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2,556 results for “RNAseq”
Optimization used in in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>OptimizationResults contain the expanded input model for each data set, in the C field are the indices of the core reactions in the expanded input model used to build the multi-cell population for the 121 different parameters, A contains the indices of reaction in the multi-cell population model for the 121 runs, thresh the parameter setting for the cover and REI. The REI is given in %. So 5 REI means 0.05. While A might change because of alternative optimals when run on a different computer, C and the expanded Input model will remain unchanged.</p> <p> </p> <p>Multi_cell_population_CRC and Multi_cell_population_NM are the models obtained by scFASTCORMICS with the optimal setting for Dataset1 and Dataset2, respectively. </p> <p>Please, check the publication (scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data) for more details.</p>
Fig. 3 in Transcriptional response of giant reed (Arundo donax L.) low ecotype to long-term salt stress by unigene-based RNAseq
Fig. 3. Distribution of transcription factors responsive to salt stress. Data are sorted by number of G34-S3 vs G34-CK DEGs.
Fig. 2 in Transcriptional response of giant reed (Arundo donax L.) low ecotype to long-term salt stress by unigene-based RNAseq
Fig. 2. GO enrichment analysis for the DEGs in A. donax (G34-S3 vs G34-CK) The X-axis indicates the numbers related to the total number of GO terms, and the Y-axis indicates the subcategories. BP, biological processes; CC, cellular components; MF, molecular functions.
Fig. 1 in Transcriptional response of giant reed (Arundo donax L.) low ecotype to long-term salt stress by unigene-based RNAseq
Fig. 1. Volcano plot showing the DEGs of G34-S3 vs G34-CK comparison. The up-regulated genes with statistically significance are represented by blue dots, the green dots represent the down-regulated genes and the red dots are DEGs with -log10padj <1.3, adopting log2FoldChange threshold of 0.58 (1.5 fold change). The X-axis is the gene expression change, and the Y-axis is the pvalue adjusted after normalization. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Dataset from RNAseq analysis of differential gene expression among developmental stages of two non-marine ostracods
<p>Dataset comprising tree files and alignments used for phylogenetic validation of data, assemblies of reference transcriptomes and draft genomes, annotation of draft genomes, as well as supplementary tables.</p>
Single nuclei RNAseq stratifies multiple sclerosis patients into distinct white matter glial responses
<p>The lack of understanding of the cellular and molecular basis of clinical and genetic heterogeneity in progressive multiple sclerosis (MS) has hindered the search for new effective therapies. Here, to address this gap, we analysed 632,000 single nuclei RNAseq profiles of 156 brain tissue samples, comprising white matter (WM) lesions, normal appearing WM, grey matter (GM) lesions and normal appearing GM from 54 MS patients and 26 controls. We observed the expected changes in overall neuronal and glial numbers previously described within the classical lesion subtypes. We found highly cell type-specific gene expression changes in MS tissue, with distinct differences between GM and WM areas, confirming different pathologies. However, surprisingly, we did not observe distinct gene expression signatures for the classical different WM lesion types, rather a continuum of change. This indicates that classical lesion characterization better reflects changes in cell abundance than changes in cell type gene expression, and indicates a global disease effect. Furthermore, the major biological determinants of variability in gene expression in MS WM samples relate to individual patient effects, rather than to lesion types or other metadata. We identify four subgroups of MS patients with distinct WM glial gene expression signatures and patterns of oligodendrocyte stress and/or maturation, suggestive of engagement of different pathological processes, with an additional more variable regenerative astrocyte signature. The discovery of these patterns, which were also found in an independent MS patient cohort, provides a framework to use molecular biomarkers to stratify patients for optimal therapeutic approaches for progressive MS, significantly advances our mechanistic understanding of progressive MS, and highlights the need for precision-medicine approaches to address heterogeneity among MS patients.</p>
NGS Combined With RNAseq on Tumor Immune Escape in NSCLC
ClinicalTrials.gov study NCT03764917. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
RNASeq data from vomocytes (dendritic cells and macrophages)
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RNAseq of tolerized auroreactive RBC-specific CD4 T cells, HODxOTII murine model of autoimmune hemolytic anemia (AIHA)
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RNAseq raw counts FLCN positive vs. FLCN negative renal proximal tubular epithelial cells (RPTEC)
