Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12799713_1.fastq.gz | Fastqsanger.gz | |||
| SRR12799713_2.fastq.gz | Fastqsanger.gz | |||
| SRR12799714_1.fastq.gz | Fastqsanger.gz | |||
| SRR12799714_2.fastq.gz | Fastqsanger.gz | |||
| SRR12799715_1.fastq.gz | Fastqsanger.gz |
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Project description:In this study, we used the illumina high throughput sequencing approach (Sequencing-By-Synthesis, or SBS) to develop the sequence resource of black pepper. To identify micro RNAs functioning in stress response of the black pepper plant, small RNA libraries were prepared from the leaf and root of Phytophthora capsici infected plants, leaves from drought stressed and control plants.
2017-08-24 | GSE65782 | GEO
Project description:Seven different Solanaceae species, Potato (Solanum tubersosum), Tomato (Solanum lycopersicum), Eggplant (Solanum melongena), Pepper (Capsicum annuum), Tobacco (Nicotiana tabaccum), Petunia and Nicotiana benthiamana were subjected to drought stress. Drought stress was applied by stopping watering of the plants, control plants were normally watered with nutrient solution. Samples were collected at 0, 1, 3, 5, 7 and 10 days after the first application of the drought stress. RNA was isolated using Qiagen RNeasy. Keywords: Direct comparison
2007-09-01 | GSE8161 | GEO
Project description:Drought is one of the major factor that limits crop production and reduces yield. To understand the early response of plants under nearly natural conditions, pepper plants were grown in a greenhouse and drought stressed by withholding water for one week. Plants adapted to the decreasing water content of the substrate by adjustment of their osmotic potential in roots by accumulation of raffinose, glucose, galactinol and proline. In contrast in leaves levels of fructose, sucrose and also galactinol increased. Due to the water deficit cadaverine, putrescine, spermidine and spermine accumulated in leaves whereas the concentration of polyamines was reduced in roots. These polyamines are suggested to rather act as stress protectants than for osmotic adjustment. To understand the molecular basis of the response to this early drought stress better, four suppression subtractive hybridisation libraries from leaves and roots were constructed. Microarray technique was used to identify differentially expressed genes. A total of 109 unique ESTs were detected. The diversity of the putative functions of all identified genes confirms the complexity of the plant response to drought stress. Keywords: Transcription profiling Two-condition experiment in roots and leaves, control leaves (CL) vs. drought-stressed leaves (DL) and control roots (CR) vs. drought-stressed roots (DR). Biological replicates: 4 control (1-4), drought-stressed (1-4), independently grown and harvested. One swap replicate per array.
2010-06-20 | E-GEOD-18627 | biostudies-arrayexpress
Project description:Drought is one of the major factor that limits crop production and reduces yield. To understand the early response of plants under nearly natural conditions, pepper plants were grown in a greenhouse and drought stressed by withholding water for one week. Plants adapted to the decreasing water content of the substrate by adjustment of their osmotic potential in roots by accumulation of raffinose, glucose, galactinol and proline. In contrast in leaves levels of fructose, sucrose and also galactinol increased. Due to the water deficit cadaverine, putrescine, spermidine and spermine accumulated in leaves whereas the concentration of polyamines was reduced in roots. These polyamines are suggested to rather act as stress protectants than for osmotic adjustment. To understand the molecular basis of the response to this early drought stress better, four suppression subtractive hybridisation libraries from leaves and roots were constructed. Microarray technique was used to identify differentially expressed genes. A total of 109 unique ESTs were detected. The diversity of the putative functions of all identified genes confirms the complexity of the plant response to drought stress. Keywords: Transcription profiling
2009-10-28 | GSE18627 | GEO
Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
2025-07-30 | GSE288713 | GEO
Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
2025-07-30 | GSE288615 | GEO
Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
2025-07-30 | GSE288412 | GEO
Project description:We have characterized the changes in miRNA expression profiles in rice leaves under drought stress and As stress and compared these to unstressed leaves. 10 pairs of drought responsive and 8 pairs of As responsive miRNA-gene were identified and validated by qRT-PCR. This study identifies putative specific miRNA-mRNA regulatory modules with roles during drought and As stress. Putative microRNAs identified in this study are involved in hormone signaling, lipid and carbohydrate metabolism, and antioxidant defence. The results of this study will assist in elucidating the role of miRNAs in post-transcriptional regulation of target genes during abiotic stress and may contribute to the development of strategies to engineer drought and heavy metal resistance.
2017-05-20 | GSE99083 | GEO