Oryza sativa
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA197246 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:DNA microarray data from transgenic rice Huahui 1 (HH1) and its parent Minghui 63 (MH63)
| PRJNA149257 | ENA
Project description:DNA microarray data from root of transgenic rice Huahui 1 (HH1) and its parent Minghui 63 (MH63)
2015-11-20 | E-GEOD-46086 | ExpressionAtlas
Project description:DNA microarray analysis has been proved to be an effective method in investigating unintended effects in genetically modified (GM) crops. However, unintended effects of GM plants in leaves through DNA microarray analysis has many researches, but research of unintended effects of GM plants of the underground portion has few. In this study, DNA microarray analysis was used to detect DEG in underground portions between transgenic rice HH1 and its non-transgenic control MH63. We used microarrays to study unintended effects in root of transgenic rice Huahui 1. Samples were collected from root of HH1 and MH63 at 30-day old and were used for RNA extraction and hybridization on Affymetrix microarrays. We selected diferentially expressed genes and common significantly changed pathways hoping to as a clue to investigate unintended effects of HH1 root.
2013-10-16 | E-GEOD-46086 | biostudies-arrayexpress
Project description:DNA microarray analysis has been proved to be an effective method in investigating unintended effects in genetically modified (GM) crops. However, unintended effects of GM plants in leaves through DNA microarray analysis has many researches, but research of unintended effects of GM plants of the underground portion has few. In this study, DNA microarray analysis was used to detect DEG in underground portions between transgenic rice HH1 and its non-transgenic control MH63. We used microarrays to study unintended effects in root of transgenic rice Huahui 1.
2013-10-16 | GSE46086 | GEO
Project description:DNA microarray analysis has been proved to be an effective method in investigating unintended effects in genetically modified (GM) crops. But the distribution of differentially expressed genes in GM crops remains unclear. So the results of microarray analysis might be invalid for assessment of unintended effects if differentially expressed genes are extremely distributed. We used microarrays to study the distribution pattern of differentially expressed genes in HH1 at different developmental stages and environmental conditions. Samples were collected from both HH1 and MH63 at different developmental stages and environmental conditions and were used for RNA extraction and hybridization on Affymetrix microarrays. We selected differentially expressed genes, common differentially expressed genes and common significantly changed pathways hoping to clarify the distributions of differentially expressed genes in HH1.
2011-10-25 | E-GEOD-33204 | biostudies-arrayexpress
Project description:DNA microarray data from transgenic rice KMD and its parent XS11
| PRJNA149255 | ENA
Project description:Analyses of QTLs for expression levels (eQTLs) of the genes reveal genetic relationship between expression variation and the regulator, thus unlocking the information for identifying the regulatory network. Oligo-nucleotide expression microarrays hybridized with RNA can simultaneously provide data for molecular markers and transcript abundance. In this study, we used Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice shoots at 72 h after germination from 110 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. Totally 1,632 single feature polymorphisms (SFPs) plus 23 PCR markers were identified and placed into 601 recombinant bins, spanning 1,459 cM in length, which were used as markers to genotype the RILs. We obtained 16,372 expression traits (e-traits) each with at least one eQTL, resulting in 26,051 eQTLs in total, including both cis- and trans-eQTLs. We also identified 171 eQTL hotspots among rice genome, each of which controls transcript variations of many e-traits. Gene Ontology analysis revealed enrichment of certain functional categories of genes in some of the eQTL hotspots. In particular, eQTLs for e-traits involving DNA metabolic process was significantly enriched in several eQTL hotspots on chromosomes 3, 5 and 10. Several transcription factors colocalizing with cis-eQTLs showed significant correlations with hundreds of e-traits, indicating possible co-regulation. We also detected correlations between the QTLs for shoot dry weight and eQTLs, revealing possible candidate genes for the trait. These results provided the clues for identification and characterization of regulatory network in the whole genome at the transcriptional level. To dissect the genetic variation between the two rice indica varieties Minghui 63 and Zhenshan 97, a total of 110 RILs from Minghui 63 and Zhenshan 97 and parents were sampled. And the Affymetrix Genechip rice Genome Array was used to investigate their dynamic transcript levels. Two independent biological replicates were sampled from each RIL, and three replicates for each parent.resulting in a dataset of 226 microarrays.
2011-06-24 | E-GEOD-22564 | biostudies-arrayexpress
Project description:Analyses of QTLs for expression levels (eQTLs) of the genes reveal genetic relationship between expression variation and the regulator, thus unlocking the information for identifying the regulatory network. In this study, we used Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice flag leaf at heading date from 210 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. In the study, we attempted to construct the regulatory network by identifying putative regulators and the respective targets using an eQTL guided co-expression analysis with a recombinant inbred line population of rice.
2014-02-05 | GSE49020 | GEO
Project description:Oryza sativa strain:Minghui 63 Raw sequence reads
| PRJNA555012 | ENA
Project description:Analyses of QTLs for expression levels (eQTLs) of the genes reveal genetic relationship between expression variation and the regulator, thus unlocking the information for identifying the regulatory network. In this study, we used Affymetrix GeneChip Rice Genome Array to analyze eQTLs in rice flag leaf at heading date from 210 recombinant inbred lines (RILs) derived from a cross between Zhenshan 97 and Minghui 63. In the study, we attempted to construct the regulatory network by identifying putative regulators and the respective targets using an eQTL guided co-expression analysis with a recombinant inbred line population of rice. The ability to reveal the regulatory architecture of the genes at the whole genome level by constructing the regulatory network is critical for understanding the biological processes and developmental programs of the organism. Here we conducted an eQTL guided function-related co-expression analysis for identifying the putative regulators and constructing gene regulatory network. The Affymetrix Genechip rice Genome Array was used to investigate their dynamic transcript levels. one replicates were sampled from each RIL, three for parents, and three replicates for each parent resulting in a dataset of 216 microarrays.
2014-02-05 | E-GEOD-49020 | biostudies-arrayexpress