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RNAseq data from: Medial prefrontal cortex samples of glutamate dehydrogenase-deficient mice, stress-exposed or -naive, and their Nestin-Cre+ controls
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RNAseq of partially paralyzed zebrafish embryos at 5 days post-fertilization compared to normal siblings
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Irisin directly stimulates osteoclastogenesis and bone resorption in vitro and in vivo: RNAseq dataset
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Tumor RNAseq and nCounter for ERY974 monotherapy and/or combination with chemotherapy
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Data from: The population genomics of sunflowers and genomic determinants of protein evolution revealed by RNAseq
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Sequencing data and normalized counts for tripartite RNAseq of Drosophila, Wolbachia, and SINV virus
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RNAseq dataset feb 2020
<p>counts file and experimental design for big data course feb 2020</p> <p>based fastq files(SRI) from project PRJEB13938 (ncbi) <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4683/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4683/</a></p> <p>mapped with hisat2 to arabidopsis genome (araport 11) and counted with featueCounds.</p> <p> </p>
A single-cell RNAseq atlas of Schistosoma mansoni identifies a key regulator of blood feeding
<p>Schistosomiasis is a neglected tropical disease that infects 240 million people. With no vaccines and only one drug available, new therapeutic targets are needed. The causative agents, schistosomes, are intravascular flatworm parasites that feed on blood and lay eggs, causing pathology. The function of the parasite's various tissues in successful parasitism are poorly understood, hindering identification of novel therapeutic targets. Using single cell RNAseq we characterize 43,642 cells from the adult schistosome, identifying 68 distinct cell populations including specialized stem cells that maintain the parasite's blood-digesting gut. These stem cells express the gene hnf4, which is required for gut maintenance, blood feeding, and pathology in vivo. Together, these data provide molecular insights into the organ systems of this important pathogen and identify potential therapeutic targets.</p>
Good vs poor responder RNAseq transcriptome profiles in DBA/2J mice
<p>Major depressive disorder is the most prevalent mental illness worldwide, still its pharmacological treatment is limited by various challenges, such as the large heterogeneity in treatment response and the lack of insight into the neurobiological pathways underlying this phenomenon. To decode the molecular mechanisms shaping antidepressant response and to distinguish those from general paroxetine effects, we used a previously established approach targeting extremes (i.e. good vs. poor responder mice). Transcriptome profiling on micro-dissected DG granule cells as well as on peripheral blood samples was performed to <i>i</i>) reveal celltype specific changes in paroxetine-induced gene expression (paroxetine vs. vehicle) and <i>ii</i>) to identify molecular signatures of treatment response within a cohort of paroxetine-treated animals. In this datasheet, we provide the mapped and norm-counted RNAseq results of our experiments in an user-friendly excel file.</p>
Data from: Evaluation of TagSeq, a reliable low-cost alternative for RNAseq
RNAseq is a relatively new tool for ecological genetics that offers researchers insight into changes in gene expression in response to a myriad of natural or experimental conditions. However, standard RNAseq methods (e.g., Illumina TruSeq® or NEBNext®) can be cost prohibitive, especially when study designs require large sample sizes. Consequently, RNAseq is often underused as a method, or is applied to small sample sizes that confer poor statistical power. Low cost RNAseq methods could therefore enable far greater and more powerful applications of transcriptomics in ecological genetics and beyond. Standard mRNAseq is costly partly because one sequences portions of the full length of all transcripts. Such whole-mRNA data is redundant for estimates of relative gene expression. TagSeq is an alternative method that focuses sequencing effort on mRNAs' 3' end, reducing the necessary sequencing depth per sample, and thus cost. We present a revised TagSeq library construction procedure, and compare its performance against NEBNext®, the "gold-standard" whole mRNAseq method. We built both TagSeq and NEBNext® libraries from the same biological samples, each spiked with control RNAs. We found that TagSeq measured the control RNA distribution more accurately than NEBNext®, for a fraction of the cost per sample (~10%). The higher accuracy of TagSeq was particularly apparent for transcripts of moderate to low abundance. Technical replicates of TagSeq libraries are highly correlated, and were correlated with NEBNext® results. Overall, we show that our modified TagSeq protocol is an efficient alternative to traditional whole mRNAseq, offering researchers comparable data at greatly reduced cost.
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.