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485 results for “pathway analysis”
Data for Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study - v0.1
<p>Data repository for the manuscript titled: <em>Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study.</em></p> <p>This repository includes all of the input data, processed data, and output data used for the project. This is a working version of our Tradeoff Analysis ready for interested users, peer reviewers, and others to run. </p> <p>README file on GitHub: https://github.com/Laura-Geronimo/Geronimo-etal_2024_NaturalHazardsReview/blob/main/README.md</p> <p> </p>
Interstage single ventricle heart disease infants show dysregulation in multiple metabolic pathways: targeted metabolomics analysis - Data
<p>The data in this Zenodo entry corresponds to the data used to produce the results in <a href="https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169">https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169</a>. The zipped folder contains three files</p> <ul> <li>Metabolite Data.csv - The meatobilte measurements for all the samples</li> <li>Clinical Data.csv - Values for the clinical variables</li> <li>Clinical Data Descriptions.csv - More in depth explanation of clinical variables as well as possible values of the variables</li> </ul> <p><span>This study was supported by the American Heart Association (AHA</span><span>20CDA35310498 and AHA18IPA34170070) and the National Institutes </span><span>of Health (NIH/NCATS Colorado CTSA, No. UL1 TR001082 and NIH/</span><span>NHLBI K23HL12363</span></p>
Meta-analysis on necessary investment shifts to reach net zero pathways in Europe
<p>This is the code and the data necessary to reproduce the six main figures and the t-test presented in the supplementary information of the publication "Meta-analysis on necessary investment shifts to reach net zero pathways in Europe". DOI: 10.1038/s41558-022-01549-5</p>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Data to initialize a TAD_Pathways Analysis
<p>Dataset is required for a TAD_Pathways analysis (see https://github.com/greenelab/tad_pathways_pipeline).</p> <p>Archived folder includes a TAD based gene index file, curated SNPs from the NHGRI-EBI GWAS catalog, and TAD based genes and SNPs for each GWAS.</p>
Datasets used for analysis and plotting in the study by Zhou et al. "Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapour"
<p>These data are model simulated water vapour (H2O) and ozone (O3) model mixing ratios between 2022 and 2023 that were used to create figures for the study by Zhou et al. "Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapour".</p><p>We use the TOMCAT/SLIMCAT 3-D off-line chemical transport model (Chipperfield, 2006) to represent the Hunga Tonga-Hunga Ha'apai (HTHH) H2O plume and quantify its longevity and ozone impacts. The model was run at a horizontal resolution of 2.8 degrees and 32 levels from the surface to about 60 km forced with ECMWF ERA5 meteorology. </p><p>A control simulation (file name with "MPC741") without treatment of HTHH was integrated from 1980 to October 2023. Output from run control for January 1st, 2022 was used to intialise a HTHH H2O perturbed run (run HT, file name with "MPC744") until October 2023 with the injection of 150 Tg of H2O into the low-mid stratosphere at southern subtropical latitudes. To test the possible future evolution of the HTHH H2O three further model runs were performed. These were integrated from January 1st, 2023 until 2030 using repeating ERA-5 meteorology for 2022. Run Con_2022 (file name with "MPC741__2022pd") was an extension of run control; run HT2022 was an extension of run HT (file name with "MPC744_2022pd")<i>, </i>and run HT2022ns (file name with "MPC744_2022pdns") was the same as run HT_2022 but had sedimentation of PSC particles turned off. Please see our paper for more information about the simulations.</p>
Figures 1–6 in Occurrence of an exotic whitefly, Siphoninus phillyreae (Haliday) (Hemiptera: Aleyrodidae) in South Korea and its potential pathway analysis
Figures 1–6. Ash whitefly (Siphoninus phillyreae (Haliday)) and regional map of South Korea. 1–2) Several life stages of the ash whitefly on the underside of leaves of a pomegranate tree (GN: Jinju, 3-viii-2021 (S.J. Suh)). 3) Puparium (slide-mounted). 4) Siphons on marginal and submarginal areas of dorsum. 5) Vasiform orifice. 6) Distribution map of ash whitefly in South Korea.
