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25,372 results for “Transcriptomics”
Transcriptomic analysis of CTC at different timepoints
<p>This repository contains processed transcriptomics data, large data sets and additional files related to the Diamantopoulou et al. (2022).</p>
Aliarcobacter butzleri gene annotation and transcriptome data
<p>Genomes assembled sequences, functional annotation files (Prokka), logFC table of 3 A. butzleri strains isolated from human (LMG 10828<sup>T</sup>, LMG 11119, 31).</p>
Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging
<p>MELC (Multi-epitope ligand cartography) multiplex imaging data of our neuroblastoma cohort supporting the publication " Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging". The zip folders contain raw image data of one to four fields of view (FoV). The folder "RoI" contains the masks of user-selected regions. "marker_status.csv" is used for normalization with RESTORE. "MELC_single_cell_data.csv" contains the normalized single-cell data with cell type assignments.</p>
Transcriptome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources Created Jun 9, 2022 12:06:22 PM, modified Jun 9, 2022 12:07:57 PM
<p>Transcriptome analysis of S. uvarum grown on synthetic wine must with different nitrogen sources: Ammonium, Phenylalanine, Asparagine, or Methionine. This is a dataset for the thesis of Angela Coral (Autumn 2022) , which can be accessed at www.ucc.ie. The work will also be submitted for publication and this will be a supplementary data file.</p>
Transcriptomic analysis of deceptively pollinated Arum maculatum (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction
<p>A compressed folder containing the R script and input files required to replicate the results in our manuscript entitled "Transcriptomic analysis of deceptively pollinated <em>Arum maculatum</em> (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction".</p> <p>Note: Raw Illumina sequencing files associated with this study have been uploaded to NCBI SRA, under the BioProject accession PRJNA856436.</p> <p><strong>ABSTRACT</strong></p> <p>Deceptive pollination often involves volatile organic compound (VOC) emissions that mislead insects into performing non-rewarding pollination. Among deceptively pollinated plants, <em>Arum maculatum</em> is particularly well-known for its potent dung-like VOC emissions and specialized floral chamber, which traps pollinators – mainly <em>Psychoda phalaenoides</em>and <em>P. grisescens</em> – overnight. However, little is known about the genes underlying the production of many <em>A. maculatum</em>VOCs, and their influence on variation in pollinator attraction rates. Therefore, we performed <em>de novo</em> transcriptome sequencing of <em>A. maculatum</em> appendix and male floret tissue collected during- and post-anthesis, from ten natural populations across Europe. These RNA-seq data were paired with GC-MS analyses of floral scent composition and pollinator data collected from the same inflorescences. Differential expression analyses revealed candidate transcripts in appendix tissue linked to malodourous VOCs including indole, <em>p</em>-cresol, and 2-heptanone. Additionally, we found that terpene synthase expression in male floret tissue during anthesis significantly covaried with sex- and species-specific attraction of <em>Psychoda phalaenoides</em> and <em>P.</em> <em>grisescens</em>. Taken together, our results provide the first insights into molecular mechanisms underlying pollinator attraction patterns in <em>A. maculatum</em>, and highlight floral chamber sesquiterpene (<em>e.g.</em>bicyclogermacrene) synthases as interesting candidate genes for further study.</p>
Direct RNA targeted in situ sequencing for transcriptomic profiling in tissue
<p>You can find here the Direct RNA In Situ Sequencing (HybISS-based) maps generated using the Hight Sensitivity kit from CARTANA AB. They include half a mouse brain coronal section, targeting 50 genes. Genes were targeted in a sequential manner. Both reads, DAPI staining and segmented cells are included. The analysis of the same cells, but using 10X magnification are also provided in an anndata object.</p>
Data files: Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures
<p>Scripts, preprocessed count matrices, single-cell data objects, and generated data (tables and .rds files) from the scRNA-seq analyses performed in <strong>“Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures".</strong></p> <p> </p>
Supplementary data: Medicago transcriptomics DRMN analysis
