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6,225 results for “expression analysis”
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
Analysis of microRNA expression profiles in peri-miniscrew implant crevicular fluid in orthodontics
<p> This study systematically evaluated microRNA (miRNA) expression patterns in peri-miniscrew implant crevicular fluid (PMICF) in orthodontic patients.</p>
Raw Data for the article: miRNA expression analysis in the human heart: Undifferentiated progenitors vs. bioptic tissues-Implications for proliferation and ageing
<p>In developed countries, cardiovascular diseases are currently the first cause of death. Cardiospheres (CSs) and cardiosphere-derived cells (CDCs) have been found to have the ability to regenerate the myocardium after myocardial infarction (MI). In recent years, much effort has been made to gain insight into the human heart repair mechanisms, in which miRNAs have been shown to play an important role. In this regard, to elucidate the involvement of miRNAs, we evaluated the miRNA expression profile across human heart biopsy, CSs and CDCs using microarray and next-generation sequencing (NGS) technologies. We identified several miRNAs more represented in the progenitors, where some of them can be responsible for the proliferation or the maintenance of an undifferentiated state, while others have been found to be downregulated in the undifferentiated progenitors compared with the biopsies. Moreover, we also found a correlation between downregulated miRNAs in CSs/CDCs and patient age (eg miR-490) and an inverse correlation among miRNAs upregulated in CSs/CDCs (eg miR-31).</p>
Differential expression analysis to aluminum toxicity in Citrus x limonia Osbeck
<p>Here we deliver the Differential gene expression on genes response to aluminum toxicity in <em>Citrus</em> x<em> limonia. </em>Root apices of ‘Mandarin’ lime plants grown for 60 days in nutrient solutions either with 1480 mM Al<sup>3+</sup> or 0 mM Al<sup>3+</sup> were analyzed by RNA-seq.</p> <p>Clean reads were mapped to the sweet orange (<em>Citrus sinensis</em>) genome (Xu et al. 2013). Gene expression levels were calculated by CPM (Counts per million) reads. We used HTSeq ver. 0.6.1 (Anders et al. 2015) CPM estimation. The differentially expressed genes (DEGs) here reported by NOIseq ver. 2.16.0 (Tarazona et al. 2016). </p> <p> </p> <p><strong>Results:</strong></p> <p><br> Number of differentially expressed (DE) features (Probability > 0.7): 3,351</p> <p>Up-regulated (M > 0): 1,664<br> Down-regulated (M < 0): 1,687</p> <p>All software were run on OmicsBox interface.</p> <p><strong>References:</strong></p> <p>Anders S., Pyl PT. and Huber W. (2015). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England), 31(2), 166-9.</p> <p>OmicsBox - Bioinformatics made easy. BioBam Bioinformatics (Version 2.0.36). March 3, 2019. www.biobam.com/omicsbox.</p> <p>Tarazona S., Furio-Tari P., Turra D., Pietro AD., Nueda MJ., Ferrer A. and Conesa A. (2015). Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic acids research, 43(21), e140.</p> <p>Xu Q, Chen L-L, Ruan X, et al (2013) The draft genome of sweet orange (Citrus sinensis). Nat Genet 45:59–66.</p> <p> </p> <p>Legend: </p> <p>Regulation - UP or DOWN = differentially expressed genes, UPregulated or DOWNregulated</p> <p>Citrus_40_Al_2 - Normalized CPM for root apexes under 1480 mM Al<sup>3+</sup> </p> <p>Citrus_0_Al_1 - Normalized CPM for root apexes under 0 mM Al<sup>3+</sup></p>
Soil ecotoxicology needs robust biomarkers – a meta-analysis approach to test the robustness of gene expression-based biomarkers for measuring chemical exposure effects in soil invertebrates
<p>Gene expression-based biomarkers are regularly proposed as rapid, sensitive and mechanistically informative tools to identify whether soil invertebrates are experiencing adverse effects due to chemical exposure. However, before biomarkers could be deployed within diagnostic studies, systematic evidence of the robustness of such biomarkers to detect effects is needed. Here, we present an approach for conducting a systematic meta-analysis of the robustness of gene expression-based biomarkers in soil invertebrates.