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DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing
23,842 datasets
24 KB of bioinformatic and experimental validation data supporting a THBS1-CD47 signaling axis in intracranial aneurysms. The dataset likely contains results from bulk transcriptomic (GSE54083) and single-cell RNA-seq (GSE193533) analyses, weighted gene co-expression network analysis (WGCNA), and CellChat predictions. Author Jianhuang Huang published the data on figshare under a CC-BY-4.0 license in April 2026.
Data Sheet 1_Transcriptome analysis reveals DNA repair–related clues associated with divergent leaf nuclear DNA diversity in Leymus chinensis.pdf is a 481.1 KB PDF file published by Haoyang Yu on figshare in April 2026. It contains transcriptomic data from a study comparing a wild population and a cultivated cultivar of Leymus chinensis, identifying 3,833 differentially expressed genes enriched in DNA repair pathways.
90 neurotoxicity-related protein targets were identified for the plasticizer DOTP using network toxicology and molecular docking. The dataset contains results from computational modeling and in vivo experiments on mice, including behavioral and molecular assays. Author Hao Tang published the data under a CC-BY-4.0 license on figshare in April 2026.
Data Sheet 1 by Hao Tang, last updated April 10, 2026, details a study on the neurotoxicity mechanisms of the plasticizer dioctyl terephthalate (DOTP). The 1.4 MB document integrates computational network toxicology, molecular docking simulations, and in vivo mouse experiments to identify 90 potential protein targets and core pathways. Results include identified core targets like EGFR and BCL2, molecular docking affinities, and phenotypic validation from behavioral and histological assays.
A 988.8 KB Excel dataset uploaded by Francesca Di Rico on April 10, 2026. It presents integrative genomic and transcriptomic analyses comparing the strongly antagonistic Pseudomonas sp. PF05 strain with a weakly antagonistic relative, Pseudomonas frederiksbergensis PF4.89, to uncover the molecular basis of biocontrol against the fungal pathogen Fusarium oxysporum.
An Excel dataset from figshare by Francesca Di Rico, last updated April 10, 2026, containing integrative genomic and transcriptomic analyses of two Pseudomonas strains. The data compares the strongly antagonistic Pseudomonas sp. PF05 with the weakly antagonistic Pseudomonas frederiksbergensis PF4.89 in their interaction with the fungal pathogen Fusarium oxysporum. The 2.0 MB file presents findings on genomic potential and transcriptional regulation underlying microbial biocontrol activity.
An integrative genomic and transcriptomic dataset compares the biocontrol activity of Pseudomonas sp. PF05 against Fusarium oxysporum with a related, weakly antagonistic strain. The data, created by Francesca Di Rico and last updated in April 2026, includes analyses of secondary metabolite biosynthesis genes and transcriptional responses during fungal interaction. It is a 172.3 KB Excel file shared under a CC-BY-4.0 license.
Pseudomonas sp. PF05 exhibits strong antagonism against Fusarium oxysporum, a key soil-borne fungal pathogen. Francesca Di Rico's 2026 study compares PF05's genomic and transcriptomic profiles with the weakly antagonistic strain PF4.89 to uncover biocontrol mechanisms. The dataset likely contains results from comparative genome and transcriptome analyses, revealing differences in secondary metabolite genes and adaptive stress responses.
Pseudomonas sp. PF05 genomic and transcriptomic analyses reveal its biocontrol strategy against Fusarium oxysporum is based on metabolic reprogramming and stress tolerance, not just antifungal gene expression. The dataset, authored by Francesca Di Rico and updated in April 2026, compares this strain with a related, less effective one to uncover key molecular determinants. It is a 772.7 KB DOCX file containing integrated analysis results from comparative genomics and transcriptomic profiling.
An eight-gene regulatory B cell-related prognostic signature for head and neck squamous cell carcinoma (HNSCC) was developed by Junyan He and published on figshare in April 2026. The model was constructed using data from The Cancer Genome Atlas (TCGA) and validated with cohorts from TCGA and the Gene Expression Omnibus (GEO). It links gene expression to patient survival, tumor mutation burden, immune cell infiltration, and potential drug sensitivity.
An eight-gene prognostic signature derived from regulatory B cell-related genes for head and neck squamous cell carcinoma (HNSCC). The signature was developed using data from The Cancer Genome Atlas (TCGA)-HNSCC cohort and validated with additional TCGA and Gene Expression Omnibus (GEO) cohorts. The dataset, authored by Junyan He and last updated in April 2026, is a 21.5 KB document file.
An eight-gene prognostic signature derived from TCGA-HNSCC and GEO cohorts predicts survival and immune landscape in head and neck squamous cell carcinoma. The dataset, published by Junyan He on figshare in April 2026, includes analyses linking the signature to tumor mutation burden, immune checkpoint expression, and drug sensitivity. The underlying data is likely tabular, containing gene expression and associated clinical or analytical results.
An eight-gene prognostic signature derived from TCGA and GEO cohorts predicts survival and immune landscape in head and neck squamous cell carcinoma. The signature, created by Junyan He and last updated in April 2026, is linked to tumor mutation burden, immune checkpoint expression, and drug sensitivity. Functional analyses identified OLR1 as a key oncogenic gene associated with immune evasion in this cancer type.
A study published in 2026 by Junyan He presents an eight-gene regulatory B cell-related prognostic signature derived from TCGA-HNSCC and GEO cohorts. The dataset, shared on figshare under a CC-BY-4.0 license, is used to predict survival, immune cell infiltration, and drug sensitivity in head and neck squamous cell carcinoma. The analysis includes associations with clinicopathological features, tumor mutation burden, and immune checkpoint expression.
Replication data supports research on China's internal information control strategies. The dataset is hosted by Harvard Dataverse and was last updated on June 30, 2026. It was authored by Shengqiao Lin.
A dataset from a 2026 study investigating the role of neddylation in stabilizing Fibroblast growth factor receptor 1 (FGFR1) in breast cancer. It includes results from DIA quantitative proteomics screening, protein stability assays, and downstream signaling analysis following neddylation inhibition. The data was authored by Junfang Qin and shared under a CC-BY-4.0 license.
Data Sheet 1_PhIP-driven prostate cancer involves key molecular regulators and immune microenvironment modulation.docx is a 1.6 MB document authored by Yaoan Wen and last updated on April 10, 2026. It details a study identifying 17 candidate genes associated with prostate cancer induced by the chemical PhIP, with SLC14A1 highlighted as a dominant contributor. The research integrates network toxicology, bioinformatics, and machine learning, validated by molecular simulations and experiments.
Petra University provides a dataset on moisture measurement in building materials. The data is intended to address problems that can have ruinous structural effects and impact occupant health. The dataset is listed as Open Access on the paperswithcode platform.
Additional file 1 from a multi-omics study on chicken follicle selection, containing 11 Excel tables with primer sequences, alignment statistics, peak counts, and differential gene expression results. Dandan Li published this 6.6 MB dataset under a CC-BY-4.0 license on figshare in April 2026. The tables likely contain results from integrated ATAC-seq and mRNA-seq analyses performed on granulosa cells.
Supplementary Material 11 provides a schematic representation of NK-cluster gene orthologs and paralogs across 17 panarthropod species, including Drosophila melanogaster and Tribolium castaneum. The data, created by Ralf Janssen, is an Excel file (10.6 KB) last updated on 2026-04-17. It visualizes gene presence, absence, and duplications, with a focus on species within the Arachnopulmonata group.