Proteomic data sets after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway analysis (IPA Qiagen)
<p>List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)</p>
Figure 2. (a) Representation of laser servo-driver for inverse kinematics analysis; (b) Representation of the lag angle, B, of the internal servomechanism (magnified version of the chin-rest).-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>As the servo-driver will be attached in the chin-rest in a non-central area with respect to the<br> semispherical structure shown in Figure 1(a), it is necessary to calculate a lag angle, according to<br> the measurements from the chin-rest, see Figure 2(b). This was done using a hybrid formula based<br> on the law of cosines,</p>
Integrated analysis of miRNA landscape and cellular networking pathways in stage-specific prostate cancer
<p><strong>Figure S1.</strong> Heat map of miRNA-microarray. Expression of miRNAs differentially expressed and assessed in microarray analysis of RNA isolated from four different cell lines of prostate cancer (LNCaP, PC3, DU145, 22Rv1) compared with control cell line of prostate cancer (PrEc). The red color depicts high and green color showed a lower level of expression at p value <0.01.</p> <p><strong>Figure S2A.</strong> Drug resistance by drug efflux. The internetworking relationship with miRNAs and plasma membrane protein P-glycoprotein (Pgp-plasma membrane protein). The miRNA-130a and miR-181a are downregulated in this pathway (green color) and linked with Pg and BCRP (breast cancer resistant protein). miR-133a and miR379 are involved in regulating the expression of MRP2. While miR-298, miR27a, miR331-5p and miR-130a are involved in regulating the expression of poly-glycoprotein (P-gp).</p> <p><strong>Figure S2B.</strong> Epithelial mesenchymal transition pathway. The miR-200b was downregulated during early-stage prostate cancer and was involved in inhibiting Jagged-2 (JAG2), one of the NOTCH ligands.</p> <p><strong>Figure S2C.</strong> Adipogenesis pathway. The expression of miR-326 was upregulated during the metastatic stage of prostate cancer, (red color) and involved in regulating the expression of CCAT/enhancer binding protein β (C/EBPβ), directly linked with the nuclear hormone receptor peroxisome proliferator-activated receptor-gamma (PPAR-γ).</p> <p><strong>Figure S2D.</strong> Bone metamorphosis signaling pathway. The expression of miR-140, miR-145, and miR-155 was upregulated, along with miR-140 and miR-145 were associated with modulation of gene SOX9, and miR-155 was involved in regulating the gene FOXO3A.</p> <p><strong>Figure S2E.</strong> Th1 pathway. In this pathway, the expression of miR-146a showed a higher level of expression (red color) and modulated the expression of NF-κB signaling.</p> <p><strong>Figure S2F.</strong> Th1 and Th2 pathway. In this pathway, the expression of miR-146a showed a lower level of expression (green color) and may modulate the expression of NF-κB signaling.</p> <p><strong>Table S1.</strong> List of differentially expressed miRNAs derived from LNCaP cells lines statistically significant as P < 0.001.</p> <p><strong>Table S2.</strong> List of differentially expressed miRNAs derived from PC3 cells lines statistically significant as P < 0.001.</p> <p><strong>Table S3.</strong> List of differentially expressed miRNAs derived from DU145 cells lines statistically significant as P < 0.001.</p> <p><strong>Table S4.</strong> List of differentially expressed miRNAs derived from 22Rv1 cells lines statistically significant as P < 0.001.</p>
Hepatic transcriptomic analysis reveals differential regulation of metabolic and immune pathways in three strains of chickens with distinct growth rate exposed to mixed parasites infections
<p><span>This dataset was generated from the study investigating hepatic gene expression in three strains of chickens: Ross-308 (R), Lohmann Brown Plus (LB), and Lohmann Dual (LD), 2 weeks after either an experimental infection (n = 18) with both <em>A. galli</em> and <em>H. gallinarum or kept as uninfected control (n = 12)</em>. </span></p>
Dataset: 1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis
<p>This dataset contains the set of 1H NMR data used in https://doi.org/10.1038/srep30982.</p> <p>Yeast was grown in five different liquid media and their metabolism was characterized at 6 different time-points during 24 h.</p> <p>The media used were YSC (Yeast nitrogen base Synthetic Complete) and four Drop-Out (DM) medium that do not contain one of the following nutrients (L-histidine, L-leucine, L-methionine and uracil). Since the used yeast strain does not encode in its genome some genes relative to the biosynthesis of these four nutrients, some gene de-regulations process will occur, detectable at the metabolome level.</p> <p>In this study, we have characterized the metabolome using <sup>1</sup>H NMR spectroscopy, detecting more than 40 metabolites, and the evolution of this metabolome along the measured time-points was described by application of PCA, ASCA and MCR-ALS chemometric methods.</p>
Figure 4 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 4. Probability for the estimated annual passenger entries with CBB-infested materials: (a) to Hawaii from CBB-occurring countries; (b) between Hawaii island and Oahu; (c) between Oahu and Maui; (d) between Oahu and Kauai; (e) between Hawaii island and Maui; (f) between Maui and Kauai; (g) between Hawaii island and Kauai; (h) between Oahu and Lanai.