<p>Summary of DRMN per-gene module assignments, module motif enrichments, inferred network edge weights, and MTG-LASSO predictions, Supplementary data tables 1-4 of this submission, respectively. </p>
Supplementary information for yqiC and global transcriptome in Salmonella
<p>Supplementary information (Additional files 1-22, including 3 files, Table S1-S9, Fig. S1-S10) in the article entitled "Effects of colonization-associated gene <em>yqiC</em> on global transcriptome, cellular respiration, and oxidative stress in <em>Salmonella </em>Typhimurium"</p>
Transcriptome data of social defeat stress of various durations
<p>Data of meta-analysis of transcriptomic changes in the PFC after SDS of various durations. This analysis enabled us to identify clusters of the most reproducible changes in gene expression after 10 and 30 days of SDS and to determine which genes are associated with susceptibility and resistance to SDS after 10 days. Furthermore, we compared such changes in mice after SDS of various durations with data from a meta-analysis of postmortem transcriptomes of the dorsolateral PFC from depressed patients and data of the transcriptomic study on the PFC from patients with PTSD.</p>
The Supplementary Material for the article entitled "Comparative analysis of global transcriptomes in nontyphoidal Salmonella clinical isolates from pediatric patients with and without bacteremia after infecting human intestinal epithelium in vitro"
<p>The Supplementary Material (Additional files 1-5, including Table S1-S4 and Figure S1) for this article.</p> <p> </p> <p><strong>Table S1.</strong> Upregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S2.</strong> Downregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S3. </strong>The enriched GO terms in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S4. </strong>The enriched KEGG pathways in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Figure S1. </strong>The enriched GO terms and KEGG pathways in Group B relative to Group A. Bar charts show the enriched GO terms (A) and the enriched KEGG pathways (B) by significance power. Color of bars indicate power of significance and length in x axes of bar indicate number of annotated genes in the particular term of pathway. Cnetplots show the relationship between GO term (C) and KEGG pathways (D). Dot size representing GO terms and KEGG pathways indicates number of significantly changed and its annotated genes. The GO terms or KEGG pathways connected through their common and annotated genes. </p>
Meta-analysis of diurnal transcriptomics reveals strong patterns of concordance and discordance in mouse liver: processed data
<p>The accumulation of public transcriptomic timeseries data enables robust meta-analyses that were not possible until recently. To assess the consistency of biological rhythms across studies, 43 public mouse liver tissue timeseries totaling 805 RNA-seq samples were obtained and analyzed. Only the control groups of each study were included, to create comparable data. Technical factors in RNA-seq library preparation were the largest contributors to transcriptome-level differences, beyond biological or experiment-specific factors such as lighting conditions. Core clock genes were remarkably consistent in phase across all studies, while phase distributions of other periodic genes were generally less consistent. Overlap of genes identified as rhythmic across studies was generally low, with around 50% between some of the highest sample count studies. Distributions of phases of significant genes were remarkably inconsistent across studies, but genes consistently identified as rhythmic clustered near ZT0 and ZT12 in acrophase. Data was integrated across studies in a JIVE analysis, which showed that the top two components of joint within-study variation are determined by time of day. A shape-invariant model with random effects was fit to the genes to identify the underlying shape of the rhythms, consistent across all studies. This revealed the extent of asymmetric and multimodal genes.<br> <br> This supplemental file provides preprocessed RNA-seq quantifications of all reviewed datasets, as well as results of multiple analyses.</p>
Data from: Single cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice
<p>2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131,613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1,339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (<em>Rspo3</em>) and pericentral <em>Apc</em>, suggested dysregulation of the Wnt/β-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies.</p>
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</p>
patteRNA: transcriptome-wide search for functional RNA elements via structural data signatures, Datasets.
