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25,372 results for “Transcriptomics”
Transcriptome profiling of medullary thymic epithelial cells from Aire-knockout rats
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Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in wild-type and PPARa knockout mouse hepatocytes
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Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology
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Data from: One thousand plant transcriptomes and the phylogenomics of green plants
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Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
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Kellet's whelk genome and transcriptome assembly
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Full-length transcriptomes of 25 grassland plant species
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Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach
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Data from: A phylogenomic approach to clarifying the relationship of Mesodinium within the Ciliophora: a case study in the complexity of mixed-species transcriptome analyses
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Synthetic bulk RNA-Seq transcriptomic profiles representing 10 Cancer hallmarks
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Extended Dataset for de novo transcriptome of Austropotamobius pallipes
<p>Supporting data for the analysis of A. pallipes transcriptome</p> <p>Apal_assembly.fasta: Final version submitted to NCBI Transcriptome Shotgun Assembly database after merging and contaminant-removal</p> <p>Apal_transcript_abundance.tsv: Transcript abundance estimation generated by Kallisto</p> <p>Apal_assembly_cds.fna: Coding sequences in the transcriptome assembly</p> <p>Apal_assembly_prot.faa: Translated coding sequences in the transcritpome assembly</p> <p>Apal_CAZY.txt: CAZY analysis of the translated coding sequences</p> <p>Apal_InterProScan.tsv: InterProScan analysis output of the translated coding sequences</p> <p>Apal_rnabloom.fasta.gz: Initial transcriptome assembly from rnabloom</p> <p>Apal_rnaSpades.fasta.gz: Initial transcriptome assembly from rnaspades</p> <p>Apal_Trinity.fasta.gz: Initial transcriptome assembly from Trinity</p> <p>BUSCO.tar.gz: BUSCO output</p> <p> </p> <p> </p>
Processed Saccharomyces cerevisiae transcriptomics and genomics data for machine learning
<p><strong>Genomic data including open reading frame (ORF) boundaries of Saccharomyces cerevisiae C288 was obtained from the Saccharomyces Genome Database (<a href="https://www.yeastgenome.org/">https://www.yeastgenome.org/</a>) (<a href="http://paperpile.com/b/QmuOBv/gg2yy">Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–5 (2012)</a>) and published data (<a href="http://paperpile.com/b/QmuOBv/g4EbQ">Xu, Z. et al. Bidirectional promoters generate pervasive transcription in yeast. Nature 457, 1033–1037 (2009)</a>, <a href="http://paperpile.com/b/QmuOBv/uJqAw">Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008)</a>). </strong><strong>Coding regions were extracted based on ORF boundaries and codon frequencies were normalized to probabilities. Processed raw RNA sequencing Star counts were obtained from the Digital Expression Explorer V2 database (<a href="http://dee2.io/index.html">http://dee2.io/index.html</a>) (<a href="http://paperpile.com/b/QmuOBv/LCGD">Ziemann, M., Kaspi, A. & El-Osta, A. Digital expression explorer 2: a repository of uniformly processed RNA sequencing data. GigaScience vol. 8 (2019)</a>) and filtered for experiments that passed quality control. Raw mRNA data were transformed to transcripts per million (TPM) counts and genes with zero mRNA output (TPM < 5) were removed. Prior to modeling, the mRNA counts were Box-Cox transformed.</strong></p>
Chaetoceros decipiens (UNC1416) reference transcriptome
