Rice leaves in the field in 2008 with 1- or 2-min interval
Ontology highlight
ABSTRACT:
ORGANISM(S): Oryza sativa
PROVIDER: GSE52120 | GEO | 2015/03/10
SECONDARY ACCESSION(S): PRJNA231316
REPOSITORIES: GEO
Similar Datasets
Project description:Time-course transcriptional profiling of rice leaf in the field in 2009. This experiment was performed to validate the results of field transcriptomic modeling. Using 461 field transcriptome data obtained in 2008 (GSE36040; GSE36042; GSE36043; GSE36044; GSE18685) and the corresponding meteorologicla dara, we perfomred statistical modeling of transcriptome.
2012-12-07 | GSE36777 | GEO
Project description:Time-course transcriptional profiling of rice leaf in the field in 2009. This experiment was performed to validate the results of field transcriptomic modeling. Using 461 field transcriptome data obtained in 2008 (GSE36040; GSE36042; GSE36043; GSE36044; GSE18685) and the corresponding meteorologicla dara, we perfomred statistical modeling of transcriptome. Rice leaves (Norin8 vs. osgi) in the paddy field were collected on Aug. 10 - 12, 24 - 25, 31, Oct. 8 - 9 in 2009.
2012-12-07 | E-GEOD-36777 | biostudies-arrayexpress
Project description:Diurnal time-course transcriptional profiling of rice leaf in the field comparing a circadian clock related mutant, osgi, with the wild-type (WT). Two sample experiments (WT vs. osgi) : 13 time-points (2h interval), 8 replicates (2leaves from individual plants x 4stages(each staggered by tranplanting dates with one week interval)): sampled on Aug. 12th 7:00-Aug.13th 7:00 (2008) at a paddy field in Tsukuba (Japan)
2011-05-18 | E-GEOD-18685 | biostudies-arrayexpress
Project description:Diurnal time-course transcriptional profiling of rice leaf in the field comparing a circadian clock related mutant, osgi, with the wild-type (WT).
2011-05-18 | GSE18685 | GEO
Project description:Time-course transcriptional profiling of rice leaf in Takatsuki field in 2013. This experiment was performed to analyze the dynamics of field transcriptome data. We have previously demonstrated statistical modeling of gene expression under various environmental factors using field transcriptome data obtained from samples grown in a paddy field at Tsukuba, Japan. In this series of experiments, we would like to improve the models for practical use. In order to do that, we have done field transcriptome assay in seven paddy fields at different sites in Japan (or, Sappro, Furukawa, Toyama, Tsukuba, Takatsuki, Furukawa, and Fukuoka). This data is from one of the places, Takatsuki.
2017-03-31 | GSE65603 | GEO
Project description:Time-course transcriptional profiling of rice leaf in Sapporo field in 2013. This experiment was performed to analyze the dynamics of field transcriptome data. We have previously demonstrated statistical modeling of gene expression under various environmental factors using field transcriptome data obtained from samples grown in a paddy field at Tsukuba, Japan. In this series of experiments, we would like to improve the models for practical use. In order to do that, we have done field transcriptome assay in seven paddy fields at different sites in Japan (or, Sappro, Furukawa, Toyama, Tsukuba, Takatsuki, Furukawa, and Fukuoka). This data is from one of the places, Sapporo.
2017-03-31 | GSE65602 | GEO
Project description:We have previously demonstrated statistical modeling of gene expression under various environmental factors using field transcriptome data obtained from samples grown in a paddy field at Tsukuba, Japan. In this series of experiments, we would like to improve the models for practical use. In order to do that, we have done field transcriptome assay in seven paddy fields at different sites in Japan (or, Sappro, Furukawa, Toyama, Tsukuba, Takatsuki, Furukawa, and Fukuoka). This data is from one of the places, Tsukuba. Time-course transcriptional profiling of rice leaf in Tsukuba field in 2014. This experiment was performed to analyze the dynamics of field transcriptome data.
2017-03-31 | GSE65612 | GEO
Project description:We have previously demonstrated statistical modeling of gene expression under various environmental factors using field transcriptome data obtained from samples grown in a paddy field at Tsukuba, Japan. In this series of experiments, we would like to improve the models for practical use. In order to do that, we have done field transcriptome assay in seven paddy fields at different sites in Japan (or, Sappro, Furukawa, Toyama, Tsukuba, Takatsuki, Furukawa, and Fukuoka). This data is from one of the places, Toyama. Time-course transcriptional profiling of rice leaf in Toyama field in 2014. This experiment was performed to analyze the dynamics of field transcriptome data.
2017-03-31 | GSE65611 | GEO
Project description:We have previously demonstrated statistical modeling of gene expression under various environmental factors using field transcriptome data obtained from samples grown in a paddy field at Tsukuba, Japan. In this series of experiments, we would like to improve the models for practical use. In order to do that, we have done field transcriptome assay in seven paddy fields at different sites in Japan (or, Sappro, Furukawa, Toyama, Tsukuba, Takatsuki, Furukawa, and Fukuoka). This data is from one of the places, Tsukuba. Time-course transcriptional profiling of rice leaf in Tsukuba field in 2013. This experiment was performed to analyze the dynamics of field transcriptome data.
2017-03-31 | GSE65605 | GEO
Project description:We have previously demonstrated statistical modeling of gene expression under various environmental factors using field transcriptome data obtained from samples grown in a paddy field at Tsukuba, Japan. In this series of experiments, we would like to improve the models for practical use. In order to do that, we have done field transcriptome assay in seven paddy fields at different sites in Japan (or, Sappro, Furukawa, Toyama, Tsukuba, Takatsuki, Furukawa, and Fukuoka). This data is from one of the places, Toyama. Time-course transcriptional profiling of rice leaf in Toyama field in 2013. This experiment was performed to analyze the dynamics of field transcriptome data.
2017-03-31 | GSE65604 | GEO