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336
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Dataset results
336 results for “Drought Stress”
Drought stress-mediated transcriptome profile in Populus davidiana (cv. Palgong1)
GEO Series GSE98172. Populus davidiana. 6 samples. Type: Expression profiling by high throughput sequencing.
Small RNA of sugar beet in response to drought stress
GEO Series GSE205328. Beta vulgaris. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
RNA-Seq gives insights into genetic regulation of the drought-stress response in the cassava leaves
GEO Series GSE98537. Manihot esculenta. 4 samples. Type: Expression profiling by high throughput sequencing.
RNA Seq analysis of tolerant (BPT5204) and susceptible (TN1) variety of Oryza sativa cv. Indica under drought, pathogen, and combined stress
GEO Series GSE197133. Oryza sativa. 8 samples. Type: Expression profiling by high throughput sequencing.
Expression profiling of SRGs of the drought-tolerant indica rice cultivar N22 during drought stress under field conditions
GEO Series GSE31974. Oryza sativa Indica Group; Oryza sativa. 24 samples. Type: Expression profiling by array.
Expression profiling of chickpea responses to drought, cold and high-salinity stresses
GEO Series GSE8554. Cicer arietinum; Lathyrus sativus; Lens culinaris. 8 samples. Type: Expression profiling by array.
The function of FtMYBs in regulating the synthesis of flavonoids in Fagopyrum tartaricum under drought stress
GEO Series GSE270048. Fagopyrum tataricum. 12 samples. Type: Expression profiling by high throughput sequencing.
Expression data in Arabidopsis rdr1/2/6 mutant under drought stress
GEO Series GSE37137. Arabidopsis thaliana. 24 samples. Type: Expression profiling by genome tiling array.
Drought stress-mediated transcriptome profile in Populus davidiana (cv. Palgong2)
GEO Series GSE98173. Populus davidiana. 6 samples. Type: Expression profiling by high throughput sequencing.
Drought stress-mediated transcriptome profile in Populus davidiana (cv. Seogwang9)
GEO Series GSE98175. Populus davidiana. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome profile for spikelets of rice plants subjected to severe reproductive stage drought stress
GEO Series GSE78777. Oryza sativa. 17 samples. Type: Expression profiling by array.
Internal reference gene selection under salt, alkali, and drought stresses in ecological remediation plant giant reed
GEO Series GSE190890. Arundo donax. 32 samples. Type: Expression profiling by RT-PCR.
Identification of drought stress-responsive transcription factor in ramie (Boehmeria nivea L．Gaud)
GEO Series GSE46253. Boehmeria nivea. 2 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome Analysis of Populus x canadensis in Response to Drought Stress
GEO Series GSE64044. Populus x canadensis. 12 samples. Type: Expression profiling by high throughput sequencing.
Drought stress-mediated transcriptome profile in Populus davidiana (cv. Seogwang15)
GEO Series GSE98174. Populus davidiana. 6 samples. Type: Expression profiling by high throughput sequencing.
Data from: High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar
<p>The following dataset contains the processed data presented in the article<strong> "High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar"</strong></p> <ul> <li><strong>Supplementary_methods.docx: </strong>contain detailed information for the experimental stress treatments, sequencing library preparation, sequencing and DMR calling.</li> </ul> <ul> <li><strong>BedGraph files (CpG.bed, CHG.bed, CHH.bed):</strong> contain methylation levels (%) for each cytosine in the Lombardy poplar genome, in the respective sequence context. The first three columns represent the genomic coordinates of the cytosine, the 56 following columns indicate the methylation levels for each of the samples. Missing values are represented with NA (when particular cytosines were not captured by the sequencing method). </li> <li><strong>DMR_annotation_Populus_nigra_Italica_after_biotic_and_abiotic_treatments.txt: </strong>contains all the identified regions that showed significant stress-induced differential methylation (DMR). The file include all annotation for each single DMR: genomic location, genomic feature, gene, TE, sequence context and stress treatment, besides other specific relevant information.</li> <li><strong>sample_IDs_basic_metadata.txt</strong>: contains the sample ID and the associated metadata (stress treatment and ortet location and ID) for all samples used in the analysis.</li> <li><strong>Supplementary_file_1_metadata_samples.xlsx: </strong>contains the metadata associated to each sample including: sequencing statistics before and after quality and adapter trimming, read mapping and coverage statistics, and number of interrogated cytosines on each sequence context (CpG, CHG, CHH).</li> <li><strong>Supplementary_file_2_GO_enrichments.xlsx: </strong>contains the complete results for the GO enrichment analysis for different gene datasets associated to: drought-CHH-DMRs, SINEs, MITEs, SINEs + MITEs.