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1,448 results for “proteomic”
scProAtlas: an atlas of multiplexed single-cell spatial proteomics imaging in human tissues
<p>All analysis results for the spatial proteomics imaging techniques in the scProAtlas database are stored in compressed files named accordingly. Within each compressed file, the folders are organized in a fixed storage structure in the following order: Analysis module > Imaging Technique > Dataset > Tissue > ROI.</p> <p>Each folder contains the corresponding metadata (including original sample information, cell type annotations, and neighborhood annotations) stored in a file named <code>cells.tsv</code>. Additionally, the module used to identify spatial pattern genes includes an <code>anndata</code> format file, named <code>adata_moran.h5ad</code>, which stores the integrated results of scRNA-seq and spatial proteomics.</p> <p>scProAtlas_analysis_code.tar.gz contains example codes for all analysis modules in scProAtlas. Here, we provide the example using <strong>SCP_CODEX1 - Large intestine. </strong>The codes include all the scripts used for the entire workflow, from image segmentation to scRNA-spatial proteomics integration, and spatial analysis.</p> <p>We have also uploaded the raw protein channel matrices with AnnData format in <strong>version 3 and 4.</strong></p>
MS/MS proteomics from: <em>Citrus sinensis</em> leaves in response to Diaphorina citri infestation and Huanglongbing disease
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Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease
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Proteomic and metabolomic analysis of COVID-19 nasal swabs
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Retinal proteome profiling of inherited retinal degeneration across three different mouse models suggests common drug targets in retinitis pigmentosa
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Data from: Genotype-by-environment interactions influence the composition of the Drosophila seminal proteome
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A simplified method for comprehensive capture of the Staphylococcus aureus proteome: S. aureus proteome data table
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RNA-Seq and proteomics of Crohn's disease: Terminal ileum of inflamed and non inflamed paired tissue biopsy
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Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology
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Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
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Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset - PXD107710 - ISA representation
<p>Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset deposited in PRIDE database with accession number: PXD107710</p> <p>ISA-Tab annotation for the "SARS-CoV-2 infected host cell proteomics reveal potential therapy targets" publication. </p> <p>Github repository: <a href="https://github.com/ISA-tools/PXD017710">https://github.com/ISA-tools/PXD017710</a></p> <p>This is part of an effort to (re-)annotate: <a href="https://dx.doi.org/10.21203/rs.3.rs-17218/v1">https://dx.doi.org/10.21203/rs.3.rs-17218/v1</a></p> <p>Additional work done as part of:</p> <ol> <li> <a href="https://github.com/virtual-biohackathons/covid-19-bh20">https://github.com/virtual-biohackathons/covid-19-bh20</a></li> <li> <a href="https://github.com/virtual-biohackathons/covid-19-bh20/wiki/FairData">https://github.com/virtual-biohackathons/covid-19-bh20/wiki/FairData</a></li> </ol> <p><strong>Proteomics data</strong></p> <p>Available from PRIDE at <a href="https://www.ebi.ac.uk/pride/archive/projects/PXD017710]">https://www.ebi.ac.uk/pride/archive/projects/PXD017710</a><br> and [<a href="https://massive.ucsd.edu/ProteoSAFe/result.jsp?task=334df9b4f1af4501bca0a2aa63278a7d&view=display_metadata_results&file=f.RMSV000000308%2F2020-03-22_nuno_334df9b4%2Fmetadata%2FMSV000085096_SARS-CoV-2_proteome_translatome.csv#%7B%22table_sort_history%22%3A%22_dyn_%23Condition_asc%22%7D">MassIVE/CCMS Maestro+MSstats reanalysis of MSV000085096 / PXD017710</a>]</p> <p><strong>ISA-Tab representation:</strong></p> <p>Rationale: Demonstrate suitability of the ISA format for representing MS based protein profiling experiment with more granularity and details, thus providing a better representation of the experiment design.