<p>Reference transcriptome and associated annotations for <em>Chaetoceros decipiens </em>(UNC1416). </p> <p>A culture was grown into late exponential phase for filtration. Total RNA was extracted using the RNAqueous-4PCR Total RNA Isolation Kit (Ambion, Foster City, CA, USA) according to the manufacturer’s protocol with an initial bead beating step to disrupt cells. RNA libraries were created with either the Illumina TruSeq Stranded mRNA Library Preparation Kit. The library was sequenced on an Illumina MiSeq (300 bp, paired-end reads) and an Illumina HiSeq 2500 with one lane in high output mode (100 bp, paired-end reads) and another lane in rapid run mode (150 bp, paired-end reads).</p> <p>Raw reads were trimmed for quality with Trimmomatic v0.36 then assembled <em>de novo </em>with Trinity v2.5.1 with the default parameters for paired-reads and a minimum contig length of 90 bp. Contigs were clustered based on 99% similarity using CD-HIT-EST v4.7 and then protein sequences were predicted with GeneMark S-T. Protein sequences were annotated by best-homology (lowest E-value) with the KEGG (Release 86.0), UniProt (Release 2018_03), and PhyloDB (v1.076) databases via BLASTP v2.7.1 (E-value ≤ 10<sup>-5</sup>) and with Pfam 31.0 via HMMER v3.1b2 (Dataset S2). KEGG Ortholog (KO) annotations were assigned from the top hit with a KO annotation from the top 10 hits (<a href="https://github.com/ctberthiaume/keggannot">https://github.com/ctberthiaume/keggannot</a>).</p> <p>Provided here are predicted proteins as nucleotides and peptides. Raw reads are deposited in SRA (SRP234548).</p>
Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS- CoV-2 infected cells - Proteomic data obtained with Huh-7 cells
<p>Cultured human Huh-7 cells were infected with SARS-CoV-2 and harvested after 24, 48 and 72 h. The extracted proteins were processed in triplicates preparing for mass spectrometric analysis. Data acquisition was completed, including control samples of non-infected cells, following isobaric tandem mass tag (TMT) chemical labeling and on-line fractionation of the 12 combined biological replicates. The resulted vendor specific raw files (Thermo Scientific) of 12 fractions are provided.</p> <p>The data is further analyzed in order to identify regulated proteins upon SARS-CoV-2 infection to understand the underlying biological processes through pathway analysis. Additional details about the study is going to be completed in the manuscript already submitted for publication.</p>
Data from: A rat liver transcriptomic point of departure predicts a prospective liver or non-liver apical point of departure
<p>Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. Using rat transcriptome and apical data for 79 molecules obtained from Open TG-GATES (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) (632 datasets), the hypothesis was tested that a short-term exposure, transcriptome-based liver biological effect POD (BEPOD) could estimate a longer-term exposure "systemic" apical endpoint POD. Apical endpoints considered were body weight, clinical observation, kidney weight and histopathology and liver weight and histopathology. A BMDExpress algorithm using Gene Ontology Biological Process gene sets was optimized to derive a liver BEPOD most predictive of a systemic apical POD. Liver BEPODs were stable from 3 hours to 29 days of exposure; the median fold difference of the 29 day BEPOD to BEPODs from earlier time points was approximately 1 (range of 0.7-1.1). Strong positive correlation (Pearson R = 0.86) and predictive accuracy (root mean square difference = 0.41) were observed between a concurrent (29 day) liver BEPOD and the systemic apical POD. Similar Pearson R and root mean square difference values were observed for comparisons between a 29 day systemic apical POD and liver BEPODs derived from 3 hours to 15 days of exposure. These data across 79 molecules suggest that a longer-term exposure study apical POD from liver and non-liver compartments can be estimated using a liver BEPOD derived from an acute or subacute exposure study.</p>
Data from: Comparison of spleen transcriptomes of two wild rodent species reveals differences in the immune response against Borrelia afzelii
<p>Different host species often differ considerably in susceptibility to a given pathogen, but the causes of such differences are rarely known. The natural hosts of the tick-transmitted bacterium <i>Borrelia afzelii</i>, which is one of causative agents of Lyme borreliosis in humans, include a variety of small mammals like voles and mice. Previous studies have shown that <i>B. afzelii-</i>infected bank voles (<i>Myodes glareolus</i>) have about ten times higher bacterial load than infected yellow-necked mice (<i>Apodemus flavicollis</i>), indicating that these two species differ in resistance. In this study, we compared the immune response to <i>B. afzelii </i>infection in these host species by using RNA-sequencing to quantify gene expression in spleen. Gene set enrichment analysis (GSEA) showed that several immune pathways were down-regulated in infected animals in both bank voles and yellow-necked mice. Moreover, IFNα response was up-regulated in <i>B. afzelii</i>-infected yellow-necked mice, while IL6 signaling and the complement pathway were down-regulated in infected bank voles; differences in regulation of these three pathways between bank voles and yellow-necked mice could thus contribute to the difference in resistance to <i>B. afzelii</i> between the species. This study provides knowledge of gene expression induced by a zoonotic pathogen in its natural host, and possible species-specific regulation of immune responses associated with resistance.</p>