</p> <p>The approach was developed and trialled for two measurements of gene expression commonly proposed as biomarkers in soil ecotoxicology: metallothionein (MT) gene expression in earthworms for metals and heat shock protein 70 (HSP70) gene expression in earthworms for organic chemicals. From a systematic analysis of the published literature, we collected 294 unique gene expression data points and used linear mixed-effect models to assess concentration, exposure duration and species effects on the quantified response.</p> <p>This database provided contains gene-expression data from publications that have used gene expression-based biomakers to study effects of chemical pollutants on soil invertebrates. R scripts are provided that were used to study the patterns of gene expression as reported in accompanying publication. </p> <p>We encourage colleagues in the field to apply this approach to other biomarkers, as such quantitative assessment is a prerequisite to ensuring that the suitability and limitations of proposed biomarkers are known and stated.</p>
Decoding diabetes biomarkers and related molecular mechanisms using machine learning, text mining, and gene expression analysis
<p>The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA- DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top ten DEGs. These genes were enriched in aerobic respiration, T-Cell antigen receptor pathway, Tricarboxylic acid metabolic process, vitamin D receptor pathway, Toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for ML analysis . The "Decision tree", "Extra-tree regressor" and "Random forest" algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. ML algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.</p>
Metabolomic analysis of cultured TRAMP-C2 cells in the presence or absence of PD-L1 expression
<p>The interaction between the immune inhibitory receptor PD-1 and its ligand PD-L1 is a critical mechanism for altering immune responses, especially during chronic antigen exposures such as cancer. While much research has focused on the PD-1 receptor, recent evidence suggests that PD-L1 can have cell-intrinsic effects in cancer and immune cells. These functions are distinct from its ability to bind and trigger PD-1 activity and are notable given that PD-L1 is widely expressed in mammals. One such cell-intrinsic function is the modulation of cellular metabolism, including regulation of mTOR activity and glycolysis. As part of our investigation into PD-L1 function, we analyzed the metabolome of cultured mouse prostate cancer cells (TRAMP-C2) expressing PD-L1 or with PD-L1 deleted via CRISPR/Cas9. We quantified 186 water-soluble metabolites from TRAMP-C2 cells expressing PD-L1 or not to better understand what metabolic pathways and processes are regulated by PD-L1 expression/activity. We found a broad range of differentially abundant metabolites, most notably a decreased abundance of glycolytic metabolites when PD-L1 expression is knocked out. In our manuscript, we show that this has a functional outcome on viral infection and cytokine signaling.</p>
Single cell multiomic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients
<p>Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.</p>
Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data -- dataset
<p>The data used in the figures in the paper "Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data" by Imamura et al. (J. Geophys. Res)</p> <p>The description of the columns in the files are given in the header section.</p>
Table with results of differential gene expression analysis in the controlled environment for fin tissues.
<p>Differences between transcriptomes of three Cottus fish lineages were assessed under controlled, laboratory conditions. Two tissues were investigated: fins and livers. Present table shows results of the differential gene expression analysis performed on fin tissues of Cottus fish. Base-mean, Log-2-fold change. standard error, statistics and associated p-values and FDR-corrected p-values are given for every contrast possible in our experimental design.</p>
Table with the results of the time series gene expression analysis for fins.
<p>Differences between transcriptomes of two Cottus fish lineages were assessed in the natural environment. Sampling was performed in a time series covering whole year. Two tissues were investigated: fins and livers. Present table shows results of a time series differential gene expression analysis performed on fin tissues.</p>
Table with results of differential gene expression analysis in the controlled environment for liver tissues.
