Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Transcriptomic and spatial datasets of human ex vivo right atrial tissue in ischemic heart disease and heart failure

This dataset contains raw counts and processed data and annotations for our transcriptomic and spatial dissection of human ex vivo right atrial tissue in ischemic heart disease and heart failure.   snRNA.zip contains the snRNA-seq dataset for heart right atrial appendade and pericardial fluid. VISIUM.zip contains the Visium spatial transcriptomics data Molecular cartography.zip contains the Resolve Biosciences molecular cartography spatial transcriptomics data

Fig. 4 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 4. Transcript accumulation of genes associated with wound induced suberization. Transcript accumulation of known and putative genes encoding steps in suberin biosynthesis, from starch degradation to final assembly, over the wound-healing time course were retrieved from RNA-seq data. Heatmaps depict log2FPKM means for n = 3 biological replicates for each time point. Numbered pathway steps correspond to numbers in the suberin roadmap (Supplemental Fig. S4). Fumarase (step 63) is included as a step in the TCA pathway, but is shown in grey because its sequence did not have a corresponding PGSC gene identification number, and therefore transcript abundance could not be estimated in this study.

Fig. 6 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 6. Transcript accumulation of wound-induced CASP and GDSL genes. Transcript accumulation of known and putative CASP and GDSL genes, over the wound-healing time course were retrieved from RNA-seq data. Heatmaps depict log2FPKM means for n = 3 biological replicates for each time point.

Fig. 3. Network modules for suberin-associated metabolism genes. Expression profiles for 317 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 3. Network modules for suberin-associated metabolism genes. Expression profiles for 317 wound-induced and suberin-associated genes encompassing primary carbohydrate metabolism and the formation of suberin phenolic and aliphatic monomers were subjected to WGCNA. Genes belonging to carbohydrate (C), tricarboxylic acid cycle (TCA), shikimate pathway (S), phenolic metabolism (P), phenolic assembly (PA), fatty acid biosynthesis (FAB), fatty acid modification (FAM) and aliphatic assembly (AA) are colour-coded (see legend). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 2. Gene set enrichment analysis (GSEA) of biological processes across differentially expressed genes (DEGs). Time point comparison panels represent a union parametric analysis of gene set enrichment (PAGE) of biological process (BP) categorized gene ontology (GO) terms. Nodes represent gene sets and their size represents a range from 5 to 464 genes, and edges show overlapping genes between sets, with width representing ranges from 5 to 149 genes. Blue sets are downregulated, red are up-regulated, and grey nodes denote terms that were not detected as significantly differentially regulated (i.e. enriched) at that time point comparison. Labels denote assigned node numbers that correspond to Table 1 with associated GO ID, GO term and regulation overview. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 1. Global overview of the wound-healing transcriptome. A. Principle component analysis (PCA) of RNA-seq libraries. Colours represent biological replicate libraries generated from the same time point (gene log2FPKM space with scaling). B. Differentially expressed genes (DEGs) across time point comparisons. Genes were considered significantly up- or down-regulated if they met p ≤ 0.01 and |log2 (fold change)| (| LFC|) ≥ 2 significance cut-offs. Lists of significantly DEGs were generated using voom by applying these parameters with the Benjamini-Hochberg procedure to TMM-normalized HT-Seq count data. C. Venn diagram of DEGs significantly up- (red) or down-regulated (blue) over the wound-healing time course. Genes were considered significantly up- or down-regulated if they met p ≤ 0.01 and |LFC| ≥ 2 significance cut-offs. Lists of significantly DEGs were generated using voom by applying these parameters with the Benjamini-Hochberg procedure to TMM-normalized HT-Seq count data. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 5. Transcript accumulation of genes associated with wound induced ABA biosynthesis and degradation. Transcript accumulation of known and putative genes encoding steps in ABA biosynthesis and degradation, over the wound-healing time course were retrieved from RNA-seq data. Heatmaps depict log2FPKM means for n = 3 biological replicates for each time point. Numbered pathway steps correspond to numbers in the ABA metabolism pathway (Supplemental Fig. 6).

