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TranscriptomicsA global analysis of QTLs for expression variations in rice flag leaf at heading date
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 le...
ORGANISM(S): Oryza sativa 
2014-02-05 | E-GEOD-49020 | biostudies-arrayexpress
UnknownExtensive sequence divergence between the reference genomes of two elite indica rice varieties Zhenshan 97 and Minghui 63.
Not available
S-EPMC5024649 | biostudies-literature
ProteomicsComparative proteomics analysis of superior and inferior spikelets in hybrid rice during grain filling and response of inferior spikelets to drought stress using isobaric tags for relative and absolute quantification
The biological functions of differently expressed proteins between superior and inferior spikelet grains were investigated based on the isobaric tags for relative and absolute quantification to further clarify the mechanism of rice grain filling at the proteomic level, as well as the response of inf...
ORGANISM(S): Oryza sativa (Rice) 
2015-08-05 | PXD001046 | Pride
Hybrid rice  grain filling  drought stress  itraq  proteome 
TranscriptomicsDissecting the developmental transcriptomes of rice
Transcriptional programs are important for the development of complex eukaryotic organisms. Suites of genes expressed with temporal and spatial controls by regulatory networks in response to environmental cues are the cornerstone for achieving the specification of morphology and physiology of the ti...
ORGANISM(S): Oryza sativa Indica Group 
2010-01-20 | E-GEOD-19024 | biostudies-arrayexpress
UnknownIdentification of QTL under Brassinosteroid-Combined Cold Treatment at Seedling Stage in Rice Using Genotyping-by-Sequencing (GBS).
Not available
S-EPMC9460439 | biostudies-literature
ProteomicsIdentification of key factors in cartilage tissue during the progression of osteoarthritis using a non-targeted metabolomics strategy
This work used non-targeted metabolomics and screened out differential metabolites and metabolic pathways, providing a reliable theoretical basis for further study of specific markers and their specific pathways in the progression of OA.
ORGANISM(S): Homo Sapiens 
2023-04-02 | PXD041287 |
UnknownBuilding two indica rice reference genomes with PacBio long-read and Illumina paired-end sequencing data.
Not available
S-EPMC5020871 | biostudies-literature
UnknownRibosome profiling reveals the translational landscape and allele-specific translational efficiency in rice.
Not available
S-EPMC10030323 | biostudies-literature
UnknownGlobal genomic diversity of Oryza sativa varieties revealed by comparative physical mapping.
Not available
S-EPMC3982694 | biostudies-literature
Genomicsena-DATASET-BIOPIC-16-05-2017-14:34:15:534-498 - samples
T cells isolated from peripheral blood, tumors and adjacent normal tissues from six hepatocellular carcinoma patients. SmartSeq2 and Tang2009 protocol were used to amplify RNA from single T cells. High depth enables simultaneously expression profiling and TCR assembling.
EGAD00001003337 | EGA
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