<p>Differences between transcriptomes of three Cottus fish lineages were assessed under controlled, laboratory conditions. Two tissues were investigated: fins and livers. Present table shows results of the differential gene expression analysis performed on liver tissues of Cottus fish. Base-mean, Log-2-fold change. standard error, statistics and associated p-values and FDR-corrected p-values are given for every contrast possible in our experimental design.</p>
Supplementary materials for the manuscript entitled "Comprehensive Identification, Phylogenetic Analysis and Expression Profiling of Multicopper Oxidase Genes in Maize"
<p><strong>Table S1. </strong>Gene IDs and names of maize and Arabidopsis MCOs</p> <p><strong>Figure S1.</strong> Phylogenetic tree of protein sequences of multicopper oxidase (MCO) of maize and Arabidopsis with rectangular layout. Branches are not cladogram-transformed. Shown in nodes are bootstrap support. The tree is polar layout, and branches are cladogram-transformed. Maize IDs are labeled in brown. Clades representative of<em> SKS</em>, <em>LAC</em> and <em>AAO</em> are labeled in blue, red and green, respectively. The tree is reconstructed by PhyML with 1000 bootstrap replicates.</p> <p><strong>Figure S2. </strong>Chromosome map depicting location of maize <em>MCO</em>s, with gene IDs shown in the map. Figure legend is the same as Figure 2.</p> <p><strong>Figure S3. </strong>Phylogenetic tree of maize and Arabidopsis <em>AAO</em>s. The trees are rectangular layout. The tree is reconstructed by PhyML with 1000 bootstrap replicates. Shown in nodes are bootstrap support.</p>
Life in the fastlane? A comparative analysis of gene expression profiles across annual, semi-annual, and non-annual killifishes (Cyprinodontiformes: Nothobranchiidae)
<ol> <li>Read Counts per gene</li> <li>All read counts by inidividual and species</li> <li>Species abbreviations key</li> <li>R script for data analyses</li> </ol>
Figure 1 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 1. Distribution of Cq values of candidate reference genes in date mussel (L. lithophaga).
Figure 2 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 2. Average expression stability (M-value) of reference genes evaluated by geNorm.
RNA-Seq analysis to identify differentially expressed genes in top and bottom leaves under Alternaria brassicicola infection
<p>The broccoli plants were infected with Alternaria brassicicola and RNA samples were extracted for control and inoculated plants at 10 days post inoculation. </p>
Comprehensive Transcriptomic Analysis of Spodoptera Frugi-perda Reveals Stage-Specific Gene Expression and P450-Mediated Adaptation Mechanisms
<p><em><span>Spodoptera frugiperda</span></em><span> is a highly adaptable agricultural pest with a complex life cycle, posing significant challenges to pest control. This study conducted a transcriptomic analysis across nine developmental stages, identifying 6,834 differentially expressed genes (DEGs) and 3,072 stage-specific genes (SSGs) critical for their development and adaptability. Significant gene expression shifts were observed during the transition from late larval stages to adulthood. Notably, a high number of SSGs in 6th instar larvae and adult males were enriched in pathways related to oxidative phosphorylation and neural signaling, indicating high metabolic and reproductive demands. Widely shared DEGs associated with ecdysone signaling and detoxification processes underscore robust adaptation mechanisms.</span></p>
Data sets of the analysis of 1sg and 2sg subject expression with the verbs 'creer' and 'saber' in a corpus of spoken Spanish
<p>These are the two data sets used for the quantitative analysis in the paper "“Perspectival factors and <em>pro</em>-drop – A corpus study of speaker/addressee pronouns with <em>creer </em>‘think/believe’ and <em>saber </em>‘know’ in spoken Spanish” (Peter Herbeck;<em> to appear </em>in <em>Glossa – a journal of general linguistics</em>). It contains the values of the annotation of null and overt speaker/addressee pronouns in the Madrid and Alcalá samples of the corpus PRESEEA (2014-). The first data set was used for the quantitative analysis of subject expression (null/overt) with the verbs <em>creer </em>and <em>saber </em>according to person (1sg vs. 2sg) and polarity (negative vs. positive verb forms). The second data set was used for the analysis of 1sg subject expression according to the complement type of <em>creer </em>and <em>saber</em>.</p>
Clinical outcome prediction in COVID-19 patients by lymphocyte subsets analysis and monocytes' iTNF-α expression