Fig. 7 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes

Fig. 7. RT-qPCR validation of select wound-induced genes. Gene expression values for 14 genes from RT-qPCR (ΔΔCq) and RNA-seq (CPM) analyses were normalized to 0 dpw values, then log2-transformed to generate log2 (foldchange) values. Pearson's correlation coefficients were calculated for log2 (foldchange) values from the two experimental procedures, with α = 0.05 (Supplemental Table 9). The 95% confidence interval was calculated and plotted as 95% confidence bands.

opencc-by-4.0Mar 2024View details →

Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"

These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics". These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.

opencc-by-4.0Feb 2024View details →

TIdeS: a comprehensive framework for accurate open reading frame identification and classification in eukaryotic transcriptomes

Includes: <ul> <li>ORF predictions for 28 diverse eukaryotic taxa, employing common tools/approaches</li> <li>Raw transcriptome assemblies used for ORF predictions</li> <li>Phylogenetic trees used as the basis for contamination identification, as well as validating TIdeS's efficacy</li> <li>Various TIdeS outputs (ORF prediction/classification)</li> <li>Copy of current TIdeS source code</li> </ul>

opencc-by-4.0Apr 2024View details →

Transcriptome analysis of immune-inflammatory regulation in Tremella Fuciformis-derived Polysaccharide reeducated B16 cells subcutaneous model

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →

Transcriptomics characterization of Psoriaris, Atopic dermatitis and Pityriasis Rubra Pilaris

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

Lemonade Creek, Yellowstone National Park, USA - Microbial Community Analysis - Genome and Transcriptome Data

Genome and Transcriptome data used for analysis of microbial community function over a diurnal cycle in Lemonade Creek, Yellowstone National Park, USA.   <code>mags.tar</code>  Non-redundant metagenome data (genome assemblies, predicted genes, and gene functional annotations).   In each directory are the the following files: - <code>*.mRNA.faa</code> protein sequences of protein-coding genes - <code>*.mRNA.fna</code> nucleotide sequences of protein-coding genes - <code>*.mRNA.gff3</code> genomic location of protein-coding genes - <code>*.mRNA.emapper.tsv</code> eggNOG-mapper annotations for the protein-coding genes - <code>*.mRNA.interproscan.gff3</code> InterProScan annotations for the protein-coding genes   In the <code>prokaryote</code> directory there are the following files: - <code>*.rRNA.fna</code> nucleotide sequences of rRNA genes - <code>*.rRNA.gff3</code> genomic location of rRNA genes - <code>*.tRNA.fna</code> nucleotide sequences of tRNA genes - <code>*.tRNA.gff3</code> genomic location of tRNA genes - <code>*.other.fna</code> nucleotide sequences of other genes (i.e., CRISPR, ncRNA, oriC, regulatory_region, repeat_region, tmRNA - if any were predicted) - <code>*.other.gff3</code> genomic location of other genes   Eukaryotes Five MAGs from other eukaryotes that were assembled from a coassembly of the Soil samples.   Prokaryotes The final dereplicated prokaryote MAGs (at 95% ID). The two <code>*stats*</code> files list the taxonomic information (from <code>GTDB-Tk</code>), completeness (from <code>CheckM</code>), and assembly stats (from the <code>stats.sh</code> script from the <code>bbmap</code> package) for each of the prokaryotic MAGs + the number of predicted protein-coding and non-protein-coding genes predicted in each MAG.   Viruses The final dereplicated viral MAGs and vOTUs.       <code>read_mapping.tar</code> Abundance results from metagenome and metatranscriptome read mapping analysis against the non-redundant metagenome data and predicted genes (respectively). This analysis includes the cyanidiophyceae reference nuclear and organelle genomes.   mags Results from <code>bbmaps</code> alignment of metagenome reads against a database of non-redudant metagenome MAGs + cyanidiophyceae reference nuclear and organelle genomes. <code>CoverM</code> was used to calculate MAG abundances.   genes <code>Salmon</code> abundance quantification of PolyA and RiboMinus metatranscriptome reads mapped against the predicted genes in the non-redudant metagenome MAGs + cyanidiophyceae reference nuclear and organelle genomes.

Transcriptomic neuron types vary topographically in function and morphology. Shainer, Kappel et al.

Data files and analysis code for Shainer, Kappel et al. are provided. Each zip file includes the relevant data, R or Python code, and session/environment information necessary to reproduce the manuscript's results and figures.

opencc-by-4.0Sep 2024View details →

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics (output data)

This contains the output results of CalicoST (inferred CNAs and cancer clones), results of comparison methods, and CNAs inferred from WES data of 13 samples across four cancer types. In this updated version, we also included the simulated data and the results from CalicoST and other methods in CalicoST_simulation_deposit.zip. README contains the details of deposited files.