<br> The formatting and re-annotation are based on information extracted from:<br> - the original publication<br> - the supplementary tables available from the publishers site<br> - the 'filtered-results.csv' helper file as supplied to @sneumann during the <a href="http://www.psidev.info/hupo-psi-meeting-2020">HUPO-PSI meeting March 2020</a></p> <p><br> Viewing the ISA-tab formatted and re-annotated PXD017710 with I<a href="https://isa-tools.org/PXD017710/isaviewer-demo.html)">SATab-Viewer</a></p> <p>Viewing the ISA-tab formatted and re-annotated PXD017710 locally, do the following:</p> <p>```bash<br> python -m http.server 8000<br> ```</p> <p>Then point your browser to `http://0.0.0.0:8000/isaviewer-demo.html`</p> <p><strong>Curation tasks performed:</strong></p> <p>* initial structure of the study design in ISA format:</p> <p>* linkage of Proteome and Translatome data (supplementary material) to ISA assay tables (via Derived Data File)</p> <p>* processing the Proteome and Translatome data (supplementary material) with python pandas library to generate the following csv files:</p> <p> - proteome_intensities_long_table_ggplot2.txt<br> - proteome_diffanal_ratio_pvalue_long_table_ggplot2.txt<br> - translatome_intensities_long_table_ggplot2.txt <br> - translatome_diffanal_ratio_pvalue_long_table_ggplot2<br> <br> The files are `long table` corresponding to a `melt` on the Excel file originally generated by the users and can be readily loaded in R ggplot2 library for graphical representation.<br> The statistical relevant elements have been annotated with the <a href="http://stato-ontology.org/">STATO ontology</a> and the tables comply with a Frictionless.io Data Package.<br> The jupyter notebook for the transformation is available.</p> <p>* conversion of raw data to mzML format: detailed in <a href="https://github.com/ISA-tools/PXD017710">https://github.com/ISA-tools/PXD017710</a></p> <p>install docker: <br> ```bash<br> >brew update<br> >brew install docker<br> ```</p> <p>sign in to docker<br> ```bash<br> >docker start<br> >docker login<br> ```</p> <p>pull docker container for ProteoWizard:<br> ```bash<br> >docker pull chambm/pwiz-i-agree-to-the-vendor-licenses<br> ```</p> <p>:warning: be sure to sign-up and login to https://hub.docker.com/</p> <p>in order to be able to reach</p> <p>https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses</p> <p><br> run the pwiz tool from the container over the raw data:<br> ```bash<br> docker run -it --rm -e WINEDEBUG=-all -v /Users/Downloads/PXD017710/raw/:/data chambm/pwiz-skyline-i-agree-to-the-vendor-licenses wine msconvert /data/*.raw --mzML<br> ```</p> <p><br> * ontology markup for:<br> * declaration of independent variables as ISA Study Factors:{biological agent, dose, time point, replicate} ->OBI<br> * Taxonomic information (host cells and virus) -> NCBITaxonomy<br> * Cell line: CaCo-2 cells -> Cell Line Ontology<br> * Disease: Colon Cancer -> Human Phenotype Ontology<br> * MS specific aspect (TMT reagent, instrument ... ) -> PSI-MS<br> * Statistical Tests -> STATO</p> <p><br> <strong>Unresolved curatorial issues:</strong></p> <p> 1. ambiguities related to Tandem Mass Tag labelling protocol<br> - the publication mentions TMT11 (see Figure 2 in https://www.researchsquare.com/article/rs-17218/v1)<br> - the information available from PRIDE mentions TMT6 (https://www.ebi.ac.uk/pride/archive/projects/PXD017710)<br> This may require another round of annotation on the TMT agents and fractions in the ISA a_assay representation</p> <p><br> 2. SARS-Cov2 isolate: no clear NCBI Taxonomic anchoring and unclear origin: -> the markup is made to the parent class (as of 06.04.2020)</p> <p><strong>Release and packaging as a BDBAG:</strong></p> <p>The tgz file associated with this upload has been producing using <a href="https://github.com/fair-research/bdbag">https://github.com/fair-research/bdbag</a>. It contains several manifest files detailing metadata and data files, providing md5 and sha256 checksums.</p> <p><strong>Github repository:</strong> <a href="https://github.com/ISA-tools/PXD017710">https://github.com/ISA-tools/PXD017710</a></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>
Phenotypic modulation of biofilm formation in a Staphylococcus epidermidis orthopedic clinical isolate grown under different mechanical stimuli: contribution from a combined proteomic study
