213 Virtual display (dpeaa)DE-He213 Common wheat (dpeaa)DE-He213 Kawaura, Kanako verfasserin aut Shimosaka, Etsuo verfasserin aut Kawakami, Naoto verfasserin aut Shin-I, Tadasu verfasserin aut Kohara, Yuji verfasserin aut Yamazaki, Yukiko verfasserin aut Ogihara, Yasunari verfasserin aut Enthalten in Molecular genetics and genomics Berlin : Springer, 1908 276(2006), 3 vom: 11. Juli, Seite 304-312 (DE-627)254630243 (DE-600)1462070-4 1432-1874 nnns volume:276 year:2006 number:3 day:11 month:07 pages:304-312 https://dx.doi.org/10.1007/s00438-006-0120-1 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 276 2006 3 11 07 304-312 |
| spelling |
10.1007/s00438-006-0120-1 doi (DE-627)SPR005976375 (SPR)s00438-006-0120-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Mochida, Keiichi verfasserin aut Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. Global gene expression (dpeaa)DE-He213 Stress response gene (dpeaa)DE-He213 Virtual display (dpeaa)DE-He213 Common wheat (dpeaa)DE-He213 Kawaura, Kanako verfasserin aut Shimosaka, Etsuo verfasserin aut Kawakami, Naoto verfasserin aut Shin-I, Tadasu verfasserin aut Kohara, Yuji verfasserin aut Yamazaki, Yukiko verfasserin aut Ogihara, Yasunari verfasserin aut Enthalten in Molecular genetics and genomics Berlin : Springer, 1908 276(2006), 3 vom: 11. Juli, Seite 304-312 (DE-627)254630243 (DE-600)1462070-4 1432-1874 nnns volume:276 year:2006 number:3 day:11 month:07 pages:304-312 https://dx.doi.org/10.1007/s00438-006-0120-1 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 276 2006 3 11 07 304-312 |
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10.1007/s00438-006-0120-1 doi (DE-627)SPR005976375 (SPR)s00438-006-0120-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Mochida, Keiichi verfasserin aut Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. Global gene expression (dpeaa)DE-He213 Stress response gene (dpeaa)DE-He213 Virtual display (dpeaa)DE-He213 Common wheat (dpeaa)DE-He213 Kawaura, Kanako verfasserin aut Shimosaka, Etsuo verfasserin aut Kawakami, Naoto verfasserin aut Shin-I, Tadasu verfasserin aut Kohara, Yuji verfasserin aut Yamazaki, Yukiko verfasserin aut Ogihara, Yasunari verfasserin aut Enthalten in Molecular genetics and genomics Berlin : Springer, 1908 276(2006), 3 vom: 11. Juli, Seite 304-312 (DE-627)254630243 (DE-600)1462070-4 1432-1874 nnns volume:276 year:2006 number:3 day:11 month:07 pages:304-312 https://dx.doi.org/10.1007/s00438-006-0120-1 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 276 2006 3 11 07 304-312 |
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10.1007/s00438-006-0120-1 doi (DE-627)SPR005976375 (SPR)s00438-006-0120-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Mochida, Keiichi verfasserin aut Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. Global gene expression (dpeaa)DE-He213 Stress response gene (dpeaa)DE-He213 Virtual display (dpeaa)DE-He213 Common wheat (dpeaa)DE-He213 Kawaura, Kanako verfasserin aut Shimosaka, Etsuo verfasserin aut Kawakami, Naoto verfasserin aut Shin-I, Tadasu verfasserin aut Kohara, Yuji verfasserin aut Yamazaki, Yukiko verfasserin aut Ogihara, Yasunari verfasserin aut Enthalten in Molecular genetics and genomics Berlin : Springer, 1908 276(2006), 3 vom: 11. Juli, Seite 304-312 (DE-627)254630243 (DE-600)1462070-4 1432-1874 nnns volume:276 year:2006 number:3 day:11 month:07 pages:304-312 https://dx.doi.org/10.1007/s00438-006-0120-1 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 276 2006 3 11 07 304-312 |
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10.1007/s00438-006-0120-1 doi (DE-627)SPR005976375 (SPR)s00438-006-0120-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.13 bkl 42.20 bkl Mochida, Keiichi verfasserin aut Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. Global gene expression (dpeaa)DE-He213 Stress response gene (dpeaa)DE-He213 Virtual display (dpeaa)DE-He213 Common wheat (dpeaa)DE-He213 Kawaura, Kanako verfasserin aut Shimosaka, Etsuo verfasserin aut Kawakami, Naoto verfasserin aut Shin-I, Tadasu verfasserin aut Kohara, Yuji verfasserin aut Yamazaki, Yukiko verfasserin aut Ogihara, Yasunari verfasserin aut Enthalten in Molecular genetics and genomics Berlin : Springer, 1908 276(2006), 3 vom: 11. Juli, Seite 304-312 (DE-627)254630243 (DE-600)1462070-4 1432-1874 nnns volume:276 year:2006 number:3 day:11 month:07 pages:304-312 https://dx.doi.org/10.1007/s00438-006-0120-1 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.13 ASE 42.20 ASE AR 276 2006 3 11 07 304-312 |
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English |
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Enthalten in Molecular genetics and genomics 276(2006), 3 vom: 11. Juli, Seite 304-312 volume:276 year:2006 number:3 day:11 month:07 pages:304-312 |
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Enthalten in Molecular genetics and genomics 276(2006), 3 vom: 11. Juli, Seite 304-312 volume:276 year:2006 number:3 day:11 month:07 pages:304-312 |
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Global gene expression Stress response gene Virtual display Common wheat |
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Molecular genetics and genomics |
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Mochida, Keiichi @@aut@@ Kawaura, Kanako @@aut@@ Shimosaka, Etsuo @@aut@@ Kawakami, Naoto @@aut@@ Shin-I, Tadasu @@aut@@ Kohara, Yuji @@aut@@ Yamazaki, Yukiko @@aut@@ Ogihara, Yasunari @@aut@@ |
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2006-07-11T00:00:00Z |
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Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. 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Mochida, Keiichi |
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Mochida, Keiichi ddc 570 bkl 42.13 bkl 42.20 misc Global gene expression misc Stress response gene misc Virtual display misc Common wheat Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
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570 ASE 42.13 bkl 42.20 bkl Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat Global gene expression (dpeaa)DE-He213 Stress response gene (dpeaa)DE-He213 Virtual display (dpeaa)DE-He213 Common wheat (dpeaa)DE-He213 |
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ddc 570 bkl 42.13 bkl 42.20 misc Global gene expression misc Stress response gene misc Virtual display misc Common wheat |
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ddc 570 bkl 42.13 bkl 42.20 misc Global gene expression misc Stress response gene misc Virtual display misc Common wheat |
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ddc 570 bkl 42.13 bkl 42.20 misc Global gene expression misc Stress response gene misc Virtual display misc Common wheat |
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| title |
Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
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| title_full |
Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
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Mochida, Keiichi |
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Molecular genetics and genomics |
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Molecular genetics and genomics |
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Mochida, Keiichi Kawaura, Kanako Shimosaka, Etsuo Kawakami, Naoto Shin-I, Tadasu Kohara, Yuji Yamazaki, Yukiko Ogihara, Yasunari |
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tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
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Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
| abstract |
Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. |
| abstractGer |
Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. |
| abstract_unstemmed |
Abstract In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics. |
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Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat |
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| score |
7.3993406 |