Scripts and data for the manuscript "Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon"

This dataset supports the manuscript titled "Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon." It contains comprehensive RNA-seq count data from gill biopsies of approximately 3000 Atlantic salmon smolt, collected during the SynchroSmolt project. The data is supplemented with RNA-seq counts from two prior photoperiod smolt experiments (2013_shortdays and 2017_winterlength) and single-nucleus RNA-seq (snRNA-seq) data from an additional experiment. Key Dataset Elements: - RNA-seq read counts and metadata for three experiments, detailing various growth, condition, and survival indicators. - Scripts for analysis include differential expression analysis, random forest model preparation and execution, and cell-type-specific gene analysis. - Intermediate data outputs such as normalized RNA-seq counts, results from differential expression analyses, and random forest model inputs and outputs. This dataset facilitates the exploration of gene expression-based predictive modeling for seawater survival, revealing key insights into the influence of photoperiod history and developmental gene regulation on Atlantic salmon's transition to seawater.

opencc-by-4.0Oct 2024View details →

Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics: fastq & bam files - Sample S5_Rec

You can find here the fastq and bam files related to the datasets used in the publication:  In this particular upload, you can find the fastq (version1) and bam (version2) files of the two replicates of sample S5_Rec (A121573) Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4

Supplementary tables for chapter 3: "Haplotype-resolved transcriptomics defines the inheritance and genetic architecture of response to Citrus Greening Disease"

Dissertation chapter 3: "Haplotype-resolved transcriptomics defines the inheritance and genetic architecture of response to Citrus Greening Disease"

opencc-by-4.0Feb 2024View details →

Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity

We acquired 10x Visium spatial transcriptomics (ST) data from 9 patients with invasive adenocarcinomas [1–5] to explore the role of the tumour microenvironment (TME) on intratumor heterogeneity (ITH) and drug response in breast cancer. By leveraging a new version of Beyondcell [6] (<a href="https://github.com/cnio-bu/beyondcell" target="_blank" rel="noopener">cnio-bu/beyondcell</a>), a tool for identifying tumour cell subpopulations with distinct drug response patterns, we predicted sensitivity to over 1,200 drugs while accounting for the spatial context and interaction between the tumour and TME compartments. Moreover, we also used Beyondcell to compute spot-wise functional enrichment scores and identify niche-specific biological functions. Here, you can find: In signatures folder: <ul> <li>SSc breast: Collection of gene signatures used to predict sensitivity to > 1,200 drugs derived from breast cancer cell lines.</li> <li>Functional signatures: Collection of gene signatures used to compute enrichment in different biological pathways.</li> </ul> In visium folder: <ul> <li>Visium objects: Processed ST Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [7]. These objects, together with the signatures, were used to compute the Beyondcell objects.</li> </ul> In single-cell folder: <ul> <li>Single-cell objects: Raw and filtered merged single-cell RNA-seq (scRNA-seq) Seurat objects with unnormalised counts used as a reference for spot deconvolution.</li> </ul> In beyondcell folder: <ul> <li>Beyondcell sensitivity objects with prediction scores for all drug response signatures in SSc breast.</li> <li>Beyondcell functional objects with enrichment scores for all functional signatures.</li> </ul>

Spatially resolved transcriptomics of benign and malignant peripheral nerve sheath tumors

Background: Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells. Here, we aimed at determining the spatial and biological heterogeneity of PNSTs. Methods: We performed spatial transcriptomics on formalin-fixed paraffin-embedded diseased peripheral nerve tissue. We used spatial clustering and weighted correlation network analysis to construct niche-similarity networks and gene expression modules. We determined differential expression in primary pathologies, analysed pathways to investigate the biological significance of identified meta-signatures, integrated the transcriptional data with histological features and existing single cell data, and validated expression data by immunohistochemistry. Results: We identified distinct transcriptional signatures differentiating PNSTs. We observed spatial transcriptional heterogeneity within hybrid PNSTs (HPNSTs) and immune cells preferentially infiltrating the neurofibroma component. S100b and Vimentin were validated as markers for schwannomas and schwannoma components of HPNSTs, while APOD highlights neurofibroma components of HPNSTs. Furthermore, we mapped cells with different differentiation states, including Schwann cell precursors, neural crest-like cells and those with mesenchymal transition in MPNST in space. Conclusions: This pilot study shows that spatial transcriptomics can be applied to PNSTs to gain insight into their biology. It helps establishing new markers, provides spatial information about cellular composition and distribution of cellular differentiation states. Hence, it is a powerful tool for integrating morphological and high-dimensional molecular data with the potential to facilitate PNSTs classification in the future.