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3,818 results for “Differential Expression”
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Differential response of α-synuclein expression to bacterial ligands and metabolites in mouse enteroendocrine cells
<p>Dataset for manuscript <em>"<strong> </strong></em><strong>α</strong><strong>-synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells</strong><em>". </em>Tabs in excel file are title with the figure number. </p>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets
<p>This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (<a href="https://doi.org/10.1186/s12915-023-01724-w" target="_blank" rel="noopener">article</a>).<br>All results were computed using the R package <a href="https://github.com/shdam/pairedGSEA">pairedGSEA</a>, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019).<br>See limma results here: <a href="https://doi.org/10.5281/zenodo.8162214">https://doi.org/10.5281/zenodo.8162214</a><br><br>Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.<br><br>The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".<br><br>All datasets were obtained from a local copy of the ARCHS4 v11 database of transcript counts (Lachmann et al., 2018).</p>
Data from: Differential gene expression in relation to mating system in Peromyscine rodents
Behaviors that increase an individual's exposure to pathogens are expected to have important effects on immunoactivity. Because sexual reproduction typically requires close contact among conspecifics, mating systems provide an ideal opportunity to study the immunogenetic correlates of behaviors with high versus low risks of pathogen exposure. Despite logical links between polygynandrous mating behavior, increased pathogen exposure, and greater immunoactivity, these relationships have seldom been examined in nonhuman vertebrates. To explore interactions among these variables in a different lineage of mammals, we used RNAseq to study the gene expression profiles of liver tissue—a highly immunoactive organ—from sympatric populations of the monogamous California mouse (Peromyscus californicus) and two polygynandrous congeners (P. maniculatus and P. boylii). Differential expression and co‐expression analyses revealed distinct patterns of gene activity among species, with much of this variation associated with differences in mating system. This tendency was particularly pronounced for MHC genes, with multiple MHC Class I genes being upregulated in the two polygynandrous species, as expected if exposure to sexually transmitted pathogens varies with mating system. Our results underscore the role of mating behavior in influencing patterns of gene expression and highlight the use of emerging transcriptomic tools in behavioral studies of free‐living animals.
Data from "Corset: enabling differential gene expression analysis for de novo assembled transcriptomes"
<p>This dataset contains de novo transcriptome assemblies for three publicly available RNA-seq dataset (SRA055442, SRR453566-SRR453571 and GSE37704 ). For each assembly we also provide a table with the read counts per contig, the output from corset (clusters and counts), and the results from a genome-based analysis. This dataset was used to assess the performance of the corset software. More detail is provided in the paper: Nadia M Davidson and Alicia Oshlack,<strong> </strong>Corset: enabling differential gene expression analysis for de novo assembled transcriptomes, <em>Genome Biology</em> 2014, <strong>15</strong>:410. http://genomebiology.com/2014/15/7/410/abstract</p>
Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection
<p>We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .</p> <p>We used the following commands in DESeq2:<br> #Run DESeq2<br> dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) <br> dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)</p> <p>#Extract differentially expressed genes in each condition<br> ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) <br> sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) <br> ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))</p>
Critical Assessment of RNA-Seq Differential Expression
<p><strong>Warden and Wu Preprint</strong>: <a href="https://www.biorxiv.org/content/10.1101/2024.02.10.579728v1">v1</a></p> <p>In general, this primarily focuses on the following types of comparisons:</p> <ol> <li>Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads.</li> <li>Processed TCGA (The Cancer Genome Atlas) data for breast cancer (BRCA) to compare gene expression by immunohistochemistry status (ER/ESR1, PR/PGR, or HER2/ERBB2).</li> </ol> <p>Differential expression methods include the following:</p> <ul> <li><em>edgeR (GLM)</em></li> <li><em>edgeR-robust (GLM)</em></li> <li><em>edgeR (QL)</em></li> <li><em>edgeR-robust (QL)</em></li> <li><em>DESeq1</em></li> <li><em>DESeq2</em></li> <li><em>limma-voom</em></li> <li><em>limma-trend (CPM)</em></li> <li><em>limma-trend (FPKM/RPKM)</em></li> <li><em>ANOVA (log2 FRPKM/RPKM)</em></li> </ul> <p>The most common preprocessing strategies include STAR, TopHat2, and Salmon. However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided.</p> <p>Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). However, a limited amount of additional processing with featureCounts is also provided.</p> <p>Most STAR and TopHat2 alignments start with the public <strong>forward</strong> reads, even if paired-end data was available.</p>
Differential Gene Expression Datasets for "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach"
<p>This dataset has the unfiltered transcriptome differential expression results used in the paper "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach". </p>
Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics
<p>Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster. </p>
FIGURE 5 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 5 Mean (+SD) relative expression of gnrh2, fshb, lhb, fshr and lhr messenger (m)RNA in (a) the breeding season () 2n, and () 4n and (b) the non-breeding season in Carassius auratus red var. () 2n, and () 4n. (RCC,) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR,). T, gene detected in the testis; O, gene detected in the ovary. *, significant difference between RCC and 4nRR (P <0.05)
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 () and Lhr () genes in Carassius auratus red var. (RCC) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR)
