Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
237
datasets available to search
ShareScore release 0.9.0
Dataset results
237 results for “Sensitivity analysis”
Epigenome-wide analysis identifies pollution-sensitive loci in fibrotic interstitial lung disease
GEO Series GSE289816. Homo sapiens. 479 samples. Type: Methylation profiling by genome tiling array.
Integrative analysis in WGBS and transcriptome profiles of an temperature sensitive etiolation tea plant cultivar ‘Zhonghuang 2’
GEO Series GSE234849. Camellia sinensis. 6 samples. Type: Expression profiling by high throughput sequencing.
Dataset for water-sensitive paper analysis
<p>The file "representation.jpg" shows the water-sensitive papers (WSP) placement. In each collection 8 WSP are placed: 2 in a place without canopy (Z1 and Z2); 3 in different positions of the canopy (one at the end closest to the sprayer (A1), one in the middle (A2) and another at the end furthest from the sprayer (A3); and another 3 placed in another vineyard in the same way as above.</p>
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>
Gene expression analysis in sensitive and tolerant Citrus rootstocks under chlorotic conditions
GEO Series GSE38667. Citrus x paradisi x Citrus trifoliata; Citrus sinensis x Citrus trifoliata; Citrus. 12 samples. Type: Expression profiling by array.
Digital-scRRBS: A cost-effective, highly sensitive and automated single-cell methylome analysis platform via digital microfluidics
GEO Series GSE214106. Homo sapiens. 45 samples. Type: Methylation profiling by high throughput sequencing.
Analysis of the impact of FOXA1 expression on MDA::ER E2-sensitive transcriptomes and ER cistromes [ChIP-Seq data]
GEO Series GSE55740. Homo sapiens. 19 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Highly sensitive MLH1 methylation analysis in blood allows the identification of low-level epigenetic mosaicism
GEO Series GSE131541. Homo sapiens. 2 samples. Type: Methylation profiling by array.
Global gene expression analysis between cetuximab-sensitive and cetuximab-resistant colorectal cancer cells
GEO Series GSE71210. Homo sapiens. 2 samples. Type: Expression profiling by array.
The use of non-amplified RNA samples for gene expression analysis by sensitive microarray.
GEO Series GSE30945. Homo sapiens. 31 samples. Type: Expression profiling by array.
Transcriptome analysis of Cymelarsan resistant and Cymelarsan sensitive strains of Trypanosoma brucei.
GEO Series GSE23288. Trypanosoma brucei brucei. 4 samples. Type: Expression profiling by high throughput sequencing.
Analysis of the impact of FOXA1 expression on MDA::ER E2-sensitive transcriptomes and ER cistromes
GEO Series GSE55741. Homo sapiens. 31 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Analysis of the impact of FOXA1 expression on MDA::ER E2-sensitive transcriptomes and ER cistromes [Transcriptome data]
GEO Series GSE55680. Homo sapiens. 12 samples. Type: Expression profiling by array.
Prediction of sensitivity of rectal cancer cells in response to preoperative chemoradiotherapy by DNA microarray analysis of gene expression profiles.
GEO Series GSE35452. Homo sapiens. 46 samples. Type: Expression profiling by array.
RNAseq analysis of treatment-resistant and sensitive breast cancer patient-derived organoids with paclitaxel treatments
GEO Series GSE183477. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Expression analysis of radio-resistant and -sensitive head and neck cancer cells
GEO Series GSE217645. Homo sapiens. 33 samples. Type: Expression profiling by high throughput sequencing.
PWB Bone SR - Supplementary Table S4 (Sensitivity Analysis)
<p>Supplementary material for the manuscript entitled 'Bone Deconditioning During Partial Weight-Bearing in Rodents – A Systematic Review and Meta-Analysis'</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.