Data from: Transcriptomic plasticity of mesophotic corals among natural populations and transplants of Montastraea cavernosa in the Gulf of Mexico and Belize
<p>While physiological responses to low-light environments have been studied among corals on mesophotic coral ecosystems worldwide (MCEs; 30–150 m), the mechanisms behind acclimatization and adaptation to depth are not well understood for most coral species. Transcriptomic approaches based on RNA sequencing are useful tools for quantifying gene expression plasticity, particularly in slow-growing species such as scleractinian corals, and for identifying potential functional differences among conspecifics. A tag-based RNA-Seq (Tag-Seq) pipeline was applied to quantify transcriptional variation in natural populations of the scleractinian coral <i>Montastraea cavernosa</i> from mesophotic and shallower environments across five sites in Belize and the Gulf of Mexico: Carrie Bow Cay, West and East Flower Garden Banks, Pulley Ridge, and Dry Tortugas. Regional site location was a stronger driver of gene expression patterns than depth. However, mesophotic corals among all sites shared similar regulation of metabolic and cell growth functional pathways that may represent common physiological responses to environmental conditions at depth. Additionally, in a transplant experiment at West and East Flower Garden Banks, colonies transplanted from mesophotic to shallower habitats diverged from the control mesophotic group over time, indicating depth-regulated plasticity of gene expression. When the shallower depth zone experienced a bleaching event, bleaching severity did not differ significantly between transplants and shallow controls, but gene expression patterns indicated variable regulation of stress responses among depth treatments. Coupled observational and experimental studies of gene expression among mesophotic and shallower <i>M. cavernosa</i> provide insights into the ability of this depth-generalist coral species to persist under varying environmental conditions. </p>
Data from: Gut microbiome critically impacts PCB-induced changes in metabolic fingerprints and the hepatic transcriptome in mice
<p class="western"><span><span><span>Polychlorinated biphenyls (PCBs) are ubiquitously detected in the environment and have been linked to metabolic diseases. The liver serves as a central hub for the metabolism of xenobiotics and endogenous metabolites. Gut dysbiosis is recognized as a critical regulator of disease susceptibility, however, little is known regarding how PCBs and gut microbiome interact to modulate the interface between xenobiotic and intermediary metabolism. We hypothesized that the gut microbiome regulates PCBs-mediated changes in the metabolic fingerprints and hepatic transcriptome. Ninety-day-old female conventional (CV) and germ-free (GF) C57BL/6 mice were orally exposed to the PCB Fox River Mixture (synthetic PCB mixture, 6 or 30 mg/kg) or corn oil (vehicle control, 10 ml/kg), once daily for 3 consecutive days. Organs were collected 24 hours after the final dose. RNA-Seq was conducted on liver, and endogenous aqueous metabolites (amino acids, carbohydrates, and nucleotides) were measured in liver and serum by LC-MS. The primary factor in clustering the transcriptomic and metabolomic signatures within the same exposure was by enterotype. The numbers of PCB-regulated genes were higher in CV than in GF conditions. The prototypical target genes of the major xenobiotic-sensing transcription factors AhR, PXR, and CAR were more readily up-regulated by PCBs in CV than in GF conditions, indicating the effect of PCBs on the hepatic transcriptome act partly through the gut microbiome. Xenobiotic and steroid metabolism pathways were up-regulated, whereas response to incorrect proteins pathway was down-regulated by PCBs in a gut microbiome-dependent manner. At the high