<p>Datasets, code and results supporting the manuscript:</p> <p>Ledda M. & Aviran S., patteRNA: transcriptome-wide search for functional RNA elements via structural data signatures</p>
Annotation table - Whole body transcriptomes of the tick Ixodes ricinus at different stage and feeding conditions
<p>Annotation table for a <em>de novo</em> assembled transcriptome of<em> Ixodes ricinus</em> in different stages and conditions.</p> <p>Description of the fields of each column (Trinotate results, and additionnal statistics):</p> <p>1. Contig_name: name of the contig (Trinity assembly)</p> <p>2. sprot_Top_BLASTX_hit: first hit of the blastx search against SwissProt (https://data.broadinstitute.org/Trinity/Trinotate v2.0 RESOURCES/)</p> <p>3. TrEMBL_Top_BLASTX_hit: first hit of the blastx search against Uniref90 (https://data.broadinstitute.org/Trinity/Trinotate v2.0 RESOURCES/)</p> <p>4. RNAMMER: identification of non-coding RNAs</p> <p>5. prot_id: identifier of the predicted protein (TransDecoder)</p> <p>6. prot_coords: coordinates (start, end and strand) of the predicted protein on the contig</p> <p>7. sprot_Top_BLASTP_hit: first hit of the blastp search between the predicted protein and SwissProt (https://data.broadinstitute.org/Trinity/Trinotate v2.0 RESOURCES/)</p> <p>8. TrEMBL_Top_BLASTP_hit: first hit of the blastp search between the predicted protein and Uniref90 (https://data.broadinstitute.org/Trinity/Trinotate v2.0 RESOURCES/)</p> <p>9. Pfam: result of the search against PfamA database</p> <p>10. SignalP: prediction of a signal peptide with SignalP</p> <p>11. TmHMM: prediction of a transmembrane domain with THMM</p> <p>12. eggnog: eggNOG database of orthologous genes (v3.0) assignation</p> <p>13. gene_ontology_blast: GO assignation based on blast results</p> <p>14. gene_ontology_pfam: GO assignation based on pfam results</p> <p>15. Contig_length: length of the contig in bp</p> <p>16. Busco_Id: name of the BUSCO (v1)</p> <p>17. Busco_status: status of the BUSCO (complete/fragmented/duplicated)</p> <p>18-32: Kallisto read counts for the 15 libraries</p> <p>A, B, C: unfed nymphs (replicates 1, 2, 3)</p> <p>D, E, F: partially fed nymphs (replicates 1, 2, 3)</p> <p>G, H, I: males (unfed) (replicates 1, 2, 3)</p> <p>J, K, L: unfed adult females (replicates 1, 2, 3)</p> <p>M, N, O: partially fed adult females (replicates 1, 2, 3)</p> <p>33. log2FoldChange_UnfedVsPartiallyFed: log fold change in base 2 of expression (comparison between "unfed" -including males- and "fed" ticks)</p> <p>34. pvalue_UnfedVsPartiallyFed: p-value of the comparison between "unfed" -including males- and "fed" ticks</p> <p>35. log2FoldChange_MaleVsFemale: log fold change in base 2 of expression (comparison between "males" and "females")</p> <p>36. pvalue_MaleVsFemale: p-value of the comparison between "males" and "females"</p> <p>37. log2FoldChange_NymphsVsAdults: log fold change in base 2 of expression (comparison between "nymphs" and "adults" -males and females-)</p> <p>38. pvalue_NymphsVsAdults: p-value of the comparison between "nymphs" and "adults" -males and females-)</p> <p> </p> <p> </p>
Transcriptomic characterisation of human menstrual fluid at homeostasis and in endometriosis
Open the record for dataset details and reuse information.
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
<p>Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.</p>
Figure 7 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 7. Temporal quantitative PCR results for (A) GST-nt, (B) ABC10, (C) PEROX12, and (D) GST-ct before and at multiple time points after dicamba treatment. Asterisks (*) indicate comparisons that were significant (t-test P-value <0.05), with error bars indicating variability across replicates.
Figure 4 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 4. Genomic distribution in sliding 50-kb window plots of differentially expressed genes (DEs). The y-axis units refer to windows in mega base pairs (Mbp) Each plot represents one of the 16 pseudo-chromosomes of Amoronthus tuberculotus. Peaks represent clusters of DEs. Blue dashed lines represent previously identified hot-spot locations for 2,4-D resistance (Giacomini et al. 2020).
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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.