<p>One of the major causes of prosthetic joint failure is infection. Recently, coagulase negative <em>Staphylococcus epidermidis</em> has been identified as an emergent, nosocomial pathogen involved in subclinical prosthetic join infections (PJIs). The diagnosis of PJIs mediated by <em>S. epidermidis</em> is usually complex and difficulties due to the absence of acute clinical signs derived from the host immune system response. Therefore, analysis of protein patterns in biofilm-producing <em>S. epidermidis</em> allows for the examination of the molecular basis of biofilm formation. Thus, in the present study, the proteome of a clinical isolate <em>S. epidermidis</em> was analyzed when cultured in its planktonic or sessile form to examine protein expression changes depending on culture conditions. After 24 hours of culture, sessile bacteria exhibited increased gene expression for ribosomal activity and for expression of proteins related to the initial attachment phase, involved in the capsular polysaccharide/adhesin, surface associated proteins and peptidoglycan biosynthesis. Likewise, planktonic <em>S. epidermidis</em> was able to aggregate after 24 hours, synthesizing the accumulation associate protein and cell-wall molecules through the activation of the YycFG and ArlRS, two component regulatory pathways. Prolonged culture under vigorous agitation generated a stressful growing environment triggering aggregation in a biofilm-like matrix as a mechanism to survive harsh conditions.</p> <p>Further studies will be essential to support these findings in order to further delineate the complex mechanisms of biofilm formation of <em>S. epidermidis</em> and they could provide the groundwork for the development of new drugs against biofilm-related infections, as well as the identification of novel biomarkers of subclinical or chronic infections mediated by these emerging, low virulence pathogens.</p>
Proteome Discoverer IR E. coli, D. radiodurans, and H. Sapiens result files
<p>File containing all of the Proteome Discoverer result files available with the publication "<strong>Ionizing radiation-induced proteomic oxidation in <em>Escherichia coli."</em></strong></p>
Data from: Quantitative proteomics reveals rapid divergence in the postmating response of female reproductive tracts among sibling species
<p><span><span><span><span><span><span><span><span><span><span><span>Fertility depends, in part, on interactions between male and female reproductive proteins inside the female reproductive tract (FRT) that mediate postmating changes in female behavior, morphology, and physiology. Coevolution between interacting proteins within species may drive reproductive incompatibilities between species, yet the mechanisms underlying postmating-prezygotic isolating barriers remain poorly resolved. Here, we used quantitative proteomics in sibling <i>Drosophila</i> species to investigate the molecular composition of the FRT environment and its role in mediating species-specific postmating responses. We found that (1) FRT proteomes in <i>D. simulans</i> and<i> D. mauritiana</i> virgin females express unique combinations of secreted proteins and are enriched for distinct functional categories, (2) mating induces substantial changes to the FRT proteome in <i>D. mauritiana</i> but not in <i>D. simulans</i>, and (3) the <i>D. simulans </i>FRT proteome exhibits limited postmating changes irrespective of whether females mate with conspecific or heterospecific males, suggesting an active female role in mediating reproductive interactions. Comparisons with similar data in the closely related outgroup species <i>D. melanogaster </i>suggest that divergence is concentrated on the <i>D. simulans </i>lineage. Our study suggests that divergence in the FRT extracellular environment and postmating response contribute to previously described patterns of postmating-prezygotic isolation and the maintenance of species boundaries.</span></span></span></span></span></span></span></span></span></span></span></p>
Light and temperature measurements and untargeted proteomic measurements