<p>Raw Box & Whiskers plot related to a manuscript submitted to "Biology" journal - MDPI - https://www.mdpi.com/journal/biology - https://doi.org/10.3390/biology10080735</p> <p><strong>Manuscript Title</strong>: Clinical outcome prediction in COVID-19 patients by lymphocyte subsets analysis and monocytes’ iTNF-α expression</p> <p><strong>Authors: G</strong>abriele Madonna <sup>1†</sup>, Silvia Sale <sup>2†</sup>, Mariaelena Capone <sup>1</sup>, Chiara De Falco <sup>2</sup>, Valentina Santocchio <sup>2</sup>, Tiziana Di Matola <sup>2</sup>, Giuseppe Fiorentino <sup>3</sup>, Caterina Pirozzi <sup>2</sup>, Anna D’Antonio <sup>2</sup>, Rocco Sabatino <sup>2</sup>, Lidia Atripaldi<sup>4</sup>, Umberto Atripaldi <sup>4</sup>, Marcello Raffone <sup>5</sup>, Marcello Curvietto<sup> 1</sup>, Antonio Maria Grimaldi <sup>1</sup>, Vito Vanella <sup>1</sup>, Lucia Festino <sup>1</sup>, Luigi Scarpato <sup>1</sup>, Marco Palla <sup>1</sup>, Michela Spatarella <sup>6</sup>, Francesco Perna <sup>7</sup>, Pellegrino Cerino <sup>8</sup>, Gerardo Botti <sup>9</sup>, Roberto Parrella <sup>10</sup>, Vincenzo Montesarchio <sup>11</sup>, Paolo Antonio Ascierto <sup>1††* </sup>and Luigi Atripaldi <sup>2††</sup></p> <p><strong>Affiliations:</strong></p> <p>1 Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy</p> <p>2 UOC Biochimica Clinica, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>3 UOC Fisiopatologia e Riabilitazione respiratoria, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>4 University of Campania "Luigi Vanvitelli", Naples, Italy</p> <p>5 UOC Microbiologia e Virologia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>6 UOSD di Farmacia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>7 Università degli Studi di Napoli "Federico II", Naples, Italy.</p> <p>8 Istituto Zooprofilattico Sperimentale del Mezzogiorno, Portici (Na), Italy.</p> <p>9 Scientific Direction, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy</p> <p>10 UOC Malattie Infettive ad Indirizzo Respiratorio, AORN Ospedali dei Colli - Monaldi – Cotugno -CTO, Napoli, Italy</p> <p>11 UOC Oncologia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>†These authors have contributed equally to this work and share first authorship</p> <p>††These authors have contributed equally to this work and share senior authorship</p> <p>*Correspondence:</p> <p><strong>Abstract of submitted Manuscript:</strong> T</p> <p>In December 2019 a novel coronavirus, “SARS-CoV-2”, was recognized as the cause of Coronavirus disease-2019 (COVID-19-disease). Several studies have explored the changes and the role of inflammatory cells and cytokines in the immunopathogenesis of disease, but until today the results have been controversial. Based on these premises, we carried out a retrospective assessment of monocytes’ intracellular TNF-α expression (iTNF-α) and on frequencies of lymphocyte sub-populations in twenty-five patients with moderate/severe COVID-19-disease. We found lymphopenia in all COVID-19 infected subjects compared with healthy subjects. On initial observation, in patients with favorable outcome we detected high absolute eosinophils count and high CD4+/CD8+ T lymphocytes ratio, while in the exitus group we observed high neutrophils and CD8+ T lymphocytes counts. During infection, in patients with favorable outcome we observed a rise in lymphocyte count, in monocytes and in Treg lymphocytes counts, in CD4+ and in CD8+ T lymphocytes count but a reduction in CD4+/CD8+ T lymphocytes ratio. Instead, in the exitus group we observed a reduction in Treg lymphocytes counts and a decrease in iTNF-α expression. Our preliminary findings point to a modulation of the different cellular mediators of immune system, which probably have a key role in the outcome of the COVID-19-disease.</p> <p><strong>Funding:</strong> This work was supported by Grants from “Regione Campania” through “POR FESR CAMPANIA 2014-2020”, CUP H64I20000300002.</p> <p><strong>Cite</strong>: Madonna, G.; Sale, S.; Capone, M.; De Falco, C.; Santocchio, V.; Di Matola, T.; Fiorentino, G.; Pirozzi, C.; D’Antonio, A.; Sabatino, R.; Atripaldi, L.; Atripaldi, U.; Raffone, M.; Curvietto, M.; Grimaldi, A.M.; Vanella, V.; Festino, L.; Scarpato, L.; Palla, M.; Spatarella, M.; Perna, F.; Cerino, P.; Botti, G.; Parrella, R.; Montesarchio, V.; Ascierto, P.A.; Atripaldi, L. Clinical Outcome Prediction in COVID-19 Patients by Lymphocyte Subsets Analysis and Monocytes’ iTNF-α Expression. <em>Biology</em> <strong>2021</strong>, <em>10</em>, 735. doi: <a href="https://doi.org/10.3390/biology10080735">10.3390/biology10080735</a></p> <p> </p>
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.