FIGURE 2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 2 (a) The mature eggs (scale bar = 100 μm) and (b) mature sperm (scale bar = 10 μm) of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR)
FIGURE 3 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 3 Reverse-transcription (RT)-PCR analysis of the expression of (a) gnrh2, (b) fshb, (c) lhb, (d) fshr and (e) lhr messenger (m)RNA in various tissues of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR). The upper strip of each panel (a)–(e) shows the positive control of actin gene while the lower strip of each panel shows the RT-PCR amplification of the target gene
FIGURE 1 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 1 The gonadal structure of Carassius auratus red var. [RCC; (a)–(c)] and autotetraploids C. auratus red var. ♀ × Megalobrama amblycephala ♂ [4nRR; (d)–(f)]: (a) ovary of 7 month-old RCC containing many phase II and a few phase III oocytes; (b) ovary of 12 month-old RCC showing many mature phase IV ova; (c) testis of 12 month-old RCC with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes; (d) ovary of 7 month-old 4nRR containing phase II and a few phase III oocytes; (e) ovary of 12 month-old 4nRR with numerous mature phase IV ova; (f) testis of 12 month-old 4nRR with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes, the scale bars: (a), (b), (d), and (e) = 100 μm; (c) and (f) = 10 μm
Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor
<p>Supplementery materials of journal article "Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in <em>Sorghum bicolor</em>"</p>
Data from: Differential gene expression during recall of behaviorally conditioned immune enhancement in rats: a pilot study
<p><strong>Background:</strong> Behaviorally conditioned immune functions are suggested to be regulated by bidirectional interactions between CNS and peripheral immune system <em>via</em> the hypothalamic-pituitary-adrenal (HPA) axis, sympathetic nervous system (SNS), and the parasympathetic nervous system (PNS). Since the current knowledge about biochemical pathways triggering conditioned immune enhancement is limited, the aim of this pilot study was gaining more insights into that.</p> <p><strong>Methods: </strong>Rats were conditioned with camphor smell and poly I:C injection, mimicking a viral infection. Following stimulus re-exposure, animals were sacrificed at different time points, and neural tissues along the HPA axis was analyzed with a rat genome array together with plasma protein using Luminex analysis.</p> <p><strong>Results:</strong> In the hypothalamus, we observed a strong upregulation of genes related to Wnt/β-catenin signaling (Otx2, Spp1, Fzd6, Zic1), monoaminergic transporter Slc18a2 and opioid-inhibitory G-protein Gpr88 as well as downregulation of dopaminergic receptors, vasoactive intestinal peptide Vip, and pro-melanin-concentrating hormone Pmch. In the pituitary, we recognized mostly upregulation of steroid synthesis in combination with GABAergic, cholinergic and opioid related neurotransmission, in adrenal glands, altered genes showed a pattern of activated metabolism plus upregulation of adrenoceptors Adrb3 and Adra1a. Data obtained from spleen showed a strong upregulation of immunomodulatory genes, chemo-/cytokines and glutamatergic/cholinergic neurotransmission related genes, as also confirmed by increased chemokine and ACTH levels in plasma.</p> <p><strong>Conclusions:</strong> Our data indicate that in addition to the classic HPA axis, there could be additional pathways as e.g. the cholinergic anti-inflammatory pathway (CAIP), connecting brain and immune system, modulating and finetuning communication between brain and immune system.</p>
R code for differential gene expression and enrichment analyses
<p>The information about the magnitude of differences in thermal plasticity both between and within populations, as well as identification of the underlying molecular mechanisms are key to understanding the evolution of thermal plasticity. In particular, genes underlying variation in the physiological response to temperature can provide raw material for selection acting on plastic traits. Using RNAseq, we investigate the transcriptional response to temperature in males and females from bulb mite populations selected for the increased frequency of one of two discrete male morphs (fighter- and scrambler-selected populations) that differ in relative fitness depending on temperature. We show that different mechanisms underlie the divergence in thermal response between fighter- and scrambler-selected populations at decreased vs. increased temperatures. Temperature decrease to 18°C was associated with higher transcriptomic plasticity of males with more elaborate armaments, as indicated by a significant selection-by-temperature interaction effect on the expression of 40 genes, 38 of which were upregulated in fighter-selected populations in response to temperature decrease. In response to 28°C, no selection-by-temperature interaction in gene expression was detected. Hence, differences in phenotypic response to temperature increase likely depended on genes associated with their distinct morph-specific thermal tolerance. Selection on males also drove gene expression patterns in females. These patterns could be associated with temperature-dependent fitness differences between females from fighter- vs. scrambler-selected populations reported in previous studies. Our study shows that selection for divergent male sexually selected morphologies and behaviors has the potential to drive divergence in metabolic pathways underlying plastic response to temperature in both sexes.</p>
Fig 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 1: Relative gene expression in tambaqui juveniles farmed in two Brazilian regions: Northern (Balbina; BA) and Southeast (Brumado; BRU). Different letters represent statistical differences between populations. The graphs show expression of A) hif-1α (p = 0.137), B) hsp-70 (p = 0.465), C) mstn (p = 0.907), D) ube3a (p = 0.205), E) ras (p = 0.041), F) cry-1 (p = 0.001), G) per-1 (p = 0.001), H) ogt (p = 0.001) and I) acly (p = 0.025).
Fig 3 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 3: IBR analyses of relative gene expression in Balbina (BA) and Brumado (BRU) populations. The IBR values are 42.7 (Balbina) and 6.79 (Brumado).
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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.