<p class="BodyText1">The right timing of animal physiology and behavior ensures the stability of populations and ecosystems. In order to predict anthropogenic impacts on these timings, more insight is needed into the interplay between environment and molecular timing mechanisms. This is particularly true in marine environments.</p> <p class="BodyText1">Using high-resolution, long-term daylight measurements from a habitat of the marine annelid <i>Platynereis dumerilii</i>, we find that temporal changes in UVA/deep violet intensities, more than longer wavelengths, can provide annual time information, which differs from annual changes in photoperiod. We developed experimental setups that resemble natural daylight illumination conditions, and automated, quantifiable behavioral tracking. Experimental reduction of UVA/deep violet light (app. 370-430nm) under long photoperiod (LD16:8) significantly decreases locomotor activities, comparable to the decrease caused by short photoperiod (8:16). In contrast, altering UVA/deep violet light intensities does not cause differences in locomotor levels under short photoperiod. This modulation of locomotion by UVA/deep violet light under long photoperiod requires c-opsin1, an UVA/deep violet-sensor employing G<sub>i</sub>-signalling. C-opsin1 also regulates the levels of rate-limiting enzymes for monogenic amine synthesis and of several neurohormones, including PDF, Vasotocin (Vasopressin/Oxytocin) and NPY-1.</p> <p class="BodyText1">Our analyses indicate a complex inteplay between UVA intensities and photoperiod as indicators of annual time.</p>
Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal disease
<p>End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We performed dense serial blood sampling in hospitalised and non-hospitalised ESKD patients with COVID-19 (n=256 samples from 55 patients) and used Olink immunoassays to measure 436 circulating proteins. Comparison to 51 non-infected ESKD patients revealed 221 proteins differentially expressed in COVID-19, of which 69.7% replicated in an independent cohort of 46 COVID-19 patients. 203 proteins were associated with clinical severity scores, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g proteinase-3) and epithelial injury (e.g. KRT19). Random Forests machine learning identified predictors of current or future severity such as KRT19, PARP1, PADI2, CCL7, and IL1RL1 (ST2). Survival analysis with joint models revealed 69 predictors of death including IL22RA1, CCL28, and the neutrophil-derived chemotaxin AZU1 (Azurocidin). Finally, longitudinal modelling with linear mixed models uncovered 32 proteins that display different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. Our findings point to aberrant innate immune activation and leucocyte-endothelial interactions as central to the pathology of severe COVID-19. The data from this unique cohort of high-risk individuals provide a valuable resource for identifying drug targets in COVID-19.</p>
Raoultella sp. KDF8 proteome data
<p>A comparative proteomic study was performed to identify proteins and pathways involved in diclofenac and codeine degradation steps in <em>Raoultella</em> sp. KDF8. Cultures of the <em>Raoultella</em> sp. KDF8 were grown in shaken flasks in mineral medium (BSBTE medium) supplemented with glycerol or one of pharmaceuticals. Growing cells were subjected to analysis.</p>
SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations
<h2>SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations</h2> <p>SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.</p> <p>This Zenodo repository includes a <code>.tar.gz</code> archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a>.</p> <h3>License:</h3> <p>Creative Commons Zero v1.0 Universal (CC0)<br>This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.</p> <h3>Archive Contents:</h3> <p>This compressed file includes:</p> <ul> <li>Datasets<br>- Tissue Datasets: <code>ALMANAC-Breast</code>, <code>ALMANAC-Lung</code>, <code>ALMANAC-Ovary</code>, <code>ALMANAC-Skin</code><br>- Study Datasets: <code>FRIEDMAN</code>, <code>ONEIL</code></li> <li>Supporting Files<br>- Raw and preprocessed data<br>- Feature dictionaries<br>- Hyperparameter configurations<br>- Trained model weights</li> </ul> <h3>Folder Structure:</h3> <blockquote> <p><code>SynProtX/</code><br><code>├── data/ # Raw and preprocessed data</code><br><code>│ ├── export/ # Processed protein/gene expression & drug combinations</code><br><code>│ ├── nps/ # Numpy arrays for all datasets</code><br><code>│ ├── nps_intersected/ # Dataset-specific numpy arrays</code><br><code>│ └── raw/ # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger</code><br><code>├── feature_dicts/ # Feature dictionaries for drug combinations</code><br><code>├── hyperparams/ # Hyperparameter configs for SynProtX-GATFP</code><br><code>│ ├── classification/ # For classification tasks</code><br><code>│ └── regression/ # For regression tasks</code><br><code>├── state_dict/ # Trained model weights</code><br><code>│ ├── classification/ # PyTorch checkpoints for classification</code><br><code>│ └── regression/ # PyTorch checkpoints for regression</code><br><code>└── README_Zenodo.md # This file</code></p> </blockquote> <h3>For more information, please visit:</h3> <p><strong>GitHub:</strong> <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a></p>