Figure 1 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 1. World coffee production with CBB world distribution: a) 2019 coffee produc- tion (FAO 2020); b) CBB distribution by introduction year (Vega et al. 2015).
Figure 2 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 2. Number of months per year with optimal temperature conditions for CBB growth. The optimal temperature conditions were determined by evaluating the area where daily minimum temperature was above 18°C and daily maximum temperature was below 30°C.
Figure 3 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 3. Mean annual number of air passengers traveling between the Hawaiian Islands. Dispersal pathways are shown for those islands that are confirmed to have coffee berry borer. Thicker blue lines indicate higher numbers of passengers.
Transcriptomic analysis of light-induced genes in Nasonia vitripennis: possible implications for circadian light entrainment pathways
<p class="MDPI17abstract"><span>Circadian entrainment to the environmental day-night cycle is essential for the optimal use of environmental resources. In insects, opsin-based photoreception in the compound eye and ocelli, and CRYPTOCHROME1 (CRY1) in circadian clock neurons are thought to be involved in sensing photic information, but genetic regulation of circadian light entrainment in species without light-sensitive CRY1 remains unclear. To elucidate a possible CRY1-independent light transduction cascade, we analysed light-induced gene expression through RNA-sequencing in <em>Nasonia vitripennis</em>. Entrained wasps were subjected to a light pulse in the subjective night to reset the circadian clock and light-induced changes in gene expression were characterized at four different time points in wasp heads. We used co-expression, functional annotation, and transcription factor binding motif analyses to gain insight into the molecular pathways in response to acute light stimulus and form a hypothesis about the circadian light resetting pathway. Maximal gene induction was found after 2h of light stimulation (1432 genes), including the opsin <em>opblue</em> and the core clock genes <em>cry2</em> and <em>npas2</em>. Pathway and cluster analyses revealed light activation of glutamatergic and GABA-ergic neurotransmission, including CREB and AP-1 transcription pathway signalling. This suggests that circadian photic entrainment in <em>Nasonia</em> may require pathways that are similar to mammals. We propose a model for hymenopteran circadian light resetting that involves opsin-based photoreception, glutamatergic neurotransmission, and gene induction of <em>cry2</em> and <em>npas2</em> to reset the circadian clock.</span></p>
Transcriptomic analysis of light-induced genes in Nasonia vitripennis: possible implications for circadian light entrainment pathways
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Data for BY-COVID Pathways to MINERVA Analysis Workflow
<p>Source data (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE182152">GSE182152</a>) was analysed with <a href="https://workflowhub.eu/workflows/688">WFHub:688</a> to generate these datasets</p>
A seasonal analysis of aerosol NO3- sources and NOx oxidation pathways in the Southern Ocean marine boundary layer
<p>This dataset includes coarse mode atmopsheric nitrate concentration and isotopic composition from the Southern Ocean marine boundary layer in summer (2018/19), winter (2019) and spring (2019). Environmental data (temperature, SLP and relative humidity) are also included.</p>
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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)
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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.