PCB dose, NADP and arginine appear to interact with drug-metabolizing enzymes (Cyp1-3 family, DhcR7, and Nqo1), which are highly correlated with <i>Anaerotruncus</i> and <i>Roseburia</i> in CV mice, providing a novel explanation of gut-liver interaction in toxicant exposures. In GF exposure groups, hepatic glucose was down-regulated, whereas fructose 6-phosphate and glucose 6-phosphate were up-regulated, indicating increased glucose utilization potentiated by lack of gut microbiota. Through querying the LINCS L1000 chemical database, Enrichr predicted that therapeutic drugs targeting the anti-inflammatory and ER stress pathways are potential remedies to mitigate PCB toxicity. In conclusion, our findings demonstrate that habitation of the gut microbiota drives PCBs-mediated hepatic responses, possibly due to crosstalk between gut and liver. </span></span></span></p>
Data from: Characterization of rhizome transcriptome and identification of a rhizomatous ER body in the clonal plant Cardamine leucantha
<p>The rhizome is a plant organ that develops from a shoot apical meristem but penetrates into belowground environments. To characterize the gene expression profile of rhizomes, we compared the rhizome transcriptome with those of the leaves, shoots and roots of a rhizomatous Brassicaceae plant, <i>Cardamine leucantha.</i> Overall, rhizome transcriptomes were characterized by the absence of genes that show rhizome-specific expression and expression profiles intermediate between those of shoots and roots. Our results suggest that both endogenous developmental factors and external environmental factors are important for controlling the rhizome transcriptome. Genes that showed relatively high expression in the rhizome compared to shoots and roots included those related to belowground defense, control of reactive oxygen species, and cell elongation under dark conditions. A comparison of transcriptomes further allowed us to identify the presence of an ER body, a defense-related belowground organelle, in epidermal cells of the <i>C. leucantha </i>rhizome, which is the first report of ER bodies in rhizome tissue.</p>
Transcriptome analysis of invasive Gypsophila paniculata (baby's breath) populations from Michigan and Washington, USA.
<p>Invasive species provide an opportune system to investigate how populations respond to new or changing environments. While the impacts of invasive species increase annually, many gaps in our understanding of how these species invade, adapt, and thrive in the areas they are introduced to remain. Using the perennial forb <i>Gypsophila paniculata</i> as a study system, we aimed to investigate how invasive species respond to different environments. Baby's breath (<i>Gypsophila paniculata</i>) was introduced to North America in the late 1800's and has since spread throughout the northwestern United States and western Canada. We used an RNA-seq approach to explore how molecular processes may be contributing to the success of invasive <i>G. paniculata</i> populations that are thought to share similar genetic backgrounds across distinct habitats. Transcription profiles were constructed for root, stem, and leaf tissue from seedlings collected from a sand dune ecosystem in Petoskey, MI (PSMI) and a sagebrush ecosystem in Chelan, WA (CHWA). Using these data we assessed differential gene expression between the two populations and identified SNPs within differentially expressed genes. We identified 1,146 transcripts that were differentially expressed across all tissues between the two populations. GO processes enriched by genes displaying higher expression in PSMI were associated with increased nutrient starvation, while enriched processes in CHWA were associated with abiotic stress. Only 7.4% of the differentially expressed genes across all three tissues contained SNPs differing in allele frequencies of at least 0.5 between the populations. In addition, common garden studies found the two populations differed in germination rate and seedling emergence success, but not in above- and below-ground tissue allocation. Our results suggest that the success of invasive <i>G. paniculata</i> across these two environments is likely the result of plasticity in molecular processes responding to different environmental conditions, although some genetic divergence may also be contributing to these differences.</p>
ScienceDex guides
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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.