Low-Complexity Domains (LCDs) in UniProt Reference Proteomes
<p>This is a comprehensive dataset of low-complexity domains in UniProt reference proteomes. For the purposes of this dataset, LCDs were identified using the LCD-Composer algorithm with default parameters for each of the 20 canonical amino acids. These searches identify "primary" LCDs, defined as protein regions for which a single type of amino acid comprises at least 40% of the region. In addition, separate searches were performed to identify "secondary" LCDs, which are defined as regions for which a single type of amino acid comprises at least 40% of the region <em><strong>and</strong></em> a second type of amino acid comprises at least 20% of the same region. Note that secondary LCDs exhibit very strong spatial overlap with primary LCDs and may be considered, approximately speaking, a subset of primary LCDs. There are seven main components to this dataset:</p> <ol> <li>Primary and secondary LCDs for the original reference proteomes from UniProt (downloaded 8/22/2022). <ul> <li>These data are found within four zipped archives ending in "_LCDs.zip", one for each domain of life (Archaea, Bacteria, Eukaryota, and Viruses).</li> <li>Within each zipped archive, results are contained in a pair of files for each organism. The start of the file name is the organism's UniProt ID. For each organism, the pair of files are: primary LCDs are contained within the file ending in "_LCDcomposer_RESULTS.tsv", whereas secondary LCDs are contained within the file ending in "_LCDcomposer_SecondaryLCDs_RESULTS.tsv".</li> <li>Reference proteomes analyzed for each organism are also provided in separate zipped archives, one for each domain of life.</li> </ul> </li> <li>Primary and secondary LCDs for a scrambled version of each proteome mentioned above. <ul> <li>These searches were performed using identical search parameters and are included for statistical comparisons.</li> <li>When scrambling the proteomes, each protein sequence was scrambled individually to maintain its amino acid composition.</li> <li>File formats are identical to those described above except that all files will have "SCRAMBLED" in the name to distinguish them from analyses of original (i.e. native) proteomes.</li> </ul> </li> <li>The "SecondaryLCDs_by_LCDcategory.zip" archive contains all secondary LCDs from the original proteomes but parsed by LCD category rather than by organism. <ul> <li>These LCDs are identical to those in #1 above but are provided in this format to aid those interested in specific types of LCDs and which organisms contain them.</li> </ul> </li> <li>The "GOA_files.zip" archive contains gene ontology files necessary for reproducing analyses in Cascarina and Ross (2024).</li> <li>The "Pfam_Data.zip" archive contains files with Pfam annotations in LCD-containing proteins and Pfam clan information. These files are necessary for reproducing analyses in Cascarina and Ross (2024).</li> <li>The "Observed_vs_Scrambled_LCDfrequency_Statistics.zip" archive contains results of statistical analyses of LCD enrichment or depletion in native ("Observed") proteomes compared to scrambled proteomes. Enrichment is defined as the native proteome having more LCD-containing proteins for a particular LCD type compared to a scrambled version of that proteome. Depletion is defined as the native having containing fewer LCD-containing proteins for a particular LCD type compared to a scrambled version of that proteome. In cases were 0 instances of an LCD class occurred in the native proteome, scrambled proteome, or both, biased estimates for the natural log of the odds ratio ("lnOR") and p-value were calculated by first adding 1 to all cells in the contingency table.</li> <li>The "RandomlySelectedOrganisms.zip" archive contains LCD-Composer results for 50 randomly selected organisms from each domain of life with the window size and composition thresholds used during the LCD searches varied systematically.</li> </ol>
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.