</li> <li><strong>italica_denovo_TE_280920.gff</strong>: contains the predicted TEs using the following methodology. First, TEs were de-novo annotated using the Extensive de-novo TE Annotator (EDTA) (version 1.8.3) (https://github.com/oushujun/EDTA) with default parameters, except for option --sensitive: 1, which uses RepeatModeler (version 2.0.1) to identify remaining TEs. All the steps in EDTA pipeline were selected (filter, final and anno) in order to perform whole-genome annotation/analysis after the TE library was constructed. Then, in the annotated library from EDTA, we merged overlapping fragments and fragments located at a close distance (<10bp) in a strand wise manner. The merged fragment was annotated as the family of longer merged fragment. Structural variants derived from nanopore data were used to redefine the boundaries of overlapping TE fragments to be more precise with actual predictions. LINE elements were identified independently by RepeatModeler in order to construct a more comprehensive de-novo TE library.</li> <li><strong>SaliS.fasta:</strong> contains the consensus sequences of <strong>Sali</strong>caceae <strong>S</strong>INE families (SaliS), the file was built by extracting information from the supplementary table 2 of the publication: "Divergence of 3′ ends as a driver of short interspersed nuclear element (SINE) evolution in the Salicaceae" (https://doi.org/10.1111/tpj.14721)</li> <li><strong>Pnigra_Italica_SaliS.bed: </strong>the file contains the annotated SaliS found by blastn over the P. nigra Italica reference genome (-qcov_hsp_perc 90 -perc_identity 70 -word_size 7). Column headers: chr, start, end, length, strand, perc_identity, SaliS family.</li> <li><strong>Pnigra_Italica_all_TEs_for_anno.bed: </strong>contains the merged information from <strong>italica_denovo_TE_280920.gff </strong>and<strong> Pnigra_Italica_SaliS.bed.</strong> Column headers: chr, start, end, length, strand, perc_identity (only for SaliS), TE superfamily.</li> <li><strong>CXX_ortet_DMRs_merged.bed</strong>: contains DMRs merged from all pairwise DMR callings between two ortets. One file per context. Column headers: chr, start, end, number of comparisons where the DMR occur, avg number of cytosines (when called in multiple DMR callings), avg differential methylation vs. control (when called in multiple DMR callings), avg adjusted p value (when called in multiple DMR callings), avg DMR length (when called in multiple DMR callings).</li> </ul> <p><strong>SCRIPTS</strong></p> <ul> <li><strong>cov_filtering.sh:</strong> to filter individual positions according to a custom threshold.</li> <li><strong>unionbedg_with_NAs.sh:</strong> to merge information from different samples in a single file taking into account the percentage of missing values per position across the given samples.</li> <li><strong>anovas_and_contrasts_boxplots_barplots_cld.r</strong>: to perform statistical tests for the effect of treatments and ortets on the average global methylation. Each sequence context was analyzed separately.</li> <li><strong>CHH_noise_filter.sh:</strong> to remove cytosines with invariable methylation values across 90% of the samples.</li> <li><strong>GlobalMethAvg_calculation.r</strong>: to calculate global average methylation given a methylation file (CpG.bed, CHG/bed or CHH.bed) and sample file.</li> <li><strong>Hclustering_and_PCAs_analysis.r:</strong> to perform hierarchical clustering, principal component analysis and plot the respective figures.</li> <li><strong>ICC_matrices_analysis.r:</strong> to calculate intraclass correlation coefficients among all pairwise combinations and plot colored grids</li> </ul> <p> </p> <p>Annotations are based on the de novo reference genome of the Populus nigra cv. Italica clone uploaded in the ENA project: PRJEB44889 (<a href="http://www.ebi.ac.uk/ena/browser/view/GCA_950102115">www.ebi.ac.uk/ena/browser/view/GCA_950102115</a>). Bisulfite sequencing data can be found under the ENA project: PRJEB51831</p>
Arbuscular mycorrhizal fungi and carrot demonstrate contrasting responses to drought stress as revealed through a dual transcriptomic approach
GEO Series GSE189806. Rhizophagus irregularis; Daucus carota. 16 samples. Type: Expression profiling by high throughput sequencing.
Series-temporal transcriptome profiling of cotton reveals the response mechanism of phosphatidylinositol signaling system in the early stage of drought stress
GEO Series GSE211298. Gossypium hirsutum. 45 samples. Type: Expression profiling by high throughput sequencing.
Global gene expression analysis of the OsDIS1 overexpression plants under normal and drought stress conditions
GEO Series GSE30151. Oryza sativa. 4 samples. Type: Expression profiling by array.
Gene Expression and Physiological Differences in Neo-Octoploid Switchgrass Subjected to Drought Stress
GEO Series GSE132772. Panicum virgatum. 40 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.